WO2021236542A1

WO2021236542A1 - System and method to enable remote adjustment of a device during a telemedicine session

Info

Publication number: WO2021236542A1
Application number: PCT/US2021/032807
Authority: WO
Inventors: Steven Mason; Daniel Posnack; Peter ARN; Wendy Para; S. Adam Hacking; Micheal Mueller; Joseph GUANERI; Jonathan Greene
Original assignee: Rom Technologies, Inc.
Priority date: 2020-05-18
Filing date: 2021-05-17
Publication date: 2021-11-25

Abstract

A computer-implemented system comprising a treatment device, a patient interface, and a processing device is disclosed. The treatment device is configured to be manipulated by a user while the user performs a treatment plan. The patient interface comprises an output device configured to present telemedicine information associated with a telemedicine session. The processing device is configured to receive a treatment plan for a patient; during the telemedicine session, use the treatment plan to generate at least one parameter; and responsive to at least one trigger condition occurring, control at least one operation of the device.

Description

SYSTEM AND METHOD TO ENABLE REMOTE ADJUSTMENT OF A DEVICE DURING A TELEMEDICINE SESSION

CROSS-REFERENCES TO RELATED APPLICATIONS

[0001] This application claims priority to and the benefit of U.S. Patent Application Serial No. 17/147,514, filed on January 13, 2021, which is a continuation-in-part of U.S. Patent Application Serial No. 17/021,895, filed on September 15, 2020, which claims priority to and the benefit of U.S. Provisional Patent Application Serial No. 62/910,232, filed on October 3, 2019; and which claims priority to and the benefit of U.S. Provisional Patent Application Serial No. 63/029,896, filed on May 26, 2020, the entire disclosures of which are incorporated herein by reference.

[0002] This application also claims priority to and the benefit of U.S. Provisional Patent Application Serial

No. 63/106,749, filed on October 28, 2020, entire disclosure of which is incorporated herein by reference. [0003] This application claims priority to and the benefit of U.S. Patent Application Serial No. 17/147,532, filed on January 13, 2021, which is a continuation-in-part of U.S. Patent Application Serial No. 17/021,895, filed on September 15, 2020, which claims priority to and the benefit of U.S. Provisional Patent Application Serial No. 62/910,232, filed on October 3, 2019; and which claims priority to and the benefit of U.S. Provisional Patent Application Serial No. 63/018,834, filed on May 1, 2020, the entire disclosures of which are incorporated herein by reference.

[0004] This application also claims priority to and the benefit of U.S. Patent Application Serial No. 16/876,472, filed on May 18, 2020, entire disclosure of which is incorporated herein by reference.

[0005] This application claims priority to and the benefit of U.S. Patent Application Serial No. 17/146,705, filed on January 12, 2021, which is a continuation-in-part of U.S. Patent Application Serial No. 17/021,895, filed on September 15, 2020, which claims priority to and the benefit of U.S. Provisional Patent Application Serial No. 62/910,232, filed on October 3, 2019; and which claims priority to and the benefit of U.S. Provisional Patent Application Serial No. 63/113,484, filed on November 13, 2020, the entire disclosures of which are incorporated herein by reference.

[0006] This application claims priority to and the benefit of U.S. Patent Application Serial No. 17/150,938, filed on January 15, 2021, which is a continuation-in-part of U.S. Patent Application Serial No. 17/021,895, filed on September 15, 2020, which claims priority to and the benefit of U.S. Provisional Patent Application Serial No. 62/910,232, filed on October 3, 2019; and which claims priority to and the benefit of U.S. Provisional Patent Application Serial No. 63/066,488, filed on August 17, 2020, the entire disclosures of which are incorporated herein by reference.

[0007] This application claims priority to and the benefit of U.S. Patent Application Serial No. 17/147,453, filed on January 12, 2021, which is a continuation-in-part of U.S. Patent Application Serial No. 17/021,895, filed on September 15, 2020, which claims priority to and the benefit of U.S. Provisional Patent Application Serial No. 62/910,232, filed on October 3, 2019; and which claims priority to and the benefit of U.S. Provisional Patent Application Serial No. 63/085,825, filed on September 30, 2020, the entire disclosures of which are incorporated herein by reference. TECHNICAL FIELD

[0008] This disclosure relates generally to a system and a method for enabling a remote adjustment of a device during a telemedicine session.

BACKGROUND

[0009] Remote medical assistance, also referred to, inter alia, as remote medicine, telemedicine, telemed, telmed, tel-med, or telehealth, is an at least two-way communication between a healthcare provider or providers, such as a physician or a physical therapist, and a patient using audio and/or audiovisual and/or other sensorial or perceptive (e.g., tactile, gustatory, haptic, pressure-sensing-based or electromagnetic (e.g., nemo stimulation) communications (e.g., via a computer, a smartphone, or a tablet). Telemedicine may aid a patient in performing various aspects of a rehabilitation regimen for a body part. The patient may use a patient interface in communication with an assistant interface for receiving the remote medical assistance via audio, visual, audiovisual, or other communications described elsewhere herein. Any reference herein to any particular sensorial modality shall be understood to include and to disclose by implication a different one or more sensory modalities.

[0010] Telemedicine is an option for healthcare providers to communicate with patients and provide patient care when the patients do not want to or cannot easily go to the healthcare providers’ offices. Telemedicine, however, has substantive limitations as the healthcare providers cannot conduct physical examinations of the patients. Rather, the healthcare providers must rely on verbal communication and/or limited remote observation of the patients.

SUMMARY

[0011] In general, the present disclosure provides a system and method for remote examination of patients through augmentation.

[0012] An aspect of the disclosed embodiments includes a computer-implemented system comprising a treatment device, a patient interface, and a processing device. The treatment device is configured to be manipulated by a user while the user performs a treatment plan. The patient interface comprises an output device configured to present telemedicine information associated with a telemedicine session. The processing device is configured to receive a treatment plan for a patient; during the telemedicine session, use the treatment plan to generate at least one parameter; and responsive to at least one trigger condition occurring, control at least one operation of the device.

[0013] Another aspect of the disclosed embodiments includes a tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to perform any of the methods, operations, or steps described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

[0014] For a more complete understanding of this disclosure and its advantages, reference is now made to the following description, taken in conjunction with the accompanying drawings. It is emphasized that, according to common practice, the various features of the drawings are not to-scale. On the contrary, the dimensions of the various features are arbitrarily expanded or reduced for clarity.

[0015] FIG. 1 generally illustrates a high-level component diagram of an illustrative system according to certain aspects of this disclosure.

[0016] FIGS. 2A-D generally illustrate example treatment devices according to certain aspects of this disclosure.

[0017] FIG. 3 generally illustrates an example master device according to certain aspects of this disclosure.

[0018] FIGS. 4A-D generally illustrate example augmented images according to certain aspects of this disclosure.

[0019] FIG. 5 generally illustrates an example method of operating a remote examination system according to certain aspects of this disclosure.

[0020] FIG. 6 generally illustrates an example method of operating a remote examination system according to certain aspects of this disclosure.

[0021] FIG. 7 generally illustrates a high-level component diagram of an illustrative system for a remote adjustment of a device according to certain aspects of this disclosure.

[0022] FIG. 8 generally illustrates a perspective view of an example of the device according to certain aspects of this disclosure.

[0023] FIG. 9 generally illustrates an example method of enabling a remote adjustment of a device according to certain aspects of this disclosure.

[0024] FIG. 10 generally illustrates an example computer system according to certain to certain aspects of this disclosure.

[0025] FIG. 11 generally illustrates a perspective view of an embodiment of the device, such as a treatment device according to certain aspects of this disclosure.

[0026] FIG. 12 generally illustrates a perspective view of a pedal of the treatment device of FIG. 11 according to certain aspects of this disclosure.

[0027] FIG. 13 generally illustrates a perspective view of a person using the treatment device of FIG. 11 according to certain aspects of this disclosure.

[0028] FIG. 14 shows a block diagram of an embodiment of a computer implemented system for managing a treatment plan according to the present disclosure;

[0029] FIG. 15 shows a perspective view of an embodiment of a treatment apparatus according to the present disclosure;

[0030] FIG. 16 shows a perspective view of a pedal of the treatment apparatus of FIG. 15 according to the present disclosure;

[0031] FIG. 17 shows a perspective view of a person using the treatment apparatus of FIG. 15 according to the present disclosure;

[0032] FIG. 18 shows an example embodiment of an overview display of an assistant interface according to the present disclosure; [0033] FIG. 19 shows an example block diagram of training a machine learning model to output, based on data pertaining to the patient, a treatment plan for the patient according to the present disclosure;

[0034] FIG. 20 shows an embodiment of an overview display of the assistant interface presenting recommended treatment plans and excluded treatment plans in real-time during a telemedicine session according to the present disclosure;

[0035] FIG. 21 shows an embodiment of the overview display of the assistant interface presenting, in real-time during a telemedicine session, recommended treatment plans that have changed as a result of patient data changing according to the present disclosure;

[0036] FIG. 22 shows an example embodiment of a method for selecting, based on assigning a patient to a cohort, a treatment plan for the patient and controlling, based on the treatment plan, a treatment apparatus according to the present disclosure;

[0037] FIG. 23 shows an example embodiment of a method for presenting, during a telemedicine session, the recommended treatment plan to a medical professional according to the present disclosure; and [0038] FIG. 24 shows an example computer system according to the present disclosure.

[0039] FIG. 25 generally illustrates a high-level component diagram of an illustrative system according to certain aspects of this disclosure.

[0040] FIGS. 26A-D generally illustrate example treatment devices according to certain aspects of this disclosure.

[0041] FIG. 27 generally illustrates an example master device according to certain aspects of this disclosure.

[0042] FIGS. 28A-D generally illustrate example augmented images according to certain aspects of this disclosure.

[0043] FIG. 29 generally illustrates an example method of operating a remote examination system according to certain aspects of this disclosure.

[0044] FIG. 30 generally illustrates an example method of operating a remote examination system according to certain aspects of this disclosure.

[0045] FIG. 31 generally illustrates an example computer system according to certain to certain aspects of this disclosure.

[0046] FIG. 32 generally illustrates a perspective view of an example of the device according to certain aspects of this disclosure.

[0047] FIG. 33 generally illustrates a perspective view of an embodiment of the device, such as a treatment device according to certain aspects of this disclosure.

[0048] FIG. 34 generally illustrates a perspective view of a pedal of the treatment device of FIG. 33 according to certain aspects of this disclosure.

[0049] FIG. 35 generally illustrates a perspective view of a person using the treatment device of FIG. 33 according to certain aspects of this disclosure.

[0050] FIG. 36 generally illustrates a block diagram of an embodiment of a computer implemented system for managing a treatment plan according to the principles of the present disclosure; [0051] FIG. 37 generally illustrates a perspective view of an embodiment of a treatment apparatus according to the principles of the present disclosure;

[0052] FIG. 38 generally illustrates a perspective view of a pedal of the treatment apparatus of FIG. 37 according to the principles of the present disclosure;

[0053] FIG. 39 generally illustrates a perspective view of a person using the treatment apparatus of FIG. 37 according to the principles of the present disclosure;

[0054] FIG. 40 generally illustrates an example embodiment of an overview display of an assistant interface according to the principles of the present disclosure;

[0055] FIG. 41 generally illustrates an example block diagram of training a machine learning model to output, based on data pertaining to the patient, a treatment plan for the patient according to the principles of the present disclosure;

[0056] FIG. 42 generally illustrates an embodiment of an overview display of the assistant interface presenting recommended treatment plans and excluded treatment plans in real-time during a telemedicine session according to the principles of the present disclosure;

[0057] FIG. 43 generally illustrates an example embodiment of a method for optimizing a treatment plan for a user to increase a probability of the user complying with the treatment plan according to the principles of the present disclosure;

[0058] FIG. 44 generally illustrates an example embodiment of a method for generating a treatment plan based on a desired benefit, a desired pain level, an indication of probability of complying with a particular exercise regimen, or some combination thereof according to the principles of the present disclosure;

[0059] FIG. 45 generally illustrates an example embodiment of a method for controlling, based on a treatment plan, a treatment apparatus while a user uses the treatment apparatus according to the principles of the present disclosure; and

[0060] FIG. 46 generally illustrates an example computer system according to the principles of the present disclosure.

[0061] FIG. 47 generally illustrates a block diagram of an embodiment of a computer-implemented system for managing a prehabilitation plan according to principles of the present disclosure.

[0062] FIG. 48A generally illustrates a perspective view of an example of an exercise and prehabilitation device according to principles of the present disclosure.

[0063] FIG. 48B generally illustrates a perspective view of another example of an exercise and prehabilitation device according to principles of the present disclosure.

[0064] FIG. 49 generally illustrates example operations of a method for controlling an electromechanical device for prehabilitation in various modes according to principles of the present disclosure.

[0065] FIG. 50 generally illustrates example operations of a method for controlling an amount of resistance provided by an electromechanical device according to principles of the present disclosure.

[0066] FIG. 51 generally illustrates example operations of a method for measuring angles of bend and/or extension of a lower leg relative to an upper leg using a goniometer according to principles of the present disclosure. [0067] FIG. 52 generally illustrates an exploded view of components of the exercise and prehabilitation device according to principles of the present disclosure.

[0068] FIG. 53 generally illustrates an exploded view of a right pedal assembly according to principles of the present disclosure.

[0069] FIG. 54 generally illustrates an exploded view of a motor drive assembly according to principles of the present disclosure.

[0070] FIG. 55 generally illustrates an exploded view of a portion of a goniometer according to principles of the present disclosure.

[0071] FIG. 56 generally illustrates a top view of a wristband according to principles of the present disclosure.

[0072] FIG. 57 generally illustrates an exploded view of a pedal according to principles of the present disclosure.

[0073] FIG. 58 generally illustrates additional views of the pedal according to principles of the present disclosure.

[0074] FIG. 59 generally illustrates an example user interface of the user portal, the user interface presenting a treatment plan for a user according to principles of the present disclosure.

[0075] FIG. 60 generally illustrates an example user interface of the user portal, the user interface presenting pedal settings for a user according to principles of the present disclosure.

[0076] FIG. 61 generally illustrates an example user interface of the user portal, the user interface presenting a scale for measuring pain of the user at a beginning of a pedaling session according to principles of the present disclosure.

[0077] FIG. 62 generally illustrates an example user interface of the user portal, the user interface presenting that the electromechanical device is operating in a passive mode according to principles of the present disclosure.

[0078] FIGs. 63 A-63D generally illustrate an example user interface of the user portal, the user interface presenting that the electromechanical device is operating in active-assisted mode and the user is applying various amounts of force to the pedals according to principles of the present disclosure.

[0079] FIG. 64 generally illustrates an example user interface of the user portal, the user interface presenting a request to modify pedal position while the electromechanical device is operating in active-assisted mode according to principles of the present disclosure.

[0080] FIG. 65 generally illustrates an example user interface of the user portal, the user interface presenting a scale for measuring pain of the user at an end of a pedaling session according to principles of the present disclosure.

[0081] FIG. 66 generally illustrates an example user interface of the user portal, the user interface enabling the user to capture an image of the body part under prehabilitation according to principles of the present disclosure.

[0082] FIGs. 67A-67D generally illustrate an example user interface of the user portal, the user interface presenting angles of extension and bend of a lower leg relative to an upper leg according to principles of the present disclosure. [0083] FIG. 68 generally illustrates an example user interface of the user portal, the user interface presenting a progress screen for a user extending the lower leg away from the upper leg according to principles of the present disclosure.

[0084] FIG. 69 generally illustrates an example user interface of the user portal, the user interface presenting a progress screen for a user bending the lower leg toward the upper leg according to principles of the present disclosure.

[0085] FIG. 70 generally illustrates an example user interface of the user portal, the user interface presenting a progress screen for a pain level of the user according to principles of the present disclosure. [0086] FIG. 71 generally illustrates an example user interface of the user portal, the user interface presenting a progress screen for a strength of a body part according to principles of the present disclosure. [0087] FIG. 72 generally illustrates an example user interface of the user portal, the user interface presenting a progress screen for an amount of steps of the user according to principles of the present disclosure. [0088] FIG. 73 generally illustrates an example user interface of the user portal, the user interface presenting that the electromechanical device is operating in a manual mode according to principles of the present disclosure.

[0089] FIG. 74 generally illustrates an example user interface of the user portal, the user interface presenting an option to modify a speed of the electromechanical device operating in the passive mode according to principles of the present disclosure.

[0090] FIG. 75 generally illustrates an example user interface of the user portal, the user interface presenting an option to modify a minimum speed of the electromechanical device operating in the active-assisted mode according to principles of the present disclosure.

[0091] FIG. 76 generally illustrates an example user interface of the clinical portal, the user interface presenting various options available to the clinician according to principles of the present disclosure.

[0092] FIG. 77 generally illustrates an example computer system according to principles of the present disclosure.

[0093] FIGs. 78A-78G generally illustrate an example prehabilitation system that utilizes machine learning to generate and optimize a prehabilitation plan of a user.

[0094] FIG. 79 generally illustrates a flowchart of an example method for using machine learning to generate a prehabilitation plan for a user and for enabling an electromechanical device to implement an electromechanical device configuration for an exercise session that is part of the prehabilitation plan.

[0095] FIG. 80 shows an example embodiment of a method for receiving a selection of an optimal treatment plan and controlling, based on the optimal treatment plan, a treatment apparatus while the patient uses the treatment apparatus according to the present disclosure.

[0096] FIG. 81 generally illustrates a block diagram of an embodiment of a computer-implemented system for managing a treatment plan according to the principles of the present disclosure.

[0097] FIG. 82 generally illustrates a perspective view of an embodiment of a treatment device according to the principles of the present disclosure.

[0098] FIG. 83 generally illustrates a perspective view of a pedal of the treatment device of FIG. 82 according to the principles of the present disclosure. [0099] FIG. 84 generally illustrates a perspective view of a person using the treatment device of FIG. 82 according to the principles of the present disclosure.

[0100] FIG. 85 generally illustrates an example embodiment of an overview display of an assistant interface according to the principles of the present disclosure.

[0101] FIG. 86 generally illustrates an example block diagram of training a machine learning model to output, based on data pertaining to the patient, a treatment plan for the patient according to the principles of the present disclosure.

[0102] FIG. 87 generally illustrates an embodiment of an overview display of the assistant interface presenting recommended treatment plans and excluded treatment plans in real-time during a telemedicine session according to the principles of the present disclosure.

[0103] FIG. 88 generally illustrates an embodiment of the overview display of the assistant interface presenting, in real-time during a telemedicine session, recommended treatment plans that have changed as a result of patient data changing according to the principles of the present disclosure.

[0104] FIG. 89 is a flow diagram generally illustrating a method for providing, based on treatment data received while a user uses the treatment device of FIG. 2, an enhanced environment to the user while the user uses the treatment device according to the principles of the present disclosure.

[0105] FIG. 90 is a flow diagram generally illustrating an alternative method for providing, based on treatment data received while a user uses the treatment device of FIG. 2, an enhanced environment to the user while the user uses the treatment device according to the principles of the present disclosure.

[0106] FIG. 91 is a flow diagram generally illustrating an alternative method for providing, based on treatment data received while a user uses the treatment device of FIG. 2, an enhanced environment to the user while the user uses the treatment device according to the principles of the present disclosure.

[0107] FIG. 92 is a flow diagram generally illustrating a method for receiving a selection of an optimal treatment plan and controlling, based on the optimal treatment plan, a treatment device while the patient uses the treatment device according to the present disclosure.

[0108] FIG. 93 generally illustrates a computer system according to the principles of the present disclosure. [0109] FIGURE 94 illustrates a high-level component diagram of an illustrative rehabilitation system architecture according to certain embodiments of this disclosure;

[0110] FIGURE 95 A illustrates a perspective view of an example of an exercise and rehabilitation device according to certain embodiments of this disclosure;

[0111] FIGURE 95B illustrates a perspective view of another example of an exercise and rehabilitation device according to certain embodiments of this disclosure;

[0112] FIGURE 96 illustrates example operations of a method for controlling an electromechanical device for rehabilitation in various modes according to certain embodiments of this disclosure;

[0113] FIGURE 97 illustrates example operations of a method for controlling an amount of resistance provided by an electromechanical device according to certain embodiments of this disclosure;

[0114] FIGURE 98 illustrates example operations of a method for measuring angles of bend and/or extension of a lower leg relative to an upper leg using a goniometer according to certain embodiments of this disclosure; [0115] FIGURE 99 illustrates an exploded view of components of the exercise and rehabilitation device according to certain embodiments of this disclosure;

[0116] FIGURE 100 illustrates an exploded view of a right pedal assembly according to certain embodiments of this disclosure;

[0117] FIGURE 101 illustrates an exploded view of a motor drive assembly according to certain embodiments of this disclosure;

[0118] FIGURE 102 illustrates an exploded view of a portion of a goniometer according to certain embodiments of this disclosure;

[0119] FIGURE 103 illustrates a top view of a wristband according to certain embodiments of this disclosure;

[0120] FIGURE 104 illustrates an exploded view of a pedal according to certain embodiments of this disclosure;

[0121] FIGURE 105 illustrates additional views of the pedal according to certain embodiments of this disclosure;

[0122] FIGURE 106 illustrates an example user interface of the user portal, the user interface presenting a treatment plan for a user according to certain embodiments of this disclosure;

[0123] FIGURE 107 illustrates an example user interface of the user portal, the user interface presenting pedal settings for a user according to certain embodiments of this disclosure;

[0124] FIGURE 108 illustrates an example user interface of the user portal, the user interface presenting a scale for measuring pain of the user at a beginning of a pedaling session according to certain embodiments of this disclosure;

[0125] FIGURE 109 illustrates an example user interface of the user portal, the user interface presenting that the electromechanical device is operating in a passive mode according to certain embodiments of this disclosure;

[0126] FIGURES 110A-110D illustrate an example user interface of the user portal, the user interface presenting that the electromechanical device is operating in active-assisted mode and the user is applying various amounts of force to the pedals according to certain embodiments of this disclosure;

[0127] FIGURE 111 illustrates an example user interface of the user portal, the user interface presenting a request to modify pedal position while the electromechanical device is operating in active-assisted mode according to certain embodiments of this disclosure;

[0128] FIGURE 112 illustrates an example user interface of the user portal, the user interface presenting a scale for measuring pain of the user at an end of a pedaling session according to certain embodiments of this disclosure;

[0129] FIGURE 113 illustrates an example user interface of the user portal, the user interface enabling the user to capture an image of the body part under rehabilitation according to certain embodiments of this disclosure;

[0130] FIGURES 114A-114D illustrate an example user interface of the user portal, the user interface presenting angles of extension and bend of a lower leg relative to an upper leg according to certain embodiments of this disclosure; [0131] FIGURE 115 illustrates an example user interface of the user portal, the user interface presenting a progress screen for a user extending the lower leg away from the upper leg according to certain embodiments of this disclosure;

[0132] FIGURE 116 illustrates an example user interface of the user portal, the user interface presenting a progress screen for a user bending the lower leg toward the upper leg according to certain embodiments of this disclosure;

[0133] FIGURE 117 illustrates an example user interface of the user portal, the user interface presenting a progress screen for a pain level of the user according to certain embodiments of this disclosure;

[0134] FIGURE 118 illustrates an example user interface of the user portal, the user interface presenting a progress screen for a strength of a body part according to certain embodiments of this disclosure;

[0135] FIGURE 119 illustrates an example user interface of the user portal, the user interface presenting a progress screen for an amount of steps of the user according to certain embodiments of this disclosure;

[0136] FIGURE 120 illustrates an example user interface of the user portal, the user interface presenting that the electromechanical device is operating in a manual mode according to certain embodiments of this disclosure;

[0137] FIGURE 121 illustrates an example user interface of the user portal, the user interface presenting an option to modify a speed of the electromechanical device operating in the passive mode according to certain embodiments of this disclosure;

[0138] FIGURE 122 illustrates an example user interface of the user portal, the user interface presenting an option to modify a minimum speed of the electromechanical device operating in the active-assisted mode according to certain embodiments of this disclosure;

[0139] FIGURE 123 illustrates an example user interface of the clinical portal, the user interface presenting various options available to the clinician according to certain embodiments of this disclosure;

[0140] FIGURE 124 illustrates an example computer system according to certain embodiments of this disclosure.

[0141] FIGURES 125A-125G illustrate an example rehabilitation system that utilizes machine learning to generate and monitor a treatment plan of a patient.

[0142] FIGURE 126 illustrates a flowchart of an example method for using machine learning to generate a health improvement plan for a user and for enabling an electromechanical device to implement a device configuration for an exercise session that is part of the health improvement plan.

[0143] FIGURE 127 shows an example embodiment of a method for receiving a selection of an optimal treatment plan and controlling, based on the optimal treatment plan, a treatment apparatus while the patient uses the treatment apparatus according to the present disclosure.

NOTATION AND NOMENCLATURE

[0144] Various terms are used to refer to particular system components. Different companies may refer to a component by different names - this document does not intend to distinguish between components that differ in name but not function. In the following discussion and in the claims, the terms “including” and “comprising” are used in an open-ended fashion, and thus should be interpreted to mean “including, but not limited to ... Also, the term “couple” or “couples” is intended to mean either an indirect or direct connection. Thus, if a first device couples to a second device, that connection may be through a direct connection or through an indirect connection via other devices and connections.

[0145] The terminology used herein is for the purpose of describing particular example embodiments only, and is not intended to be limiting. As used herein, the singular forms “a,” “an,” and “the” may be intended to include the plural forms as well, unless the context clearly indicates otherwise. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order discussed or illustrated, unless specifically identified as an order of performance. It is also to be understood that additional or alternative steps may be employed.

[0146] The terms first, second, third, etc. may be used herein to describe various elements, components, regions, layers and/or sections; however, these elements, components, regions, layers and/or sections should not be limited by these terms. These terms may be only used to distinguish one element, component, region, layer, or section from another region, layer, or section. Terms such as “first,” “second,” and other numerical terms, when used herein, do not imply a sequence or order unless clearly indicated by the context. Thus, a first element, component, region, layer, or section discussed below could be termed a second element, component, region, layer, or section without departing from the teachings of the example embodiments. The phrase “at least one of,” when used with a list of items, means that different combinations of one or more of the listed items may be used, and only one item in the list may be needed. For example, “at least one of: A, B, and C” includes any of the following combinations: A, B, C, A and B, A and C, B and C, and A and B and C. In another example, the phrase “one or more” when used with a list of items means there may be one item or any suitable number of items exceeding one.

[0147] Spatially relative terms, such as “inner,” “outer,” “beneath,” “below,” “lower,” “above,” “upper,” “top,” “bottom,” and the like, may be used herein. These spatially relative terms can be used for ease of description to describe one element’s or feature’s relationship to another element(s) or featme(s) as illustrated in the figures. The spatially relative terms may also be intended to encompass different orientations of the device in use, or operation, in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as “below” or “beneath” other elements or features would then be oriented “above” the other elements or features. Thus, the example term “below” can encompass both an orientation of above and below. The device may be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptions used herein interpreted accordingly.

[0148] A “treatment plan” may include one or more treatment protocols, and each treatment protocol includes one or more treatment sessions. Each treatment session comprises several session periods, with each session period including a particular exercise for treating the body part of the patient. For example, a treatment plan for post-operative rehabilitation after a knee surgery may include an initial treatment protocol with twice daily stretching sessions for the first 3 days after surgery and a more intensive treatment protocol with active exercise sessions performed 4 times per day starting 4 days after surgery. A treatment plan may also include information pertaining to a medical procedure to perform on the patient, a treatment protocol for the patient using a treatment device, a diet regimen for the patient, a medication regimen for the patient, a sleep regimen for the patient, additional regimens, or some combination thereof. The treatment plan may also include one or more training protocols, such as strength training protocols, range of motion training protocols, cardiovascular training protocols, endurance training protocols, and the like. Each training protocol may include one or more training sessions comprising several training session periods, with each session period comprising a particular exercise directed to one or more of strength training, range of motion training, cardiovascular training, endurance training, and the like.

[0149] The terms telemedicine, telehealth, telemed, teletherapeutic, telemedicine, remote medicine, etc. may be used interchangeably herein.

[0150] The term “enhanced reality” may include a user experience comprising one or more of augmented reality, virtual reality, mixed reality, immersive reality, or a combination of the foregoing (e.g., immersive augmented reality, mixed augmented reality, virtual and augmented immersive reality, and the like).

[0151] The term “augmented reality” may refer, without limitation, to an interactive user experience that provides an enhanced environment that combines elements of a real-world environment with computer generated components perceivable by the user.

[0152] The term “virtual reality” may refer, without limitation, to a simulated interactive user experience that provides an enhanced environment perceivable by the user and wherein such enhanced environment may be similar to or different from a real-world environment.

[0153] The term “mixed reality” may refer to an interactive user experience that combines aspects of augmented reality with aspects of virtual reality to provide a mixed reality environment perceivable by the user. [0154] The term “immersive reality” may refer to a simulated interactive user experienced using virtual and/or augmented reality images, sounds, and other stimuli to immerse the user, to a specific extent possible (e.g., partial immersion or total immersion), in the simulated interactive experience. For example, in some embodiments, to the specific extent possible, the user experiences one or more aspects of the immersive reality as naturally as the user typically experiences corresponding aspects of the real-world. Additionally, or alternatively, an immersive reality experience may include actors, a narrative component, a theme (e.g., an entertainment theme or other suitable theme), and/or other suitable features of components.

[0155] The term “body halo” may refer to a hardware component or components, wherein such component or components may include one or more platforms, one or more body supports or cages, one or more chairs or seats, one or more back supports or back engaging mechanisms, one or more leg or foot engaging mechanisms, one or more arm or hand engaging mechanisms, one or more head engaging mechanisms, other suitable hardware components, or a combination thereof.

[0156] As used herein, the term “enhanced environment” may refer to an enhanced environment in its entirety, at least one aspect of the enhanced environment, more than one aspect of the enhanced environment, or any suitable number of aspects of the enhanced environment.

[0157] As used herein, the term “threshold” and/or the term “range” may include one or more values expressed as a percentage, an absolute value, a unit of measurement, a difference value, a numerical quantity, or other suitable expression of the one or more values.

[0158] The term “optimal treatment plan” may refer to optimizing a treatment plan based on a certain parameter or combinations of more than one parameter, such as, but not limited to, a monetary value amount generated by a treatment plan and/or billing sequence, wherein the monetary value amount is measured by an absolute amount in dollars or another currency, a Net Present Value (NPV) or any other measure, a patient outcome that results from the treatment plan and/or billing sequence, a fee paid to a medical professional, a payment plan for the patient to pay off an amount of money owed or a portion thereof, a plan of reimbursement, an amount of revenue, profit or other monetary value amount to be paid to an insurance or third-party provider, or some combination thereof.

[0159] Real-time may refer to less than or equal to 2 seconds. Near real-time may refer to any interaction of a sufficiently short time to enable two individuals to engage in a dialogue via such user interface, and will generally be less than 10 seconds but greater than 2 seconds.

[0160] Any of the systems and methods described in this disclosure may be used in connection with rehabilitation. Rehabilitation may be directed at cardiac rehabilitation, rehabilitation from stroke, multiple sclerosis, Parkinson’s disease, myasthenia gravis, Alzheimer’s disease, any other neurodegenative or neuromuscular disease, a brain injury, a spinal cord injury, a spinal cord disease, a joint injury, a joint disease, or the like. Rehabilitation can further involve muscular contraction in order to improve blood flow and lymphatic flow, engage the brain and nervous system to control and affect a traumatized area to increase the speed of healing, reverse or reduce pain (including arthralgias and myalgias), reverse or reduce stiffness, recover range of motion, encourage cardiovascular engagement to stimulate the release of pain-blocking hormones or to encourage highly oxygenated blood flow to aid in an overall feeling of well-being. Rehabilitation may be provided for individuals of average height in reasonably good physical condition having no substantial deformities, as well as for individuals more typically in need of rehabilitation, such as those who are elderly, obese, subject to disease processes, injured and/or who have a severely limited range of motion. Unless expressly stated otherwise, is to be understood that rehabilitation includes prehabilitation (also referred to as '^re habilitation" or "prehab"). Prehabilitation may be used as a preventative procedure or as a pre-surgical or pre treatment procedure. Prehabilitation may include any action performed by or on a patient (or directed to be performed by or on a patient, including, without limitation, remotely or distally through telemedicine) to, without limitation, prevent or reduce a likelihood of injury (e.g., prior to the occurrence of the injury); improve recovery time subsequent to surgery; improve strength subsequent to surgery; or any of the foregoing with respect to any non-surgical clinical treatment plan to be undertaken for the purpose of ameliorating or mitigating injury, dysfunction, or other negative consequence of surgical or non-surgical treatment on any external or internal part of a patient's body. For example, a mastectomy may require prehabilitation to strengthen muscles or muscle groups affected directly or indirectly by the mastectomy. As a further non-limiting example, the removal of an intestinal tumor, the repair of a hernia, open-heart surgery or other procedures performed on internal organs or structures, whether to repair those organs or structures, to excise them or parts of them, to treat them, etc., can require cutting through, dissecting and/or harming numerous muscles and muscle groups in or about, without limitation, the skull or face, the abdomen, the ribs and/or the thoracic cavity, as well as in or about all joints and appendages. Prehabilitation can improve a patient's speed of recovery, measure of quality of life, level of pain, etc. in all the foregoing procedures. In one embodiment of prehabilitation, a pre-surgical procedure or a pre-non-surgical-treatment may include one or more sets of exercises for a patient to perform prior to such procedure or treatment. Performance of the one or more sets of exercises may be required in order to qualify for an elective surgery, such as a knee replacement. The patient may prepare an area of his or her body for the surgical procedure by performing the one or more sets of exercises, thereby strengthening muscle groups, improving existing muscle memory, reducing pain, reducing stiffness, establishing new muscle memory, enhancing mobility (i.e., improve range of motion), improving blood flow, and/or the like.

DETAILED DESCRIPTION

[0161] The following discussion is directed to various embodiments of the present disclosure. Although one or more of these embodiments may be preferred, the embodiments disclosed should not be interpreted, or otherwise used, as limiting the scope of the disclosure, including the claims. In addition, one skilled in the art will understand that the following description has broad application, and the discussion of any embodiment is meant only to be exemplary of that embodiment, and not intended to intimate that the scope of the disclosure, including the claims, is limited to that embodiment.

[0162] Determining optimal remote examination procedures to create an optimal treatment plan for a patient having certain characteristics (e.g., vital-sign or other measurements; performance; demographic; geographic; psychographic; diagnostic; measurement- or test-based; medically historic; behavioral historic; cognitive; etiologic; cohort-associative; differentially diagnostic; surgical, physically therapeutic, pharmacologic and other treatment(s) recommended; etc.) may be a technically challenging problem. For example, a multitude of information may be considered when determining a treatment plan, which may result in inefficiencies and inaccuracies in the treatment plan selection process. In a rehabilitative setting, some of the multitude of information considered may include characteristics of the patient such as personal information, performance information, and measurement information. The personal information may include, e.g., demographic, psychographic or other information, such as an age, a weight, a gender, a height, a body mass index, a medical condition, a familial medication history, an injury, a medical procedure, a medication prescribed, or some combination thereof. The performance information may include, e.g., an elapsed time of using a treatment device, an amount of force exerted on a portion of the treatment device, a range of motion achieved on the treatment device, a movement speed of a portion of the treatment device, a duration of use of the treatment device, an indication of a plurality of pain levels using the treatment device, or some combination thereof. The measurement information may include, e.g., a vital sign, a respiration rate, a heartrate, a temperature, a blood pressure, a glucose level or other biomarker, or some combination thereof. It may be desirable to process and analyze the characteristics of a multitude of patients, the treatment plans performed for those patients, and the results of the treatment plans for those patients.

[0163] Further, another technical problem may involve distally treating, via a computing device during a telemedicine session, a patient from a location different than a location at which the patient is located. An additional technical problem is controlling or enabling, from the different location, the control of a treatment apparatus used by the patient at the patient’s location. Oftentimes, when a patient undergoes rehabilitative surgery (e.g., knee surgery), a medical professional may prescribe a treatment apparatus to the patient to use to perform a treatment protocol at their residence or at any mobile location or temporary domicile. A medical professional may refer to a doctor, physician assistant, nurse, chiropractor, dentist, physical therapist, acupuncturist, physical trainer, or the like. A medical professional may refer to any person with a credential, license, degree, or the like in the field of medicine, physical therapy, rehabilitation, or the like.

[0164] When the healthcare provider is located in a location different from the patient and the treatment device, it may be technically challenging for the healthcare provider to monitor the patient’s actual progress (as opposed to relying on the patient’ s word about their progress) in using the treatment device, modify the treatment plan according to the patient’s progress, adapt the treatment device to the personal characteristics of the patient as the patient performs the treatment plan, and the like. Further, in addition to the information described above, determining optimal examination procedures for a particular ailment (e.g., injury, disease, any applicable medical condition, etc.) may include physically examining the injured body part of a patient. The healthcare provider, such as a physician or a physical therapist, may visually inspect the injured body part (e.g., a knee joint). The inspection may include looking for signs of inflammation or injury (e.g., swelling, redness, and warmth), deformity (e.g., symmetrical joints and abnormal contours and/or appearance), or any other suitable observation. To determine limitations of the injured body part, the healthcare provider may observe the injured body part as the patient attempts to perform normal activity (e.g., bending and extending the knee and gauging any limitations to the range of motion of the injured knee). The healthcare provide may use one or more hands and/or fingers to touch the injured body part. By applying pressure to the injured body part, the healthcare provider can obtain information pertaining to the extent of the injury. For example, the healthcare provider’s fingers may palpate the injured body part to determine if there is point tenderness, warmth, weakness, strength, or to make any other suitable observation.

[0165] It may be desirable to compare characteristics of the injured body part with characteristics of a corresponding non-injured body part to determine what an optimal treatment plan for the patient may be such that the patient can obtain a desired result. Thus, the healthcare provider may examine a corresponding non- injured body part of the patient. For example, the healthcare provider’s fingers may palpate a non-injured body part (e.g., a left knee) to determine a baseline of how the patient’s non-injured body part feels and functions. The healthcare provider may use the results of the examination of the non-injured body part to determine the extent of the injury to the corresponding injured body part (e.g., a right knee). Additionally, injured body parts may affect other body parts (e.g., a knee injury may limit the use of the affected leg, leading to atrophy of leg muscles). Thus, the healthcare provider may also examine additional body parts of the patient for evidence of atrophy of or injury to surrounding ligaments, tendons, bones, and muscles, examples of muscles being such as quadriceps, hamstrings, or calf muscle groups of the leg with the knee injury. The healthcare provider may also obtain information as to a pain level that the patient reports or experiences before, during, and/or after the examination.

[0166] The healthcare provider can use the information obtained from the examination (e.g., the results of the examination) to determine a proper treatment plan for the patient. If the healthcare provider cannot conduct a physical examination of the one or more body parts of the patient, the healthcare provider may not be able to fully assess the patient’s injury and the treatment plan may not be optimal. Accordingly, embodiments of the present disclosure pertain to systems and methods for conducting a remote examination of a patient. The remote examination system provides the healthcare provider with the ability to conduct a remote examination of the patient, not only by communicating with the patient, but by virtually observing and/or feeling the patient’s one or more body parts.

[0167] In some embodiments, the systems and methods described herein may be configured to use a treatment device configured to be manipulated by an individual while the user performs a treatment plan. The individual may include a user, patient, or other a person using the treatment device to perform various exercises for prehabilitation, rehabilitation, stretch training, and the like. The systems and methods described herein may be configured to use and/or provide a patient interface comprising an output device configured to present telemedicine information associated with a telemedicine session.

[0168] In some embodiments, the systems and methods described herein may be configured to receive a treatment plan for a patient; during the telemedicine session, use the treatment plan to generate at least one parameter; and responsive to at least one trigger condition occurring, control at least one operation of the device. Any or all of the methods described may be implemented during a telemedicine session or at any other desired time.

[0169] In some embodiments, the treatment devices may be communicatively coupled to a server. Characteristics of the patients, including the treatment data, may be collected before, during, and/or after the patients perform the treatment plans. For example, any or each of the personal information, the performance information, and the measurement information may be collected before, during, and/or after a patient performs the treatment plans. The results (e.g., improved performance or decreased performance) of performing each exercise may be collected from the treatment device throughout the treatment plan and after the treatment plan is performed. The parameters, settings, configurations, etc. (e.g., position of pedal, amount of resistance, etc.) of the treatment device may be collected before, during, and/or after the treatment plan is performed.

[0170] Each characteristic of the patient, each result, and each parameter, setting, configuration, etc. may be timestamped and may be correlated with a particular step or set of steps in the treatment plan. Such a technique may enable the determination of which steps in the treatment plan lead to desired results (e.g., improved muscle strength, range of motion, etc.) and which steps lead to diminishing returns (e.g., continuing to exercise after 3 minutes actually delays or harms recovery).

[0171] Data may be collected from the treatment devices and/or any suitable computing device (e.g., computing devices where personal information is entered, such as the interface of the computing device described herein, a clinician interface, patient interface, and the like) over time as the patients use the treatment devices to perform the various treatment plans. The data that may be collected may include the characteristics of the patients, the treatment plans performed by the patients, the results of the treatment plans, any of the data described herein, any other suitable data, or a combination thereof.

[0172] In some embodiments, the data may be processed to group certain people into cohorts. The people may be grouped by people having certain or selected similar characteristics, treatment plans, and results of performing the treatment plans. For example, athletic people having no medical conditions who perform a treatment plan (e.g., use the treatment device for 30 minutes a day 5 times a week for 3 weeks) and who fully recover may be grouped into a first cohort. Older people who are classified obese and who perform a treatment plan (e.g., use the treatment plan for 10 minutes a day 3 times a week for 4 weeks) and who improve their range of motion by 75 percent may be grouped into a second cohort.

[0173] In some embodiments, an artificial intelligence engine may include one or more machine learning models that are trained using the cohorts. In some embodiments, the artificial intelligence engine may be used to identity trends and/or patterns and to define new cohorts based on achieving desired results from the treatment plans and machine learning models associated therewith may be trained to identify such trends and/or patterns and to recommend and rank the desirability of the new cohorts. For example, the one or more machine learning models may be trained to receive an input of characteristics of a new patient and to output a treatment plan for the patient that results in a desired result. The machine learning models may match a pattern between the characteristics of the new patient and at least one patient of the patients included in a particular cohort. When a pattern is matched, the machine learning models may assign the new patient to the particular cohort and select the treatment plan associated with the at least one patient. The artificial intelligence engine may be configured to control, distally and based on the treatment plan, the treatment device while the new patient uses the treatment device to perform the treatment plan.

[0174] As may be appreciated, the characteristics of the new patient (e.g., a new user) may change as the new patient uses the treatment device to perform the treatment plan. For example, the performance of the patient may improve quicker than expected for people in the cohort to which the new patient is currently assigned. Accordingly, the machine learning models may be trained to dynamically reassign, based on the changed characteristics, the new patient to a different cohort that includes people having characteristics similar to the now-changed characteristics as the new patient. For example, a clinically obese patient may lose weight and no longer meet the weight criterion for the initial cohort, result in the patient’ s being reassigned to a different cohort with a different weight criterion.

[0175] A different treatment plan may be selected for the new patient, and the treatment device may be controlled, distally (e.g., which may be referred to as remotely) and based on the different treatment plan, while the new patient uses the treatment device to perform the treatment plan. Such techniques may provide the technical solution of distally controlling a treatment device.

[0176] Further, the systems and methods described herein may lead to faster recovery times and/or better results for the patients because the treatment plan that most accurately fits their characteristics is selected and implemented, in real-time, at any given moment. “Real-time” may also refer to near real-time, which may be less than 10 seconds or any reasonably proximate difference between two different times. As described herein, the term “results” may refer to medical results or medical outcomes. Results and outcomes may refer to responses to medical actions. The term “medical action(s)” may refer to any suitable action performed by the medical professional, and such action or actions may include diagnoses, prescription of treatment plans, prescription of treatment devices, and the making, composing and/or executing of appointments, telemedicine sessions, prescription of medicines, telephone calls, emails, text messages, and the like.

[0177] Depending on what result is desired, the artificial intelligence engine may be trained to output several treatment plans. For example, one result may include recovering to a threshold level (e.g., 75% range of motion) in a fastest amount of time, while another result may include fully recovering (e.g., 100% range of motion) regardless of the amount of time. The data obtained from the patients and sorted into cohorts may indicate that a first treatment plan provides the first result for people with characteristics similar to the patient’s, and that a second treatment plan provides the second result for people with characteristics similar to the patient.

[0178] Further, the artificial intelligence engine may be trained to output treatment plans that are not optimal i.e., sub-optimal, nonstandard, or otherwise excluded (all referred to, without limitation, as “excluded treatment plans”) for the patient. For example, if a patient has high blood pressure, a particular exercise may not be approved or suitable for the patient as it may put the patient at unnecessary risk or even induce a hypertensive crisis and, accordingly, that exercise may be flagged in the excluded treatment plan for the patient. In some embodiments, the artificial intelligence engine may monitor the treatment data received while the patient (e.g., the user) with, for example, high blood pressure, uses the treatment device to perform an appropriate treatment plan and may modify the appropriate treatment plan to include features of an excluded treatment plan that may provide beneficial results for the patient if the treatment data indicates the patient is handling the appropriate treatment plan without aggravating, for example, the high blood pressure condition of the patient. In some embodiments, the artificial intelligence engine may modify the treatment plan if the monitored data shows the plan to be inappropriate or counterproductive for the user.

[0179] In some embodiments, the treatment plans and/or excluded treatment plans may be presented, during a telemedicine or telehealth session, to a healthcare provider. The healthcare provider may select a particular treatment plan for the patient to cause that treatment plan to be transmitted to the patient and/or to control, based on the treatment plan, the treatment device. In some embodiments, to facilitate telehealth or telemedicine applications, including remote diagnoses, determination of treatment plans and rehabilitative and/or pharmacologic prescriptions, the artificial intelligence engine may receive and/or operate distally from the patient and the treatment device.

[0180] In such cases, the recommended treatment plans and/or excluded treatment plans may be presented simultaneously with a video of the patient in real-time or near real-time during a telemedicine or telehealth session on a user interface of a computing device of a medical professional. The video may also be accompanied by audio, text and other multimedia information and/or other sensorial or perceptive (e.g., tactile, gustatory, haptic, pressure-sensing-based or electromagnetic (e.g., neurostimulation). Real-time may refer to less than or equal to 2 seconds. Near real-time may refer to any interaction of a sufficiently short time to enable two individuals to engage in a dialogue via such user interface, and will generally be less than 10 seconds (or any suitably proximate difference between two different times) but greater than 2 seconds.

[0181] Presenting the treatment plans generated by the artificial intelligence engine concurrently with a presentation of the patient video may provide an enhanced user interface because the healthcare provider may continue to visually and/or otherwise communicate with the patient while also reviewing the treatment plans on the same user interface. The enhanced user interface may improve the healthcare provider’s experience using the computing device and may encourage the healthcare provider to reuse the user interface. Such a technique may also reduce computing resources (e.g., processing, memory, network) because the healthcare provider does not have to switch to another user interface screen to enter a query for a treatment plan to recommend based on the characteristics of the patient. The artificial intelligence engine may be configured to provide, dynamically on the fly, the treatment plans and excluded treatment plans.

[0182] In some embodiments, the treatment device may be adaptive and/or personalized because its properties, configurations, and positions may be adapted to the needs of a particular patient. For example, the pedals may be dynamically adjusted on the fly (e.g., via a telemedicine session or based on programmed configurations in response to certain measurements being detected) to increase or decrease a range of motion to comply with a treatment plan designed for the user. In some embodiments, a healthcare provider may adapt, remotely during a telemedicine session, the treatment device to the needs of the patient by causing a control instruction to be transmitted from a server to treatment device. Such adaptive nature may improve the results of recovery for a patient, furthering the goals of personalized medicine, and enabling personalization of the treatment plan on a per-individual basis.

[0183] FIGS. 1-13, discussed below, and the various embodiments used to describe the principles of this disclosure are by way of illustration only and should not be construed in any way to limit the scope of the disclosure. [0184] FIG. 1 illustrates a high-level component diagram of an illustrative remote examination system 100 according to certain embodiments of this disclosure. In some embodiments, the remote examination system 100 may include a slave computing device 102 communicatively coupled to a slave device, such as a treatment device 106. The treatment device can include a slave sensor 108 and a slave pressure system 110. The slave pressure system can include a slave motor 112. The remote examination system may also be communicatively coupled to an imaging device 116. Each of the slave computing device 102, the treatment device 106, and the imaging device 116 may include one or more processing devices, memory devices, and network interface cards. The network interface cards may enable communication via a wireless protocol for transmitting data over short distances, such as Bluetooth, ZigBee, etc. In some embodiments, the slave computing device 102 is communicatively coupled to the treatment device 106 and the imaging device 116 via Bluetooth.

[0185] Additionally, the network interface cards may enable communicating data over long distances, and in one example, the slave computing device 102 may communicate with a network 104. The network 104 may be a public network (e.g., connected to the Internet via wired (Ethernet) or wireless (WiFi)), a private network (e.g., a local area network (LAN) or wide area network (WAN)), or a combination thereof. The slave computing device 102 may be communicatively coupled with one or more master computing devices 122 and a cloud-based computing system 142.

[0186] The slave computing device 102 may be any suitable computing device, such as a laptop, tablet, smartphone, or computer. The slave computing device 102 may include a display capable of presenting a user interface, such as a patient portal 114. The patient portal 114 may be implemented in computer instructions stored on the one or more memory devices of the slave computing device 102 and executable by the one or more processing devices of the slave computing device 102. The patient portal 114 may present various screens to a patient that enable the patient to view his or her medical records, a treatment plan, or progress during the treatment plan; to initiate a remote examination session; to control parameters of the treatment device 106; to view progress of rehabilitation during the remote examination session; or combination thereof. The slave computing device 102 may also include instructions stored on the one or more memory devices that, when executed by the one or more processing devices of the slave computing device 102, perform operations to control the treatment device 106.

[0187] The slave computing device 102 may execute the patient portal 114. The patient portal 114 may be implemented in computer instructions stored on the one or more memory devices of the slave computing device 102 and executable by the one or more processing devices of the slave computing device 102. The patient portal 114 may present various screens to a patient which enable the patient to view a remote examination provided by a healthcare provider, such as a physician or a physical therapist. The patient portal 114 may also provide remote examination information for a patient to view. The examination information can include a summary of the examination and/or results of the examination in real-time or near real-time, such as measured properties (e.g., angles of bend/extension, pressure exerted on the treatment device 106, images of the examined/treated body part, vital signs of the patient, such as heart rate, temperature, etc.) of the patient during the examination. The patient portal 114 may also provide the patient’s health information, such as a health history, a treatment plan, and a progress of the patient throughout the treatment plan. So the examination of the patient may begin, the examination information specific to the patient may be transmitted via the network 104 to the cloud-based computing system 142 for storage and/or to the slave computing device 102. [0188] The treatment device 106 may be an examination device for a body part of a patient. As illustrated in FIGS. 2 A-D, the treatment device 106 can be configured in alternative arrangements and is not limited to the example embodiments described in this disclosure. Although not illustrated, the treatment device 106 can include a slave motor 112 and a motor controller 118. The treatment device 106 can include a slave pressure system 110. The slave pressure system 110 is any suitable pressure system configured to increase and/or decrease the pressure in the treatment device 106. For example, the slave pressure system 110 can comprise the slave motor 112, the motor controller 118, and a pump. The motor controller 118 can activate the slave motor 112 to cause a pump or any other suitable device to inflate or deflate one or more sections 210 of the treatment device 106. The treatment device 106 can be operatively coupled to one or more slave processing devices. The one or more slave processing devices can be configured to execute instructions in accordance with aspects of this disclosure.

[0189] As illustrated in FIG. 2A, the treatment device 106 may comprise a brace 202 (e.g., a knee brace) configured to fit on the patient’s body part, such as an arm, a wrist, a neck, a torso, a leg, a knee, an ankle, hips, or any other suitable body part. The brace 202 may include slave sensors 108. The slave sensors 108 can be configured to detect information associated with the patient. For example, the slave sensors 108 can detect a measured level of force exerted from the patient to the treatment device 106, a temperature of the one or more body parts in contact with the patient, a movement of the treatment device 106, any other suitable information, or any combination thereof. The brace 202 may include sections 210. The sections 210 can be formed as one or more chambers. The sections 210 may be configured to be filled with a liquid (e.g., a gel, air, water, etc.). The sections 210 may be configured in one or more shapes, such as, but not limited to rectangles, squares, diamonds circles, trapezoids, any other suitable shape, or combination thereof. The sections 210 may be the same or different sizes. The sections 210 may be positioned throughout the treatment device 106. The sections 210 can be positioned on the brace 202 above a knee portion, below the knee portion, and along the sides of the knee portion. In some embodiments, the brace 202 may include sections 210 positioned adjacent to each other and positioned throughout the brace 202. The sections 210 are not limited to the exemplary illustrations in FIG. 4. The brace 202 may include the one or more materials for the brace 202 and, in some embodiments, straps coupled to the brace 202. The brace 202 be formed from metal, foam, plastic, elastic, or any suitable material or combination of materials. The brace 202 may be formed in any suitable shape, size, or design.

[0190] As illustrated in FIG. 2B, the treatment device 106 may comprise a cap 204 that can be configured to fit onto the patient’s head. FIG. 2B illustrates exemplary layers of the treatment device 106. The treatment device 106 may include a first layer 212 and a second layer 214. The first layer may be an outer later and the second layer 214 may be an inner layer. The second layer 214 may include the sections 210 and one or more sensors 108. In this example embodiment, the sections 210 are coupled to and/or from portions of the second layer 214. The sections 210 can be configured in a honeycomb pattern. The one or more sensors 108 may be coupled to the first layer 212. The first layer 212 can be coupled to the second layer 214. The first layer 212 can be designed to protect the sections 210 and the sensors 108. The cap 204 may include a strap. The cap 204 and/or the strap be formed from metal, foam, plastic, elastic, or any suitable material or combination of materials. The cap 204 may be formed in any suitable shape, size, or design.

[0191] As illustrated in FIG. 2C, the slave may comprise a mat 206. The mat 206 may be configured for a patient to lie or sit down, or to stand upon. The mat 206 may include one or more sensors 108. The mat 206 may include one or more sections 210. The sections 210 in the treatment device 106 can be configured in a square grid pattern. The one or more sensors 108 may be coupled to and/or positioned within the one or more sections 210. The mat 206 can be rectangular, circular, square, or any other suitable configuration. The mat 206 be formed from metal, foam, plastic, elastic, or any suitable material or combination of materials. The mat 206 may include one or more layers, such as a top layer.

[0192] As illustrated in FIG. 2D, the slave may comprise a wrap 208. The wrap 208 may be configured to wrap the wrap 208 around one or more portions and/or one or more body parts of the patient. For example, the wrap 208 may be configured to wrap around a person’s torso. The wrap 208 may include one or more sensors 108. The wrap 208 may include one or more sections 210. The sections 210 in the treatment device 106 can be configured in a diamond grid pattern. The one or more sensors 108 may be coupled to and/or positioned within the one or more sections 210. The wrap 208 can be rectangular, circular, square, or any other suitable configuration. The wrap 208 may include a strap. The wrap 208 and/or the strap be formed from metal, foam, plastic, elastic, or any suitable material or combination of materials.

[0193] The treatment device 106 may include at least one or more motor controllers 118 and one or more motors 112, such as an electric motor. A pump, not illustrated, may be operatively coupled to the motor. The pump may be a hydraulic pump or any other suitable pump. The pump may be configured to increase or decrease pressure within the treatment device 106. The size and speed of the pump may determine the flow rate (i.e., the speed that the load moves) and the load at the slave motor 112 may determine the pressure in one or more sections 210 of the treatment device 106. The pump can be activated to increase or decrease pressure in the one or more sections 210. One or more of the sections 210 may include a sensor 108. The sensor 108 can be a sensor for detecting signals, such as a measured level of force, a temperature, or any other suitable signal. The motor controller 118 may be operatively coupled to the motor 112 and configured to provide commands to the motor 112 to control operation of the motor 112. The motor controller 118 may include any suitable microcontroller including a circuit board having one or more processing devices, one or more memory devices (e.g., read-only memory (ROM) and/or random access memory (RAM)), one or more network interface cards, and/or programmable input/output peripherals. The motor controller 118 may provide control signals or commands to drive the motor 112. The motor 112 may be powered to drive the pump of the treatment device 106. The motor 112 may provide the driving force to the pump to increase or decrease pressure at configurable speeds. Further, the treatment device 106 may include a current shunt to provide resistance to dissipate energy from the motor 112. In some embodiments, the treatment device 106 may comprise a haptic system, a pneumatic system, any other suitable system, or combination thereof. For example, the haptic system can include a virtual touch by applying forces, vibrations, or motions to the patient through the treatment device 106.

[0194] The slave computing device 102 may be communicatively connected to the treatment device 106 via a network interface card on the motor controller 118. The slave computing device 102 may transmit commands to the motor controller 118 to control the motor 112. The network interface card of the motor controller 118 may receive the commands and transmit the commands to the motor 112 to drive the motor 112. In this way, the slave computing device 102 is operatively coupled to the motor 112.

[0195] The slave computing device 102 and/or the motor controller 118 may be referred to as a control system (e.g., a slave control system) herein. The patient portal 114 may be referred to as a patient user interface of the control system. The control system may control the motor 112 to operate in a number of modes: standby, inflate, and deflate. The standby mode may refer to the motor 112 powering off so it does not provide a driving force to the one or more pumps. For example, if the pump does not receive instructions to inflate or deflate the treatment device 106, the motor 112 may remain turned off. In this mode, the treatment device 106 may not provide additional pressure to the patient’s body part(s).

[0196] The inflate mode may refer to the motor 112 receiving manipulation instructions comprising measurements of pressure, causing the motor 112 to drive the one or more pumps coupled to the one or more sections of the treatment device 106 to inflate the one or more sections. The manipulation instruction may be configurable by the healthcare provider. For example, as the healthcare provider moves a master device 126, the movement is provided in a manipulation instruction for the motor 112 to drive the pump to inflate one or more sections of the treatment device 106. The manipulation instruction may include a pressure gradient to inflate first and second sections in a right side of a knee brace to first and second measured levels of force and inflate a third section in a left side of the knee brace to a third measured level of force. The first measured level of force correlates with the amount of pressure applied to the master device 126 by the healthcare provider’s first finger. The second measured level of force correlates with the amount of pressure applied to the master device 126 by the healthcare provider’s second finger. The third measured level of force correlates with the amount of pressure applied to the master device 126 by the healthcare provider’s third finger.

[0197] The deflation mode may refer to the motor 112 receiving manipulation instructions comprising measurements of pressure, causing the motor 112 to drive the one or more pumps coupled to the one or more sections of the treatment device 106 to deflate the one or more sections. The manipulation instruction may be configurable by the healthcare provider. For example, as the healthcare provider moves the master device 126, the movement is provided in a manipulation instruction for the motor 112 to drive the pump to deflate one or more sections of the treatment device 106. The manipulation instruction may include a pressure gradient to deflate the first and second sections in the right side of the knee brace to fourth and fifth measured levels of force and deflate the third section in the left side of the knee brace to the third measured level of force. The fourth measured level of force correlates with the amount of pressure applied to the master device 126 by the healthcare provider’ s first finger. The fifth measured level of force correlates with the amount of pressure applied to the master device 126 by the healthcare provider’s second finger. The sixth measured level of force correlates with the amount of pressure applied to the master device 126 by the healthcare provider’s third finger. In this example, the healthcare provider loosened a grip (e.g., applied less pressure to each of the three fingers) applied to the treatment device 106 virtually via the master device 126.

[0198] During one or more of the modes, the one or more slave sensors 108 may measure force (i.e., pressure or weight) exerted by a part of the body of the patient. For example, the each of the one or more sections 310 of the treatment device 106 may contain any suitable sensor (e.g., strain gauge load cell, piezoelectric crystal, hydraulic load cell, etc.) for measuring force exerted on the treatment device 106. Further, the each of the one or more sections 310 of the treatment device 106 may contain any suitable sensor for detecting whether the body part of the patient separates from contact with the treatment device 106. The force detected may be transmitted via the network interface card of the treatment device 106 to the control system (e.g., slave computing device 102 and/or the slave controller 118). As described further below, the control system may modify a parameter of operating the slave motor 112 using the measured force. Further, the control system may perform one or more preventative actions (e.g., locking the slave motor 112 to stop the pump from activating, slowing down the slave motor 112, presenting a notification to the patient such as via the patient portal 114, etc.) when the body part is detected as separated from the treatment device 106, among other things. [0199] In some embodiments, the remote examination system 100 includes the imaging device 116. The imaging device 116 may be configured to capture and/or measure angles of extension and/or bend of body parts and transmit the measured angles to the slave computing device 102 and/or the master computing device 122. The imaging device 116 may be included in an electronic device that includes the one or more processing devices, memory devices, and/or network interface cards. The imaging device 116 may be disposed in a cavity of the treatment device 106 (e.g., in a mechanical brace). The cavity of the mechanical brace may be located near a center of the mechanical brace such that the mechanical brace affords to bend and extend. The mechanical brace may be configured to secure to an upper body part (e.g., leg, arm, etc.) and a lower body part (e.g., leg, arm, etc.) to measure the angles of bend as the body parts are extended away from one another or retracted closer to one another.

[0200] The imaging device 116 canbe a wearable, such as a wristband 704. The wristband 704 may include a 2-axis accelerometer to track motion in the X, Y, and Z directions, an altimeter for measuring altitude, and/or a gyroscope to measure orientation and rotation. The accelerometer, altimeter, and/or gyroscope may be operatively coupled to a processing device in the wristband 704 and may transmit data to the processing device. The processing device may cause a network interface card to transmit the data to the slave computing device 102 and the slave computing device 102 may use the data representing acceleration, frequency, duration, intensity, and patterns of movement to track measurements taken by the patient over certain time periods (e.g., days, weeks, etc.). Executing a clinical portal 134, the slave computing device 102 may transmit the measurements to the master computing device 122. Additionally, in some embodiments, the processing device of the wristband 704 may determine the measurements taken and transmit the measurements to the slave computing device 102. In some embodiments, the wristband 704 may use photoplethysmography (PPG), which detects an amount of red light or green light on the skin of the wrist, to measure heart rate. For example, blood may absorb green light so that when the heart beats, the blood flow may absorb more green light, thereby enabling the detection of heart rate. The heart rate may be sent to the slave computing device 102 and/or the master computing device 122.

[0201] The slave computing device 102 may present the measurements (e.g., measured level of force or temperature) of the body part of the patient taken by the treatment device 106 and/or the heart rate of the patient via a graphical indicator (e.g., a graphical element) on the patient portal 114, as discussed further below. The slave computing device 102 may also use the measurements and/or the heart rate to control a parameter of operating the treatment device 106. For example, if the measured level of force exceeds a target pressure level for an examination session, the slave computing device 102 may control the motor 112 to reduce the pressure being applied to the treatment device 106.

[0202] In some embodiments, the remote examination system 100 may include a master computing device 122 communicatively coupled to a master console 124. The master console 124 can include a master device 126. The master device 126 can include a master sensor 128 and a master pressure system 130. The master pressure system can include a master motor 132. The remote examination system may also be communicatively coupled to a master display 136. Each of the master computing device 122, the master device 126, and the master display 136 may include one or more processing devices, memory devices, and network interface cards. The network interface cards may enable communication via a wireless protocol for transmitting data over short distances, such as Bluetooth, ZigBee, Near-Field Communications (NFC), etc. In some embodiments, the master computing device 122 is communicatively coupled to the master device 126 and the master display 136 via Bluetooth.

[0203] Additionally, the network interface cards may enable communicating data over long distances, and in one example, the master computing device 122 may communicate with a network 104. The master computing device 122 may be communicatively coupled with the slave computing device 102 and the cloud-based computing system 142.

[0204] The master computing device 122 may be any suitable computing device, such as a laptop, tablet, smartphone, or computer. The master computing device 122 may include a display capable of presenting a user interface, such as a clinical portal 134. The clinical portal 134 may be implemented in computer instructions stored on the one or more memory devices of the master computing device 122 and executable by the one or more processing devices of the master computing device 122. The clinical portal 134 may present various screens to a user (e.g., a healthcare provider), the screens configured to enable the user to view a patient’s medical records, a treatment plan, or progress during the treatment plan; to initiate a remote examination session; to control parameters of the master device 126; to view progress of rehabilitation during the remote examination session, or combination thereof. The master computing device 122 may also include instructions stored on the one or more memory devices that, when executed by the one or more processing devices of the master computing device 122, perform operations to control the master device 126.

[0205] The master computing device 122 may execute the clinical portal 134. The clinical portal 134 may be implemented in computer instructions stored on the one or more memory devices of the master computing device 122 and executable by the one or more processing devices of the master computing device 122. The clinical portal 134 may present various screens to a healthcare provider (e.g., a clinician), the screens configured to enables the clinician to view a remote examination of a patient, such as a patient rehabilitating from a surgery (e.g., knee replacement surgery) or from an injury (e.g., sprained ankle). During a telemedicine session, an augmented image representing one or more body parts of the patient may be presented simultaneously with a video of the patient on the clinical portal 134 in real-time or in near real-time. For example, the clinical portal 134 may, at the same time, present the augmented image 402 of the knee of the patient and portions of the patient’s leg extending from the knee and a video of the patient’s upper body (e.g., face), so the healthcare provider can engage in more personal communication with the patient (e.g., via a video call). The video may be of the patient’s full body, such that, during the telemedicine session, the healthcare provider may view the patient’s entire body. The augmented image 402 can be displayed next to the video and/or overlaid onto the respective one or more body parts of the patient. For example, the augmented image 402 may comprise a representation of the treatment device 106 coupled to the patient’s knee and leg portions. The clinical portal 134 may display the representation of the treatment device 106 overlaid onto the respective one or more body parts of the patient. Real-time may refer to less than 2 seconds, or any other suitable amount of time. Near real-time may refer to 2 or more seconds. The video may also be accompanied by audio, text, and other multimedia information. The master display 136 may also be configured to present the augmented image and/or the video as described herein. [0206] Presenting the remote examination generated by the artificial intelligence engine concurrently with a presentation of the patient video may provide an enhanced user interface because the healthcare provider, while reviewing the examination on the same user interface, may also continue to visually and/or otherwise communicate with the patient. The enhanced user interface may improve the healthcare provider’s experience in using the computing device and may encourage the healthcare provider to reuse the user interface. Such a technique may also reduce computing resources (e.g., processing, memory, network), because the healthcare provider does not have to switch to another user interface screen and, using the characteristics of the patient, enter a query for examination guidelines to recommend. For example, the enhanced user interface may provide the healthcare provider with recommended procedures to conduct during the telemedicine session. The recommended procedures may comprise a guide map, including indicators of locations and measured amounts of pressure to apply on the patient’s one or more body parts. The artificial intelligence engine may analyze the examination results (e.g., measured levels of force exerted to and by the patient’s one or more body parts, the temperature of the patient, the pain level of the patient, a measured range of motion of the one or more body parts, etc.) and provide, dynamically on the fly, the optimal examination procedures and excluded examination procedures.

[0207] The clinical portal 134 may also provide examination information generated during the telemedicine session for the healthcare provider to view. The examination information can include a summary of the examination and/or the results of the examination in real-time or near real-time, such as measured properties of the patient during the examination. Examples of the measured properties may include, but are not limited to, angles of bend/extension, pressure exerted on the master device 126, pressure exerted by the patient on the treatment device 106, images of the examined/treated body part, and vital signs of the patient, such as heart rate and temperature. The clinical portal 134 may also provide the clinician’s notes and the patient’s health information, such as a health history, a treatment plan, and a progress of the patient throughout the treatment plan. So the healthcare provider may begin the remote examination, the examination information specific to the patient may be transmitted via the network 104 to the cloud-based computing system 142 for storage and/or to the master computing device 122.

[0208] In some embodiments, the clinical portal 134 may include a treatment plan that includes one or more examination procedures (e.g., manipulation instructions to manipulate one or more sections 210 of the treatment device 106). For example, a healthcare provider may input, to the clinical portal 134, a treatment plan with pre-determined manipulation instructions for the treatment device 106 to perform during the remote examination. The healthcare provider may input the pre-determined manipulation instructions prior the remote examination. The treatment device 106 can be activated to perform the manipulations in accordance with the pre-determined manipulation instructions. The healthcare provider may observe the remote examination in real time and make modifications to the pre-determined manipulation instructions during the remote examination. Additionally, the system 100 can store the results of the examination and the healthcare provider can complete the examination using the stored results (e.g., stored slave sensor data) and the master device 126. In other words, the master processing device can use the slave sensor data to manipulate the master device 126. This manipulation of the master device 126 can allow the healthcare provider to virtually feel the patient’s one or more body parts and provide the healthcare provider with additional information to determine a personalized treatment plan for the patient. [0209] The master device 126 may be an examination device configured for control by a healthcare provider. The master device 126 may be a joystick, a model treatment device (e.g., a knee brace to fit over a manikin knee), an examination device to fit over a body part of the healthcare provider (e.g., a glove device), any other suitable device, or combination thereof. The joystick may be configured to be used by a healthcare provider to provide manipulation instructions. The joystick may have one or more buttons (e.g., a trigger) to apply more or less pressure to one or more sections of the treatment device 106. The joystick may be configured to control a moveable indicator (e.g., a cursor) displayed at the master display or any other suitable display. The moveable indicator can be moved over an augmented image 400 of the treatment device 106 and/or one or more body parts of the patient. The healthcare provider may be able to provide verbal commands to increase and/or decrease pressure based on where the moveable indicator is positioned relative to the augmented image 400. The joystick may have master sensors 128 within a stick of the joystick. The stick may be configured to provide feedback to the user (e.g., vibrations or pressure exerted by the stick to the user’s hand).

[0210] The model of the treatment device may be formed similarly to the treatment device 106. For example, if the treatment device 106 is the knee brace 202, the master device can be a model knee brace with similar characteristics of the knee brace 202. The model can be configured for coupling to a manikin or any other suitable device. The model can comprise the master pressure system 130 and master sensors 128 and function as described in this disclosure. The model may be configured for a healthcare provider to manipulate (e.g., touch, move, and/or apply pressure) to one or more sections of the model and to generate master sensor data based on such manipulations. The model canbe operatively coupled to the treatment device 106. The master sensor data can be used to inflate and/or deflate one or more corresponding sections of the treatment device 106 (e.g., as the healthcare provider is manipulating the model, the treatment device 106 is being manipulated on the patient). Responsive to receiving the slave sensor data, the master pressure system 130 can active and inflate and/or deflate one or more sections of the model (e.g., the pressure applied to the treatment device 106 by the patient’s one or more body parts is similarly applied to the model for the healthcare provider to examine). The healthcare provider can essentially feel, with his or her bare (or appropriately gloved) hands, the patient’s one or more body parts (e.g., the knee) while the healthcare provider virtually manipulates the patient body part(s). [0211] In some embodiments, the system 100 may include one or more master computing devices 122 and one or more master consoles 124. For example, a second master console can include a second master device 126 operatively coupled to a second master computing device. The second master device can comprise a second master pressure system 130, and, using the slave force measurements, the one or more processing devices of system 100 can be configured to activate the second master pressure system 130. During and/or after a telemedicine session, one or more healthcare providers can manipulate the treatment device 106 and/or use the slave sensor data to virtually feel the one or more body parts of the patient. For example, a physician and a physical therapist may virtually feel the one or more body parts of the patient at the same time or at different times. The physician may provide the manipulation instructions and the physical therapist may observe (e.g., virtually see and/or feel) how the patient’s one or more body parts respond to the manipulations. The physician and the physical therapist may use different examination techniques (e.g., locations of the manipulations and/or measure levels of force applied to the treatment device 106) to obtain information for providing a treatment plan for the patient. Resulting from the physician using the master device 106 and the physical therapist using the second master device, each can provide manipulation instructions to the treatment device 106. The manipulation instructions from the master device 106 and the second master device may be provided at the same time or at a different time (e.g., the physician provides a first manipulation instruction via the master device 126 and the physical therapist provides a second manipulation instruction via the second master device). In another example, the physician may have input a pre-determined manipulation instruction for the remote examination and the physical therapist may use the second master device to adjust the pre-determined manipulation instructions. The physician and the physical therapist may be located remotely from each other (and remotely from the patient) and each can use the system 100 to examine the patient and provide a personalized treatment plan for the patient. The system 100 can allow for collaboration between one or more healthcare providers and provide the healthcare providers with information to make optimal adjustments to the patient’s treatment plan.

[0212] As illustrated in FIG. 3, the master device 126 comprises a glove device 300 configured to fit on a healthcare provider’s hand. The glove device 300 can include fingers 302. The glove may include one or more sensors (e.g., one or more master sensors 128). The glove device 300 may include the master sensors 128 positioned along the fingers 302, 304, 306, 308, 310 (collectively, fingers 302), throughout the palm of the glove, in any other suitable location, or in any combination thereof. For example, each finger can include a series of master sensors 128 positioned along the fingers 302. Each of the series of master sensors 128 can be operatively coupled to one or more master controllers 138. The master device 126 may include at least one or more master controllers 138 and one or more master motors 132, such as an electric motor (not illustrated). [0213] A pump (not illustrated) may be operatively coupled to the motor. The pump may be configured to increase or decrease pressure within the master device 126. The master device 126 may include one or more sections and the pump can be activated to increase or decrease pressure (e.g., inflating or deflating fluid, such as water, gel, air) in the one or more sections (e.g., one or more fingertips). One or more of the sections may include a master sensor 128. The master sensor 128 can be a sensor for detecting signals, such as pressure, or any other suitable signal. The master controller 138 may be operatively coupled to the master motor 132 and configured to provide commands to the master motor 132 to control operation of the master motor 132. The master controller 138 may include any suitable microcontroller including a circuit board having one or more processing devices, one or more memory devices (e.g., read-only memory (ROM) and/or random access memory (RAM)), one or more network interface cards, and/or programmable input/output peripherals. The master controller 138 may provide control signals or commands to drive the master motor 132. The master motor 132 may be powered to drive the pump of the master device 126. The master motor 132 may provide the driving force to the pump to increase or decrease pressure at configurable speeds. Further, the master device 126 may include a current shunt to provide resistance to dissipate energy from the master motor 132. In some embodiments, the treatment device 106 may comprise a haptic system, a pneumatic system, any other suitable system, or combination thereof. For example, the haptic system can include a virtual touch by applying forces, vibrations, or motions to the healthcare provider through the master device 126.

[0214] The master computing device 122 may be communicatively connected to the master device 126 via a network interface card on the master controller 138. The master computing device 122 may transmit commands to the master controller 138 to control the master motor 132. The network interface card of the master controller 138 may receive the commands and transmit the commands to the master controller 138 to drive the master motor 132. In this way, the master computing device 122 is operatively coupled to the master motor 132. [0215] The master computing device 122 and/or the master controller 138 may be referred to as a control system (e.g., a master control system) herein. The clinical portal 134 may be referred to as a clinical user interface of the control system. The master control system may control the master motor 132 to operate in a number of modes, including: standby, inflate, and deflate. The standby mode may refer to the master motor 132 powering off so that it does not provide any driving force to the one or more pumps. For example, when the healthcare provider is not touching an augmented image of the treatment device 106, the pump of the master device 126 may not receive instructions to inflate or deflate one or more sections of the master device 126 and the master motor 132 may remain turned off. In the standby mode, the master device 126 may not apply pressure to the healthcare provider’s body part(s) (e.g., to the healthcare provider’s finger 304 via the glove device 300) because the healthcare provider is not in virtual contact with the treatment device 106. Furthermore, in standby mode, the master device 126 may not transmit the master sensor data based on manipulations of the master device 126 (e.g., pressure virtually exerted from the healthcare care provider’s hand to the master device 126) to the patient via the treatment device 106.

[0216] The inflate mode may refer to the master motor 132 receiving slave sensor data comprising measurements of pressure, causing the master motor 132 to drive the one or more pumps coupled to the one or more sections of the master device 126 (e.g., one or more fingers 302, 304, 406, 308, 310) to inflate the one or more sections. The slave sensor data may be provided by the one or more slave sensors 108 of the treatment device 106 via the slave computing device 102. For example, as the healthcare provider manipulates (e.g., moves) the master device 126 to virtually contact one or more body parts of the patient using the treatment device 106 in contact with the patient’s one or more body parts, the treatment device 106 is manipulated. The slave sensors 108 are configured to detect the manipulation of the treatment device 106. The detected information may include how the patient’ s one or more body parts respond to the manipulation. The one or more slave sensors 108 may detect that one area of the patient’s body part exerts a first measured level of force and that another area of the patient’s body part exerts a second measured level of force (e.g., the one area may be swollen or inconsistent with baseline measurements or expectations as compared to the other area). The master computing device 122 can receive the information from the slave sensor data and instruct the master motor 132 to drive the pump to inflate one or more sections of the master device 126. The level of inflation of the one or more sections of the master device 126 may correlate with one or more measured levels of force detected by the treatment device 106. The slave sensor data may include a pressure gradient. The master computing device 122 may instruct the master pressure system 130 to inflate a first section (e.g., the fingertips of the first finger 302) associated with the first measured level of force exerted from a left side of the knee brace 202. The master computing device 122 may instruct the master pressure system 130 to inflate second and third sections (e.g., the fingertips of second and third fingers 304, 306) associated with second and third measured levels of force exerted from a front side of the knee brace 202. In other words, in response to the master device 126 virtually touching the treatment device 106, the first measured level of force may correlate with the amount of pressure applied to the healthcare provider’s first finger through the first finger 302 of the master device 126. Similarly, the second measured level of force may correlate with the amount of measured force applied by the healthcare provider’s second finger through the second finger 304 of the master device 126. The third measured level of force may correlate with the amount of measured force applied by the healthcare provider’ s third finger through the third finger 306 of the master device 126. The glove device 300 can include a fourth finger 308 to provide a fourth measured level of force, a fifth finger 310 to provide a fifth measured level of force, and/or other sections, such as a palm, or any combination thereof configured to provide measured levels of force to the healthcare provider. The sections of the glove device 300 can be inflated or deflated to correlate with the same and/or different levels of measured force exerted on the treatment device 106.

[0217] The deflation mode may refer to the master motor 132 receiving slave sensor data comprising measurements of pressure, causing the master motor 132 to drive the one or more pumps coupled to the one or more sections of the master device 126 (e.g., one or more fingers 302) to deflate the one or more sections. The deflation mode of the master pressure system 130 can function similarly as the inflation mode; however, in the deflation mode, the master pressure system 130 deflates, rather than inflates, the one or more sections of the master device 126. For example, the one or more slave sensors 108 may detect that one area of the patient’s body part exerts a first measured level of force and that another area of the patient’s body part exerts a second measured level of force (e.g., the one area may be less swollen or less inconsistent with baseline measurements or expectations as compared to the other area). The master computing device 122 can receive the information from the slave sensor data and instruct the master motor 132 to drive the pump to deflate one or more sections of the master device 126. The level of deflation of the one or more sections of the master device 126 may correlate with one or more measured levels of force detected by the treatment device 106.

[0218] The measured levels of force can be transmitted between the treatment device 106 and the master device 126 in real-time, near real-time, and/or at a later time. In other words, the healthcare provider can use the master device 126 to virtually examine the patient’s body part using the healthcare provider’s hand and feel the patient’s body part (e.g., the pressure, etc.). Similarly, the patient can feel the healthcare provider virtually touching his or her body part (e.g., from the pressure exerted by the treatment device 106). During the telemedicine session, the patient, via the patient portal 114, can communicate to the healthcare provider via the clinical portal 134,. For example, during the remote examination, the patient can inform the healthcare provider that the location of the body part that the healthcare provider is virtually touching (e.g., manipulating), is painful. The information can be communicated verbally and/or visually (e.g., input into the patient portal 114 directly by the client and transmitted to the clinical portal 134 and/or the master display 136). The healthcare provider can receive additional information, such as temperature of the patient’s body part, vital signs of the patient, any other suitable information, or any combination thereof.

[0219] During one or more of the inflation and deflation modes, the one or more master sensors 128 may measure force (i.e., pressure) exerted by the healthcare provider via the master device 126. For example, one or more sections of the master device 126 may contain any suitable sensor (e.g., strain gauge load cell, piezoelectric crystal, hydraulic load cell, etc.) for measuring force exerted on the master device 126. Further, each section 310 of the master device 126 may contain any suitable sensor for detecting whether the body part of the healthcare provider separates from contact with the master device 126. The measured level(s) of force detected may be transmitted via the network interface card of the master device 126 to the control system (e.g., master computing device 122 and/or the master controller 138). As described further below, using the measured level(s) of force, the control system may modify a parameter of operating the master motor 132. Further, the control system may perform one or more preventative actions (e.g., locking the master motor 132 to stop the pump from activating, slowing down the master motor 132, or presenting a notification to the healthcare provider (such as via the clinical portal 134, etc.)) when the body part is detected as being separated from the master device 126, among other things.

[0220] In some embodiments, the remote examination system 100 includes the master display 136. The master console 124 and/or the clinical portal 134 may comprise the master display 136. The master display 136 may be configured to display the treatment device 106 and/or one or more body parts of a patient. For example, the slave computing device 102 may be operatively coupled to an imaging device 116 (e.g., a camera or any other suitable audiovisual device) and/or other sensorial or perceptive (e.g., tactile, gustatory, haptic, pressure sensing-based or electromagnetic (e.g., neurostimulation) communication devices. Any reference herein to any particular sensorial modality shall be understood to include and to disclose by implication a different one or more sensory modalities. The slave computing device 102 can transmit, via the network 104, real images and/or a real live-streaming video of the treatment device 106 and/or the patient, to the master display 136. The real images and/or real video may include angles of extension and/or bend of body parts of the patient, or any other suitable characteristics of the patient. The treatment device 106 may be operatively coupled to a medical device, such as a goniometer 702. The goniometer 702 may detect angles of extension and/or bend of body parts of the patient and transmit the measured angles to the slave computing device 102 and/or the treatment device 106. The slave computing device 102 can transmit the measured angles to the master computing device 122, to the master display 136, or any other suitable device. The master display 136 can display the measured angles in numerical format, as an overlay image on the image of the treatment device 106 and/or the patient’ s one or more body parts, any other suitable format, or combination thereof. For example, as illustrated in FIG. 4 A, body parts (e.g., a leg and a knee) are extended at a first angle. In FIG. 4B, the body parts are illustrated as being extended at a second angle. The master display 136 may be included in an electronic device that includes the one or more processing devices, memory devices, and/or network interface cards.

[0221] Depending on what result is desired, the master computing device 122 and/or a training engine 146 may be trained to output a guide map. The guide map may be overlaid on the augmented image 400. The guide map may include one or more indicators. To guide the master device 126, the indicators can be positioned over one or more sections 310 of the augmented image 400 of the treatment device 106. For example, the augmented image 402 may include a first indicator (e.g., dotted lines in the shape of a fingertip) positioned over a top portion of patient’s knee and a second indicator positioned over a left side of the patient’s knee. The first indicator is a guide for the healthcare provider to place the first finger 302 on the first indicator and the second finger 304 on the second indicator. The guide map may comprise a pressure gradient map. The pressure gradient map can include the current measured levels of force at the location of the indicator and/or a desired measured level of force at the location of the indicator. For example, the first indicator may comprise a first color, a first size, or any other suitable characteristic to indicate a first measured level of force. The second indicator may comprise a second color, a second size, or any other suitable characteristic to indicate a second measured level of force. When the master device 126 reaches the desired measured levels of force, an alert may be provided. The alert may be a visual, audio and/or another alert. For example, the alert may comprise the indicator changing colors when the measured level of force is provided. The guide map may include one or more configurations using characteristics of the injury, the patient, the treatment plan, the recovery results, the examination results, any other suitable factors, or combination thereof. One or more configurations may be displayed during the remote examination portion of a telemedicine session. [0222] The master computing device 122 and/or the training engine 146 may include one or more thresholds, such as pressure thresholds. The one or more pressure thresholds may be based on characteristics of the injury, the patient, the treatment plan, the recovery results, the examination results, the pain level, any other suitable factors, or combination thereof. For example, one pressure threshold pertaining to the pain level of the patient may include a pressure threshold level for the slave pressure system 110 not to inflate a particular section 210 more than a first measured level of force. As the pain level of the patient decreases, the pressure threshold may change such that a second measured level of force may be applied to that particular section 210. In this case, the patient’s decreased pain level may, for more optimal examination results (e.g., the second measured level of force is greater than the first measured level of force), allow for the healthcare provider to increase the measured amount of pressure applied to the patient’s body part. Similarly, the master computing device 122 and/or the training engine 146 may be configured to adjust any pre-determined manipulation instructions. In this way, the manipulation instructions can be adapted to the specific patient.

[0223] In other embodiments, the master display 136 can display an augmented image (e.g., exemplary augmented images 400 illustrated in FIG. 4), an augmented live-streaming video, a holographic image, any other suitable transmission, or any combination thereof of the treatment device 106 and/or one or more body parts of the patient. For example, the master display 136 may project an augmented image 402 representing the treatment device 106 (e.g., a knee brace 202). The augmented image 402 can include a representation 410 of the knee brace 202. The augmented image 402 can include a representation 412 of one or more body parts of a patient. Using the master device 126, the healthcare provider can place a hand on the image and manipulate the image (e.g., apply pressure virtually to one or more sections of the patient’s knee via the treatment device 106. The one or more processing devices may cause a network interface card to transmit the data to the master computing device 122 and the master computing device 122 may use the data representing pressure, temperature, and patterns of movement to track measurements taken by the patient’s recovery over certain time periods (e.g., days, weeks, etc.). In FIG. 4, the augmented images 400 are two dimensional, but the augmented images 400 may be transmitted as three-dimensional images or as any other suitable image dimensionality.

[0224] The master display 136 can be configured to display information obtained from a wearable, such as the wristband 704. The information may include motion measurements of the treatment device 106 in the X, Y, and Z directions, altitude measurements, orientation measurements, rotation measurements, any other suitable measurements, or combination thereof. The wristband 704 may be operatively coupled to an accelerometer, an altimeter, and/or a gyroscope. The accelerometer, the altimeter, and/or the gyroscope may be operatively coupled to a processing device in the wristband 704 and may transmit data to the one or more processing devices. The one or more processing devices may cause a network interface card to transmit the data to the master computing device 122 and the master computing device 122 may use the data representing acceleration, frequency, duration, intensity, and patterns of movement to track measurements taken by the patient over certain time periods (e.g., days, weeks, etc.). Executing the clinical portal 134, the master computing device 122 may transmit the measurements to the master display 136. Additionally, in some embodiments, the processing device of the wristband 704 may determine the measurements taken and transmit the measurements to the slave computing device 102. The measurements may be displayed on the patient portal 114. In some embodiments, the wristband 704 may measure heart rate by using photoplethysmography (PPG), which detects an amount of red light or green light on the skin of the wrist. For example, blood may absorb green light so when the heart beats, the blood volume flow may absorb more green light, thereby enabling heart rate detection. In some embodiments, the wristband 704 may be configured to detect temperature of the patient. The heart rate, temperature, any other suitable measurement, or any combination thereof may be sent to the master computing device 122.

[0225] The master computing device 122 may present the measurements (e.g., pressure or temperature) of the body part of the patient taken by the treatment device 106 and/or the heart rate of the patient via a graphical indicator (e.g., a graphical element) on the clinical portal 134. The measurements may be presented as a gradient map, such as a pressure gradient map or a temperature gradient map. The map may be overlaid over the image of the treatment device 106 and/or the image of the patient’s body part. For example, FIG. 4C illustrates an exemplary augmented image 406 displaying a pressure gradient 414 over the image of the patient’s body parts 412 (e.g., feet). FIG. 4D illustrates an exemplary augmented image 408 displaying a temperature gradient 416 over the image of the patient’s body parts 412 (e.g., feet).

[0226] Referring back to FIG. 1 , the remote examination system 100 may include a cloud-based computing system 142. In some embodiments, the cloud-based computing system 142 may include one or more servers 144 that form a distributed computing architecture. Each of the servers 144 may include one or more processing devices, memory devices, data storage devices, and/or network interface cards. The servers 144 may be in communication with one another via any suitable communication protocol. The servers 144 may store profiles for each of the users (e.g., patients) configured to use the treatment device 106. The profiles may include information about the users such as a treatment plan, the affected body part, any procedure the user had had performed on the affected body part, health, age, race, measured data from the imaging device 116, slave sensor data, measured data from the wristband 704, measured data from the goniometer 702, user input received at the patient portal 114 during the telemedicine session, a level of discomfort the user experienced before and after the remote examination, before and after remote examination images of the affected body part(s), and so forth. [0227] In some embodiments, the cloud-based computing system 142 may include a training engine 146 capable of generating one or more machine learning models 148. The machine learning models 148 may be trained to generate treatment plans, procedures for the remote examination, or any other suitable medical procedure for the patient in response to receiving various inputs (e.g., a procedure via a remote examination performed on the patient, an affected body part the procedure was performed on, other health characteristics (age, race, fitness level, etc.)). The one or more machine learning models 148 may be generated by the training engine 146 and may be implemented in computer instructions executable by one or more processing devices of the training engine 146 and/or the servers 144.

[0228] To generate the one or more machine learning models 148, the training engine 146 may train the one or more machine learning models 148. The training engine 146 may use a base data set of patient characteristics, results of remote examination(s), treatment plans followed by the patient, and results of the treatment plan followed by the patients. The results may include information indicating whether the remote examination led to an identification of the affected body part and whether the identification led to a partial recovery of the affected body part or lack of recovery of the affected body part. The results may include information indicating the measured levels of force applied to the one or more sections of the treatment device 106. [0229] The training engine 146 may be a rackmount server, a router computer, a personal computer, an Internet of Things (IoT) device, a portable digital assistant, a smartphone, a laptop computer, a tablet computer, a camera, a video camera, a netbook, a desktop computer, a media center, any other desired computing device, or any combination of the above. The training engine 146 may be cloud-based or a real-time software platform, and it may include privacy software or protocols, and/or security software or protocols.

[0230] The one or more machine learning models 148 may also be trained to translate characteristics of patients received in real-time (e.g., from an electronic medical records (EMR) system, from the slave sensor data, etc.). The one or more machine learning models 148 may refer to model artifacts that are created by the training engine 146 using training data that includes training inputs and corresponding target outputs. The training engine 146 may find patterns in the training data that map the training input to the target output, and generate the machine learning models 148 that capture these patterns. Although depicted separately from the slave computing device 102, in some embodiments, the training engine 146 and/or the machine learning models 148 may reside on the slave computing device 102 and/or the master computing device 122.

[0231] Different machine learning models 148 may be trained to recommend different optimal examination procedures for different desired results. For example, one machine learning model may be trained to recommend optimal pressure maps for most effective examination of a patient, while another machine learning model may be trained to recommend optimal pressure maps using the current pain level and/or pain level tolerance of a patient.

[0232] The machine learning models 148 may include one or more of a neural network, such as an image classifier, recurrent neural network, convolutional network, generative adversarial network, a fully connected neural network, or some combination thereof, for example. In some embodiments, the machine learning models 148 may be composed of a single level of linear or non-linear operations or may include multiple levels of non-linear operations. For example, the machine learning model may include numerous layers and/or hidden layers that perform calculations (e.g., dot products) using various neurons.

[0233] FIGS. 1-4 are not intended to be limiting: the remote examination system 100 may include more or fewer components than those illustrated in FIGS. 1-4.

[0234] FIG. 5 illustrates a computer-implemented method 500 for remote examination. The method 500 may be performed by the remote examination system 100, such as at a master processing device. The processing device is described in more detail in FIG. 6. The steps of the method 500 may be stored in a non-transient computer-readable storage medium.

[0235] At step 502, the method 500 includes the master processing device receiving slave sensor data from one or more slave sensors 108. The master processing device may receive, via the network 104, the slave sensor data from a slave processing device.

[0236] At step 504, the master processing device can transmit an augmented image 400. The augmented image 400 may be based on the slave sensor data.

[0237] At step 506, the master processing device receives master sensor data associated with a manipulation of the master device 126. For example, the master sensor data may include a measured level of force that the user, such as a healthcare provider, applied to the master device 126. [0238] At step 508, the master processing device can generate a manipulation instruction. The manipulation instruction is based on the master sensor data associated with the manipulation of the master device 126.

[0239] At step 510, the master processing device transmits the manipulation instruction. The master processing device may transmit, via the network 104, the manipulation instruction to the slave computing device 102.

[0240] At step 512, the master processing device causes the slave pressure system to activate. Using the manipulation instruction, the slave computing device 102 can cause the treatment device 106 to activate the slave pressure system 110. For example, responsive to the manipulation instruction (e.g., to increase and/or decrease one or more measured levels of force in one or more sections of the treatment device), the slave pressure system 110 can cause the slave controller 118 to activate the slave motor 112 to inflate and/or deflate the one or more sections 210 to one or more measured levels of force.

[0241] At step 514, the master processing device receives slave force measurements. The slave force measurements can include one or more measurements associated with one or more measured levels of force that the patient’s body is applying to the treatment device 106.

[0242] At step 516, the master processing device uses the pressure slave measurements to activate the master pressure system 130. For example, the master pressure system 130 can cause the master device 126 to inflate and/or deflate one or more sections 310 of the master device 126 such that the measured levels of force of the one or more sections 310 directly correlate with the one or more measured levels of force that the patient’ s body is applying to the one or more sections 210 of the treatment device 106.

[0243] FIG. 6 illustrates a computer-implemented method 600 for remote examination. The method 600 may be performed by the remote examination system 100, such as at a slave processing device. The processing device is described in more detail in FIG. 6. The steps of the method 600 may be stored in a non-transient computer-readable storage medium.

[0244] At step 602, the method 600 includes the slave processing device receiving slave sensor data from one or more slave sensors 108. The one or more slave sensors 108 may include one or more measured levels of force that the patient’s body is applying to the treatment device 106.

[0245] At step 604, the slave processing device transmits the slave sensor data. The slave processing device may transmit, via the network 104, the slave sensor data to the master computing device 122.

[0246] At step 606, the slave processing device may transmit an augmented image 400. The augmented image 400 is based on the slave sensor data. For example, the augmented image 400 may include a representation of the treatment device 106, one or more body parts of the patient, measured levels of force, measured levels of temperature, any other suitable information, or combination thereof.

[0247] At step 608, the slave processing device receives a manipulation instruction. The manipulation instruction can be generated based on the master sensor data.

[0248] At step 610, using the manipulation instruction, the slave processing device activates the slave pressure system 110. For example, the manipulation instruction may cause the slave pressure system 110 to inflate and/or deflate one or more sections 210 of the treatment device 106 to correlate with one or more levels of force applied to one or more sections 310 of the master device 126. [0249] At step 612, the slave processing device receives slave force measurements. The slave force measurements can include one or more measured levels of force exerted by the patient’s body to the treatment device 106.

[0250] At step 614, the slave processing device transmits the slave force measurements, such as to the master processing device.

[0251] At step 616, using the slave force measurements, the slave processing device causes a master pressure system 130 to activate. For example, the master pressure system 130 can cause the master device 126 to inflate and/or deflate one or more sections 310 of the master device 126 such that the measured levels of force of the one or more sections 310 correlate with the one or more measured levels of force that the patient’ s body is applying to the one or more sections 210 of the treatment device 106.

[0252] FIGS. 5-6 are not intended to be limiting: the methods 500, 600 can include more or fewer steps and/or processes than those illustrated in FIGS. 5-6. Further, the order of the steps of the methods 500, 600 is not intended to be limiting; the steps can be arranged in any suitable order. Any or all of the steps of methods 500,600 may be implemented during a telemedicine session or at any other desired time.

[0253] FIG. 7 illustrates a high-level component diagram of an illustrative architecture of system 700 for enabling remote adjustment of a device, such as during a telemedicine session, according to certain aspects of this disclosure. The system 700 may include one or more components of FIG. 1 that have been described above. Any component or combination of the components illustrated in the system 700 may be included in and/or used in connection with the examination system 100. The system 100 and/or the system 700 is not limited to use in the medical field.

[0254] In some embodiments, the system 700 may include a slave computing device 102 communicatively coupled to a treatment device 800, such as an electromechanical device 802, a goniometer 702, a wristband 810, and/or pedals 810 of the electromechanical device 802. Each of the computing device 102, the electromechanical device 802, the goniometer 702, the wristband 810, and the pedals 810 may include one or more processing devices, memory devices, and network interface cards. The network interface cards may enable communication via a wireless protocol for transmitting data over short distances, such as Bluetooth, ZigBee, etc. In some embodiments, the computing device 102 is communicatively coupled to the electromechanical device 802, goniometer 702, the wristband 810, and/or the pedals 810 via Bluetooth.

[0255] The patient portal 114 may present various screens to a user that enable the user to view a treatment plan, initiate a pedaling session of the treatment plan, control parameters of the electromechanical device 802, view progress of rehabilitation during the pedaling session, and so forth as described in more detail below. The computing device 102 may also include instructions stored on the one or more memory devices that, when executed by the one or more processing devices of the computing device 102, perform operations to control the electromechanical device 802.

[0256] The clinical portal 134 may present various screens to a healthcare provider, such as a physician that enable the physician to create a treatment plan for a patient, view progress of the user throughout the treatment plan, view measured properties (e.g., angles of bend/extension, force exerted on pedals 810, heart rate, steps taken, images of the affected body part) of the user during sessions of the treatment plan, view properties (e.g., modes completed, revolutions per minute, etc.) of the electromechanical device 802 during sessions of the treatment plan. The treatment plan specific to a patient may be transmitted via the network 104 to the cloud- based computing system 142 for storage and/or to the computing device 102 so the patient may begin the treatment plan. The healthcare provider can adjust the treatment plan during a session of the treatment plan in real-time or near real-time. For example, the healthcare provider may be monitoring the patient while the patient is using the electromechanical device 802 and, by using the measured properties, the healthcare provider may adjust the treatment plan and transmit the adjusted treatment plan to control at least one operation of the electromechanical device 802. The treatment plan and/or an adjusted treatment plan can include parameters for operation of the electromechanical device 802. If the patient is operating the electromechanical device 802 such that the operations are not within the parameters, a trigger condition may occur, and may be detected or enabled to be detected. In any of the forgoing cases, the one or more processors can control at least one operation of the electromechanical device 102. The automated control can function as a safety feature for the patient as the control mitigates the patient’s risk of further injury.

[0257] The electromechanical device 802 may be an adjustable pedaling device for exercising, strengthening, and rehabilitating arms and/or legs of a user. The electromechanical device 802 may include at least one or more motor controllers 804, one or more electric motors 806, and one or more radially-adjustable couplings 808. Two pedals 810 may be coupled to two radially-adjustable couplings 808 via left and right pedal assemblies that each include respective stepper motors. The motor controller 804 may be operatively coupled to the electric motor 806 and configured to provide commands to the electric motor 806 to control operation of the electric motor 806. The motor controller 804 may include any suitable microcontroller including a circuit board having one or more processing devices, one or more memory devices (e.g., read-only memory (ROM) and/or random access memory (RAM)), one or more network interface cards, and/or programmable input/output peripherals. The motor controller 804 may provide control signals or commands to drive the electric motor 806. The electric motor 806 may be powered to drive one or more radially-adjustable couplings 808 of the electromechanical device 802 in a rotational manner. The electric motor 806 may provide the driving force to rotate the radially-adjustable couplings 808 at configurable speeds. The couplings 808 are radially-adjustable in that a pedal 810 attached to the coupling 808 may be adjusted to a number of positions on the coupling 808 in a radial fashion. Further, the electromechanical device 802 may include current shunt to provide resistance to dissipate energy from the electric motor 806. As such, the electric motor 806 may be configured to provide resistance to rotation of the radially-adjustable couplings 808.

[0258] The computing device 102 may be communicatively connected to the electromechanical device 802 via the network interface card on the motor controller 804. The computing device 102 may transmit commands to the motor controller 804 to control the electric motor 806. The network interface card of the motor controller 804 may receive the commands and transmit the commands to the electric motor 806 to drive the electric motor 806. In this way, the computing device 102 is operatively coupled to the electric motor 806. [0259] The computing device 102 and/or the motor controller 804 may be referred to as a control system herein. The patient portal 114 may be referred to as a user interface of the control system herein. The control system may control the electric motor 806 to operate in a number of modes: passive, active-assisted, resistive, and active. The passive mode may refer to the electric motor 806 independently driving the one or more radially- adjustable couplings 808 rotationally coupled to the one or more pedals 810. In the passive mode, the electric motor 806 may be the only source of driving force on the radially-adjustable couplings. That is, the user may engage the pedals 810 with their hands or their feet and the electric motor 806 may rotate the radially -adjustable couplings 808 for the user. This may enable moving the affected body part and stretching the affected body part without the user exerting excessive force.

[0260] The active-assisted mode may refer to the electric motor 806 receiving measurements of revolutions per minute of the one or more radially -adjustable couplings 808, and causing the electric motor 806 to drive the one or more radially-adjustable couplings 808 rotationally coupled to the one or more pedals 810 when the measured revolutions per minute satisfy a parameter (e.g., a threshold condition). The threshold condition may be configurable by the user and/or the physician. The electric motor 806 may be powered off while the user provides the driving force to the radially-adjustable couplings 808 as long as the revolutions per minute are above a revolutions per minute threshold and the threshold condition is not satisfied. When the revolutions per minute are less than the revolutions per minute threshold then the threshold condition is satisfied and the electric motor 806 may be controlled to drive the radially-adjustable couplings 808 to maintain the revolutions per minute threshold.

[0261] The resistive mode may refer to the electric motor 806 providing resistance to rotation of the one or more radially-adjustable couplings 808 coupled to the one or more pedals 810. The resistive mode may increase the strength of the body part being rehabilitated by causing the muscle to exert force to move the pedals against the resistance provided by the electric motor 806.

[0262] The active mode may refer to the electric motor 806 powering off to provide no driving force assistance to the radially-adjustable couplings 808. Instead, in this mode, the user provides the sole driving force of the radially-adjustable couplings using their hands or feet, for example.

[0263] During one or more of the modes, each of the pedals 810 may measure force exerted by a part of the body of the user on the pedal 810. For example, the pedals 810 may each contain any suitable sensor (e.g., strain gauge load cell, piezoelectric crystal, hydraulic load cell, etc.) for measuring force exerted on the pedal 810. Further, the pedals 810 may each contain any suitable sensor for detecting whether the body part of the user separates from contact with the pedals 810. In some embodiments, the measured force may be used to detect whether the body part has separated from the pedals 810. The force detected may be transmitted via the network interface card of the pedal 810 to the control system (e.g., computing device 102 and/or motor controller 804). As described further below, the control system may modify a parameter of operating the electric motor 806 based on the measured force. Further, the control system may perform one or more preventative actions (e.g., locking the electric motor 120 to stop the radially-adjustable couplings 808 from moving, slowing down the electric motor 806, presenting a notification to the user, etc.) when the body part is detected as separated from the pedals 810, among other things.

[0264] The goniometer 702 may be configured to measure angles of extension and/or bend of body parts and transmit the measured angles to the computing device 102 and/or the computing device 134. The goniometer 702 may be included in an electronic device that includes the one or more processing devices, memory devices, and/or network interface cards. The goniometer 702 may be disposed in a cavity of a mechanical brace. The cavity of the mechanical brace may be located near a center of the mechanical brace where the mechanical brace affords to bend and extend. The mechanical brace may be configured to secure to an upper body part (e.g., arm, etc.) and a lower body part (e.g., leg, etc.) to measure the angles of bend as the body parts are extended away from one another or retracted closer to one another. [0265] The wristband 810 may include a 3 -axis accelerometer to track motion in the X, Y, and Z directions, an altimeter for measuring altitude, and/or a gyroscope to measure orientation and rotation. The accelerometer, altimeter, and/or gyroscope may be operatively coupled to a processing device in the wristband 810 and may transmit data to the processing device. The processing device may cause a network interface card to transmit the data to the computing device 102 and the computing device 102 may use the data representing acceleration, frequency, duration, intensity, and patterns of movement to track steps taken by the user over certain time periods (e.g., days, weeks, etc.). The computing device 102 may transmit the steps to the master computing device 134 executing a clinical portal 134. Additionally, in some embodiments, the processing device of the wristband 810 may determine the steps taken and transmit the steps to the computing device 102. In some embodiments, the wristband 810 may use photoplethysmography (PPG) to measure heart rate that detects an amount of red light or green light on the skin of the wrist. For example, blood may absorb green light so when the heart beats, the blood flow may absorb more green light, thereby enabling detecting heart rate. The heart rate may be sent to the computing device 102 and/or the computing device 134.

[0266] The computing device 102 may present the steps taken by the user and/or the heart rate via respective graphical element on the patient portal 114, as discussed further below. The computing device may also use the steps taken and/or the heart rate to control a parameter of operating the electromechanical device 802. For example, if the heart rate exceeds a target heart rate for a pedaling session, the computing device 102 may control the electric motor 806 to reduce resistance being applied to rotation of the radially -adjustable couplings 808. In another example, if the steps taken are below a step threshold for a day, the treatment plan may increase the amount of time for one or more modes in which the user is to operate the electromechanical device 802 to ensure the affected body part is getting sufficient movement.

[0267] In some embodiments, the cloud-based computing system 142 may include one or more servers 144 that form a distributed computing architecture. Each of the servers 144 may include one or more processing devices, memory devices, data storage, and/or network interface cards. The servers 144 may be in communication with one another via any suitable communication protocol. The servers 144 may store profiles for each of the users that use the electromechanical device 802. The profiles may include information about the users such as a treatment plan, the affected body part, any procedure the user had performed on the affected body part, health, age, race, measured data from the goniometer 702, measured data from the wristband 810, measured data from the pedals 810, user input received at the patient portal 114 during operation of any of the modes of the treatment plan, a level of discomfort, comfort, or general patient satisfaction that the user experiences before and after any of the modes, before and after session images of the affected body part, and so forth.

[0268] In some embodiments the cloud-based computing system 142 may include a training engine 130 that is capable of generating one or more machine learning models 132. The one or more machine learning models 132 may be generated by the training engine 130 and may be implemented in computer instructions that are executable by one or more processing device of the training engine 130 and/or the servers 144. To generate the one or more machine learning models 132, the training engine 130 may train the one or more machine learning models 132. The training engine 130 may use a base data set of patient characteristics, treatment plans followed by the patient, and results of the treatment plan followed by the patients. The results may include information indicating whether the treatment plan led to full recovery of the affected body part, partial recovery of the affected body part, or lack of recovery of the affected body part. The one or more machine learning models 132 may refer to model artifacts that are created by the training engine 130 using training data that includes training inputs and corresponding target outputs. The training engine 130 may find patterns in the training data that map the training input to the target output, and generate the machine learning models 132 that capture these patterns. Although depicted separately from the computing device 102, in some embodiments, the training engine 130 and/or the machine learning models 132 may reside on the computing device 102 and/or the computing device 134.

[0269] As illustrated in FIGS. 8 and 11-12, the treatment device 106 may comprise an electromechanical device, such as a physical therapy device. FIG. 8 illustrates a perspective view of an example of a treatment device 800 according to certain aspects of this disclosure. Specifically, the treatment device 800 illustrated is an electromechanical device 802, such as an exercise and rehabilitation device (e.g., a physical therapy device or the like). The electromechanical device 802 is shown having pedal 810 on opposite sides that are adjustably positionable relative to one another on respective radially-adjustable couplings 808. The depicted electromechanical device 802 is configured as a small and portable unit so that it is easily transported to different locations at which rehabilitation or treatment is to be provided, such as at patients’ homes, alternative care facilities, or the like. The patient may sit in a chair proximate the electromechanical device 802 to engage the electromechanical device 802 with the patient’s feet, for example. The electromechanical device 802 includes a rotary device such as radially-adjustable couplings 808 or flywheel or the like rotatably mounted such as by a central hub to a frame or other support. The pedals 810 are configured for interacting with a patient to be rehabilitated and may be configured for use with lower body extremities such as the feet, legs, or upper body extremities, such as the hands, arms, and the like. For example, the pedal 810 may be a bicycle pedal of the type having a foot support rotatably mounted onto an axle with bearings. The axle may or may not have exposed end threads for engaging a mount on the radially-adjustable coupling 808 to locate the pedal on the radially- adjustable coupling 808. The radially-adjustable coupling 808 may include an actuator configured to radially adjust the location of the pedal to various positions on the radially-adjustable coupling 808.

[0270] Alternatively, the radially-adjustable coupling 808 may be configured to have both pedals 810 on opposite sides of a single coupling 808. In some embodiments, as depicted, a pair of radially-adjustable couplings 808 maybe spaced apart from one another but interconnected to the electric motor 806. In the depicted example, the computing device 102 may be mounted on the frame of the electromechanical device 802 and may be detachable and held by the user while the user operates the electromechanical device 802. The computing device 102 may present the patient portal 114 and control the operation of the electric motor 806, as described herein.

[0271] In some embodiments, as described in U.S. Patent No. 10,173,094 (U.S. Appl. No. 15/700/293), which is incorporated by reference herein in its entirety for all purposes, the treatment device 106 may take the form of a traditional exercise/rehabilitation device which is more or less non-portable and remains in a fixed location, such as a rehabilitation clinic or medical practice. The treatment device 106 may include a seat and is less portable than the treatment device 106 shown in FIGURE 8. FIG. 8 is not intended to be limiting: the treatment device 800 may include more or fewer components than those illustrated in FIG. 8.

[0272] FIGS. 11-12 generally illustrate an embodiment of a treatment device, such as a treatment device 10. More specifically, FIG. 11 generally illustrates a treatment device 10 in the form of an electromechanical device, such as a stationaiy cycling machine 14, which may be called a stationary bike, for short. The stationary cycling machine 14 includes a set of pedals 12 each attached to a pedal arm 20 for rotation about an axle 16. In some embodiments, and as generally illustrated in FIG. 11, the pedals 12 are movable on the pedal arm 20 in order to adjust a range of motion used by the patient in pedaling. For example, the pedals being located inwardly toward the axle 16 corresponds to a smaller range of motion than when the pedals are located outwardly away from the axle 16. A pressure sensor 18 is attached to or embedded within one of the pedals 12 for measuring an amount of force applied by the patient on the pedal 102. The pressure sensor 18 may communicate wirelessly to the treatment device 10 and/or to the patient interface 26. FIGS. 11-12 are not intended to be limiting: the treatment device 10 may include more or fewer components than those illustrated in FIGS. 11-12.

[0273] FIG. 13 generally illustrates a person (a patient) usingthe treatment device of FIG. 11, and showing sensors and various data parameters connected to a patient interface 26. The example patient interface 26 is a tablet computer or smartphone, or a phablet, such as an iPad, an iPhone, an Android device, or a Surface tablet, which is held manually by the patient. In some other embodiments, the patient interface 26 may be embedded within or attached to the treatment device 10. FIG. 13 generally illustrates the patient wearing the ambulation sensor 22 on his wrist, with a note showing “STEPS TODAY 1355”, indicating that the ambulation sensor 22 has recorded and transmitted that step count to the patient interface 26. FIG. 13 also generally illustrates the patient wearing the goniometer 24 on his right knee, with a note showing “KNEE ANGLE 72°”, indicating that the goniometer 24 is measuring and transmitting that knee angle to the patient interface 26. FIG. 13 generally illustrates a right side of one of the pedals 12 with a pressure sensor 18 showing “FORCE 12.5 lbs.”, indicating that the right pedal pressure sensor 18 is measuring and transmitting that force measurement to the patient interface 26. FIG. 13 also generally illustrates a left side of one of the pedals 12 with a pressure sensor 18 showing “FORCE 27 lbs.”, indicating that the left pedal pressure sensor 18 is measuring and transmitting that force measurement to the patient interface 26. FIG. 13 also generally illustrates other patient data, such as an indicator of “SESSION TIME 0:04: 13”, indicating that the patient has been using the treatment device 10 for 4 minutes and 13 seconds. This session time may be determined by the patient interface 26 based on information received from the treatment device 10. FIG. 13 also generally illustrates an indicator showing “PAIN LEVEL 3”, Such a pain level may be obtained from the patient in response to a solicitation, such as a question, presented upon the patient interface 26.

[0274] FIG. 9 illustrates a computer-implemented method 900 for enabling a remote adjustment of a device. The device may be a treatment device, such as the treatment device 800, the device 10, or any other desired device. The device may comprise at least one of a physical therapy device (e.g., the rehabilitation device 802), a brace (e.g., the brace 202), a cap (e.g., the cap 204), a mat (e.g., the mat 206), a wrap (e.g., the wrap 208), a treatment device (e.g., the treatment device 10, the treatment device 106, the stationaiy cycling machine 14, or the like), any other suitable device, or combination thereof. The device may be configured to be manipulated by a user while the user performs a treatment plan. The method 900 may be performed at a processing device operatively coupled to the remote examination system 100, the system 800, or any combination thereof. For example, the method may be performed using a patient interface comprising an output device configured to present telemedicine information associated with a telemedicine session. The steps of the method 900 may be stored in a non-transient computer-readable storage medium. [0275] A healthcare provider can use information obtained from an examination of a patient to determine a proper treatment plan for the patient. Using the systems 100, 800, the healthcare provider can conduct a remote physical examination of the one or more body parts of the patient and/or view results of an exercise, rehabilitation, or other session to provide a treatment plan for the patient. For example, the healthcare provider can conduct the remote physical examination during a telemedicine session.

[0276] At step 902, the method 900 includes receiving a treatment plan for a patient. The treatment plan can be received from a clinical portal 134. For example, the healthcare provider may input a treatment plan into the clinical portal 134, which in turn can transmit the treatment plan to the slave computing device 102 and the treatment device 106, 800. For example, the transmission of the treatment plan can be transmitted during a telemedicine session or at another desired time.

[0277] At step 904, the method 900 includes using the treatment plan to generate at least one parameter. The at least one parameter may be generated during a telemedicine session or at another desired time. The treatment plan may include a plan to treat a patient (e.g., prehabilitation, rehabilitation, or the like). The plan may include patient information (e.g., patient health history, characteristics of an injury, etc.), one or more types of exercises, a schedule of when and for how long to perform the exercises, at least one threshold that the patient should meet and/or not exceed, any other suitable information, or combination thereof. The processing device can use the information in the treatment plan to generate the at least one parameter. For example, the at least one parameter may be a measurable threshold or threshold ranges of data to be detected by the sensor(s) relating to the patient (e.g., pain level, vital signs, etc.) or to the operation of the treatment device 106, 800 (e.g., volume of sections 210, revolutions per minute, angle of the pedals 810, etc.). The at least one parameter can be at least one of a force parameter, a resistance parameter, a range of motion parameter, a temperature parameter, a pain level parameter, an exercise session parameter, a vital sign parameter, a time parameter, any other suitable parameter, or combination thereof. In one example, the force parameter may be based on characteristics of the injury, the patient, the treatment plan, the recovery results, the examination results, the pain level, any other suitable factors, or combination thereof. The force parameter may pertain to the pain level of the patient and include a measured level of force for the patient to exert on the pedals 810. The resistance parameter may be a parameter pertaining to a measured amount of resistance that the motor 806 applies to the pedals 810 during a cycling session. The range of motion parameter may be a parameter pertaining to a measured range of motion of a patient’s body part (e.g., a knee). The temperature parameter may be a parameter pertaining to a measured temperature of the patient or the patient’s body part. The pain level parameter may be a parameter pertaining to a level of pain that the patient reports or experiences before, during, or after the patient uses the treatment device 800. The exercise session parameter may be a parameter pertaining to a type of exercise, a number of steps that the patient has taken during the day and/or during an exercise session, or any other suitable exercise information. The exercise session can include a session for any purpose, including rehabilitation, prehabilitation, exercise, strength training, endurance training, any other type of exercise, or combination thereof. The vital sign parameter may be a parameter pertaining to a measurement of the patient’ s heart rate, pulse rate, blood pressure, respiration rate, or any other vital sign. The time parameter may be a parameter pertaining to an amount of time (e.g., minutes) for which the patient should engage in an exercise session, an amount of time (e.g., hours) between exercise sessions, any other suitable time measurements, or combination thereof. [0278] At step 906, the method 900 includes receiving data correlating with at least one operation of the device. The data may be received during a telemedicine session or at another desired time. The device may comprise one or more sensors for detecting data correlating with the at least one operation. Examples of the measured properties may include, but are not limited to, angles of bend/extension, pressure exerted on the device, the speed of rotating the device (e.g., pedaling speed), the amount of resistance (e.g., pedal resistance), the distance the patient has traveled (e.g., cycled, walked, etc.), the number of steps the patient has taken, images of the examined/treated body part, and vital signs of the patient, such as heart rate and temperature. The data can be received from the one or more sensors in real-time or near real-time.

[0279] At step 908, the method 900 includes determining if a trigger condition has occurred. The trigger may be determined during a telemedicine session or at another desired time. A trigger condition is a condition that occurs when at least one of the data, the at least one parameter, a patient input, any other suitable information, or combination thereof is outside of the at least one parameter. Patient input may include a pain level, a pain tolerance, a weight, or any other suitable information from the patient. In one embodiment, the processing device may use the measured heart rate to determine if the heart rate is outside of the vital sign parameter (e.g., above and/or below a heart rate threshold). In another example, the processing device may use the counted number of steps taken to determine if the number of steps taken is outside of the exercise session parameter (e.g., above and/or below a step threshold). If one or more measurements are outside of the respective parameters (e.g., if the patient’s heart rate is above the heart rate threshold, if the number of steps the patient has taken during the day is below the step threshold), a trigger condition has occurred. Patient input may be received during a telemedicine session or at another desired time.

[0280] At step 910, responsive to at least one trigger condition occurring, the method 900 proceeds with controlling at least one operation of the device. The processing device may control the operation of the device (e.g., the treatment device 106, 800). The processing device may control the operation of the device during a telemedicine session or at another desired time. The controlling of the at least one operation of the device can include causing the device to modify at least one of a volume, a pressure, a resistance, an angle, a speed, an angular or rotational velocity, and a time period. The modification may include not just a value but also a constraint, limitation, maximum, minimum, etc. For example, if the heart rate of the patient exceeds a vital sign parameter for a pedaling session, the computing device 102 may control the electric motor 806 to reduce the resistance being applied to the rotation of the radially-adjustable couplings 808. The motor controller 804 may be operatively coupled to the electric motor 806 and configured to provide commands to the electric motor 806 to control operation of the electric motor 806. In another example, if a volume of a section 210 of the treatment device 106 exceeds the volume parameter, the processing device may control the treatment device 106 to deflate the section 210 to a volume within the volume parameter. In this example, if the measured level of volume exceeds the volume parameter, the excess pressure that the treatment device 106 may be exerting on the patient may cause the patient pain or discomfort, and thus, the processing device is configured to adjust the volume (e.g., decrease the volume) to decrease the pressure exerted on the patient.

[0281] At step 912, the method 900 proceeds with transmitting a notification to a clinical portal. The notification may be transmitted during a telemedicine session or at another desired time. The notification may include results of an exercise session, the patient’s recovery results, the vital sign(s), the pain level, input from the patient, any other suitable information, or combination thereof. The notification can be transmitted to the clinical portal 134 in real-time, in near real-time, before or after an exercise session, at any other suitable time, or combination thereof. The notification can assist the healthcare provider in assessing the patient’s treatment plan and making any adjustments to the treatment plan that may optimize the patient’ s treatment (i.e., to decrease the patient’s recovery time; to increase the patient’s strength, range of motion, and flexibility, etc.).

[0282] At step 914, the method 900 proceeds with receiving at least one adjusted parameter. The parameter may be received during a telemedicine session or at another desired time. The healthcare provider may input the at least one adjusted parameter to the clinical portal 134 for transmitting to the patient portal 114, the treatment device 106, 800, the slave computing device 102, or any combination thereof. For example, while using the rehabilitation device 802 over the course of a few days, if the patient is not within the time parameter (e.g., not exercising for a long enough period of time) and if the patient’s pain level exceeds a pain level parameter, the healthcare provider may adjust the time parameter (e.g., to decrease the amount of time for the exercise) and adjust the force parameter (e.g., to increase the level of motor assistance for a cycling exercise). Such adjustments may result in improved patient compliance with the treatment plan and decrease the patient’s recovery time. The at least one adjusted parameter can be received in real-time, in near real-time, prior to an exercise session, at any other suitable time, or any combination thereof. For example, the healthcare provider may be remotely reviewing the notification(s) in real-time or near real-time while a patient is engaging in an exercise session and/or after the patient has finished the exercise session. As an example, the healthcare provider may upload the treatment plan, the adjusted treatment plan, and/or the adjusted parameter one day and the patient may use the device at a later time, such as later in the day, the following morning, the following day, or the following week, etc.

[0283] In another embodiment, the method 900 receives an adjusted treatment plan, such as from the clinical portal 134. The adjusted treatment plan may be received during a telemedicine session or at another desired time. The adjusted treatment plan may include at least some different information from the treatment plan. For example, the doctor may have used the notification, client input, results from the exercise session, any other suitable information, or combination thereof to make a change to the treatment plan. The processing device may use the adjusted treatment plan to generate an adjusted parameter.

[0284] At step 916, the method 900 proceeds with using the at least one adjusted parameter to control the at least one operation of the device. The at least one adjusted parameter may be used to control the at least one operation of the device during a telemedicine session or at another desired time. In one example, if the steps taken by a patient are below an exercise session parameter (e.g., a step threshold for a day), the exercise session parameter may be adjusted to increase the amount of time for one or more modes in which the patient is to operate the electromechanical device 802 to ensure the affected body part is getting sufficient movement. The at least one adjusted parameter can be used in real-time or near real-time to control the at least one operation of the device. For example, if the healthcare provider is remotely observing the patient during the exercise session (e.g., reviewing the results of the exercise session, notifications, etc.) and provides an adjusted parameter while the patient is using the device, the at least one operation of the electromechanical device 802 can be adjusted in real-time or near real-time (e.g., providing motor assist while the patient is cycling). The at least one adjusted parameter can be received prior to the patient operating the device to control the at least one operation of the device at a time subsequent to receiving the at least one adjusted parameter. For example, the healthcare provider may determine that the patient is recovering and adjust one or more parameters (e.g., increase motor resistance on the pedals 810) to increase the intensity of the workout so that the patient can rebuild muscle strength and recover more quickly.

[0285] FIG. 9 is not intended to be limiting: the method 900 can include more or fewer steps and/or processes than those illustrated in FIG. 9. Further, the order of the steps of the method 900 is not intended to be limiting; the steps can be arranged in any suitable order.

[0286] FIG. 10 illustrates, in accordance with one or more aspects of the present disclosure, an example computer system 1000 which can perform any one or more of the methods described herein. The computer system 1000 may correspond to the slave computing device 102 (e.g., a patient’s computing device), the master computing device 122 (e.g., a healthcare provider’s computing device), one or more servers of the cloud-based computing system 142, the training engine 146, the server 144, the slave pressure system 110, the master pressure system 130, the slave controller 118, the master controller 138, the imaging device 116, the master display 136, the treatment device 106, the master device 126, the master console 124, the treatment device 800, the motor controller 804, the electric motor 806, the radially-adjustable couplings 808, the pedals 810, the goniometer 702, and/or the wristband 704 illustrated in FIGS. 1 and/or 7. The computer system 1000 may be capable of executing the patient portal 114 and/or clinical portal 134 of FIGS. 1 and 7. The computer system 1000 may be connected (e.g., networked) to other computer systems in a LAN, an intranet, an extranet, or the Internet. The computer system 1000 may operate in the capacity of a server in a client-server network environment. The computer system may be a personal computer (PC), a tablet computer, a motor controller, a goniometer (e.g., the goniometer 702), a wearable (e.g., the wristband 704), a set-top box (STB), a personal Digital Assistant (PDA), a mobile phone, a camera, a video camera, or any device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that device. Further, while only a single computer system is illustrated, the term “computer” shall also be taken to include any collection of computers that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methods discussed herein.

[0287] The computer system 1000 includes a processing device 1002 (e.g., the slave processing device, the master processing device), a main memory 1004 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM)), a static memory 1006 (e.g., flash memory, static random access memory (SRAM)), and a data storage device 1008, which communicate with each other via a bus 1010.

[0288] The processing device 1002 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device 1002 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing device 1002 may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processing device 1002 is configured to execute instructions for performing any of the operations and steps discussed herein.

[0289] The computer system 1000 may further include a network interface device 1012. The computer system 1000 also may include a video display 1014 (e.g., a liquid crystal display (LCD), a light-emitting diode (LED), an organic light-emitting diode (OLED or Organic LED), or a cathode ray tube (CRT)). The video display 1014 can represent the master display 136 or any other suitable display. The computer system 1000 may include one or more input devices 1016 (e.g., a keyboard, a mouse, the goniometer 702, the wristband 704, the imaging device 116, or any other suitable input). The computer system 1000 may include one or more output devices (e.g., a speaker 1018). In one illustrative example, the video display 1014, the input device(s) 1016, and/or the speaker 1018 may be combined into a single component or device (e.g., an LCD touch screen). [0290] The data storage device 1008 may include a computer-readable medium 1020 on which the instructions 1022 (e.g., implementing the control system, the patient portal 114, the clinical portal 134, and/or any functions performed by any device and/or component depicted in the FIGS and described herein) embodying any one or more of the methodologies or functions described herein are stored. The instructions 1022 may also reside, completely or at least partially, within the main memory 1004 and/or within the processing device 1002 during execution thereof by the computer system 1000. As such, the main memory 1004 and the processing device 1002 also constitute computer-readable media. The instructions 1022 may further be transmitted or received over a network via the network interface device 1012.

[0291] While the computer-readable storage medium 1020 is shown in the illustrative examples to be a single medium, the term “computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer-readable storage medium” shall also be taken to include any medium capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure. The term “computer-readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media.

[0292] In one exemplary embodiment, the computer system 1000 includes the input device 1016 (e.g., the master console 124 comprising the master device 126) and the control system comprising the processing devices 1002 (e.g., the master processing device) operatively coupled to the input device 1016 and the treatment device 106. The system 1000 may comprise one or more memory devices (e.g., main memory 1004, data storage device 1008, etc.) operatively coupled to the processing device 1002. The one or more memory devices can be configured to store instructions 1022. The processing device 1002 can be configured to execute the instructions 1022 to receive the slave sensor data from the one or more slave sensors 108, to use a manipulation of the master device 126 to generate a manipulation instruction, to transmit the manipulation instruction, and to use the manipulation instruction to cause the slave pressure system 110 to activate. The instructions can be executed in real-time or near real-time.

[0293] The processing device 1002 can be further configured to use the slave sensor data to transmit an augmented image 400 to the video display (e.g., the master display 136). The healthcare provider may view the augmented image 400 and/or virtually touch the augmented image using the video display 1014. In other words, the augmented image 400 may comprise a representation of the treatment device 106 and one or more body parts of the patient. The representation may be displayed in 2D, 3D, or any other suitable dimension. As the healthcare provider conducts the remote examination during a telemedicine session, the augmented image 400 may change to reflect the manipulations of the treatment device 106 and/or of any movement of the patient’s one or more body parts. [0294] The augmented image 400 can comprise one or more pressure indicators, temperature indicators, any other suitable indicator, or combination thereof. Each pressure indicator can represent a measured level of force (i.e., based on the slave force measurements). Each temperature indicator can represent a measured level of temperature (i.e., based on the slave temperature measurements). For example, the pressure indicators and/or the temperature indicators may be different colors, each color associated with one of the measured levels of force and temperature, respectively. The indicators may be displayed as a map. The map may be a gradient map displaying the pressure indicators and/or temperature indicators. The map may be overlaid over the augmented image. The map may be transmitted to the clinical portal, the master display, the patient portal, any other suitable display, or combination thereof.

[0295] The processing device 1002 can be further configured to use the slave sensor data (e.g., the slave force measurements) to provide a corresponding level of measured force to the master device 126. In other words, while using the master device 126, the healthcare provider can essentially feel the measured levels of force exerted by the patient’s one or more body parts during the remote examination.

[0296] As the healthcare provider is virtually examining the patient, the processing device 1002 can use the master sensor data to generate and transmit the manipulation instruction (e.g., a measured level of force) to manipulate the treatment device 106. In other words, as the healthcare provider applies more force pressure) to the master device 126, the master sensors 128 can detect the measured level of force and instruct the treatment device 106 to apply a correlated measured level of force. In some embodiments, the measured level of force can be based on a proximity of the master device 126 to the representation. In other words, as the healthcare provider manipulates the master device 126 closer to the representation and/or within the representation of the treatment device 126 and/or the patient’s one or more body parts, the master sensors 128 can detect that the measured force has increased. In some embodiments, the input device 1016 can comprise a pressure gradient. Using the pressure gradient, the processing device 1002 can be configured to cause the slave pressure system 110 to apply one or more measured levels of force to one or more sections 210 of the treatment device 106.

[0297] In another exemplary embodiment, the computer system 1000 may include the input device 1016 (e.g., the treatment device 106) and the control system comprising the processing device 1002 (e.g., the slave processing device) operatively coupled to the input device 1016 and the master device 126. The system 1000 may comprise one or more memory devices (e.g., main memory 1004, data storage device 1008, etc.) operatively coupled to the processing device 1002. The one or more memory devices can be configured to store instructions 1022. The processing device 1002 can be configured to execute the instructions 1022 to receive the slave sensor data from the one or more slave sensors 108, to transmit the slave sensor data, to receive the manipulation instruction, and to use the manipulation instruction to activate the slave pressure system 110. The instructions can be executed in real-time or near real-time.

[0298] Inyet another embodiment, the computer system 1000 may include one or more input devices 1016 (e.g., the master console 124 comprising the master device 126, the treatment device 106, etc.) and the control system comprising one or more processing devices 1002 (e.g., the master processing device, the slave processing device) operatively coupled to the input devices 1016. For example, the master processing device may be operatively coupled to the master console 124 and the slave processing device may be operatively coupled to the treatment device 106. The system 1000 may comprise one or more memory devices (e.g., master memory coupled to the master processing device, slave memory coupled to the slave processing device, etc.) operatively coupled to the one or more processing devices 1002. The one or more memory devices can be configured to store instructions 1022 (e.g., master instructions, slave instructions, etc.). The one or more processing devices 1002 (e.g., the master processing device) can be configured to execute the master instructions 1022 to receive the slave sensor data from the slave processing device, use a manipulation of the master device 126 to generate a manipulation instruction, and transmit the manipulation instruction to the slave processing device. The one or more processing devices 1002 (e.g., the slave processing device) can be configured to execute the slave instructions 1022 to receive the slave sensor data from the one or more slave sensors, to transmit the slave sensor data to the master processing device, to receive the manipulation instruction from the master processing device, and to use the manipulation instruction to activate the slave pressure system. The instructions can be executed in real-time or near real-time.

[0299] In another exemplary embodiment, the computer system 1000 may include the input device 1016 (e.g., the treatment device 800) and the control system comprising the processing device 1002 (e.g., the slave processing device) operatively coupled to the input device 1016 and the master computing device 122. The system 1000 may comprise one or more memory devices (e.g., main memory 1004, data storage device 1008, etc.) operatively coupled to the processing device 1002. The one or more memory devices can be configured to store instructions 1022. The processing device 1002 can be configured to execute the instructions 1022 to receive a treatment plan (e.g., from a clinical portal 134) for a patient and to use the treatment plan to generate at least one parameter. The at least one parameter can be at least one of a force parameter, a resistance parameter, a range of motion parameter, a temperature parameter, a pain level parameter, an exercise session parameter, a vital sign parameter, and a time parameter. Responsive to the at least one trigger condition occurring, the instructions can further cause the processing device 1002 to control at least one operation of the treatment device 800. The controlling of the at least one operation of the device can comprise causing the treatment device 800 to modify at least one of a volume, a pressure, a resistance, an angle, a speed, an angular or rotational velocity, and a time period. The processing device 1002 can be further configured to execute the instructions 1022 to receive the slave sensor data (e.g., data associated with the at least one operation) from the one or more slave sensors 108. To determine the at least one trigger condition, the instructions 1022 can further cause the processing device 1002 to use at least one of the data, the at least one parameter, and a patient input. The instructions 1022 can be executed in real-time or near real-time. For example, a notification can be transmitted to the clinical portal 134 in real-time or near real-time, the at least one adjusted parameter can be received in real-time or near real-time, and, using the at least one adjusted parameter, the at least one operation of the treatment device 800 can be controlled in real-time or near real-time. The instructions 1022 can be executed at any other suitable time. For example, the notification can be transmitted to a clinical portal 134 at a first time, the at least one adjusted parameter can be received by the treatment device 800 at a second time, and, using the at least one adjusted parameter, the at least one operation of the treatment device 800 can be controlled at a third time subsequent to the first and second times (i.e., subsequent to transmitting the notification and receiving the at least one adjusted parameter).

[0300] FIG. 10 is not intended to be limiting: the system 1000 may include more or fewer components than those illustrated in FIG. 10.

[0301] Any of the systems and methods described in this disclosure may be used in connection with rehabilitation. Unless expressly stated otherwise, is to be understood that rehabilitation includes prehabilitation (also referred to as "pre-habilitation" or "prehab"). Prehabilitation may be used as a preventative procedure or as a pre-surgical or pre-treatment procedure. Prehabilitation may include any action performed by or on a patient (or directed to be performed by or on a patient, including, without limitation, remotely or distally through telemedicine) to, without limitation, prevent or reduce a likelihood of injury (e.g., prior to the occurrence of the injury); improve recovery time subsequent to surgery; improve strength subsequent to surgery; or any of the foregoing with respect to any non-surgical clinical treatment plan to be undertaken for the purpose of ameliorating or mitigating injury, dysfunction, or other negative consequence of surgical or non-surgical treatment on any external or internal part of a patient's body. For example, a mastectomy may require prehabilitation to strengthen muscles or muscle groups affected directly or indirectly by the mastectomy. As a further non-limiting example, the removal of an intestinal tumor, the repair of a hernia, open-heart surgery or other procedures performed on internal organs or structures, whether to repair those organs or structures, to excise them or parts of them, to treat them, etc., can require cutting through and harming numerous muscles and muscle groups in or about, without limitation, the abdomen, the ribs and/or the thoracic cavity. Prehabilitation can improve a patient's speed of recovery, measure of quality of life, level of pain, etc. in all the foregoing procedures. In one embodiment of prehabilitation, a pre-surgical procedure or a pre-non-surgical-treatment may include one or more sets of exercises for a patient to perform prior to such procedure or treatment. The patient may prepare an area of his or her body for the surgical procedure by performing the one or more sets of exercises, thereby strengthening muscle groups, improving existing and/or establishing new muscle memory, enhancing mobility, improving blood flow, and/or the like.

[0302] In some embodiments, the systems and methods described herein may use artificial intelligence and/or machine learning to generate a prehabilitation treatment plan for a user. Additionally, or alternatively, the systems and methods described herein may use artificial intelligence and/or machine learning to recommend an optimal exercise machine configuration for a user. For example, a data model may be trained on historical data such that the data model may be provided with input data relating to the user and may generate output data indicative of a recommended exercise machine configuration for a specific user. Additionally, or alternatively, the systems and methods described herein may use machine learning and/or artificial intelligence to generate other types of recommendations relating to prehabilitation, such as recommended reading material to educate the patient, a recommended health professional specialist to contact, and/or the like.

[0303] Consistent with the above disclosure, the examples of systems and methods enumerated in the following clauses are specifically contemplated and are intended as a non-limiting set of examples.

[0304] Clause 1. A computer-implemented system, comprising: a treatment device configured to be manipulated by a user while the user performs a treatment plan; a patient interface comprising an output device configured to present telemedicine information associated with a telemedicine session; and a processing device configured to: receive a treatment plan for a patient; during the telemedicine session, use the treatment plan to generate at least one parameter; and responsive to at least one trigger condition occurring, control at least one operation of the treatment device. [0305] Clause 2. The computer-implemented system of any clause herein, wherein the treatment device comprises a sensor for detecting data associated with the at least one operation.

[0306] Clause 3. The computer-implemented system of any clause herein, wherein the processing device is configured to receive the data from the sensor in real-time or near real-time.

[0307] Clause 4. The computer-implemented system of any clause herein, wherein, to determine the at least one trigger condition, the one or more processing devices are configured to use at least one of the data, the at least one parameter, and a patient input.

[0308] Clause 5. The computer-implemented system of any clause herein, wherein the controlling of the at least one operation of the device comprises causing the device to modify at least one of a volume, a pressure, a resistance, an angle, a speed, an angular or rotational velocity, and a time period.

[0309] Clause 6. The computer-implemented system of any clause herein, wherein the at least one parameter is at least one of a force parameter, a resistance parameter, a range of motion parameter, a temperature parameter, a pain level parameter, an exercise session parameter, a vital sign parameter, and a time parameter. [0310] Clause 7. A system for a remote examination of a patient, comprising: a master console comprising a master device; a treatment device comprising one or more slave sensors and a slave pressure system; and a control system comprising one or more processing devices operatively coupled to the master console and the treatment device, wherein the one or more processing devices are configured to: receive slave sensor data from the one or more slave sensors; use a manipulation of the master device to generate a manipulation instruction; transmit the manipulation instruction; and use the manipulation instruction to cause the slave pressure system to activate.

[0311] Clause 8. The system of any clause herein, wherein the master device comprises master sensors for detecting master sensor data associated with the manipulation; and wherein the manipulation instruction is based on the master sensor data.

[0312] Clause 9. The system of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the master device comprises a master pressure system; and wherein, using the slave force measurements, the one or more processing devices are further configured to activate the master pressure system.

[0313] Clause 10. The system of any clause herein, further comprising: a second master device comprising a second master pressure system; wherein the slave sensor data comprises slave force measurements; and wherein, using the slave force measurements, the one or more processing devices are further configured to activate the second master pressure system.

[0314] Clause 11. The system of any clause herein, wherein the one or more processing devices are further configured to: use the slave sensor data to transmit an augmented image to a master display. [0315] Clause 12. The system of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the augmented image comprises one or more pressure indicators; and wherein the one or more pressure indicators are based on the slave force measurements.

[0316] Clause 13. The system of any clause herein, wherein the slave sensor data comprises slave temperature measurements; wherein the augmented image comprises one or more temperature indicators; and wherein the one or more temperature indicators are based on the slave temperature measurements. [0317] Clause 14. The system of any clause herein, wherein the master device comprises a pressure gradient; and wherein, using the pressure gradient, the one or more processing devices are configured to cause the slave pressure system to apply one or more measured levels of force to one or more sections of the treatment device.

[0318] Clause 15. The system of any clause herein, wherein the augmented image comprises a representation of at least one of the treatment device and a body part of the patient, and wherein the representation is in 2D or 3D.

[0319] Clause 16. The system of any clause herein, wherein the manipulation instruction comprises a measured level of force; and wherein the measured level of force is based on a proximity of the master device to the representation. [0320] Clause 17. The system of any clause herein, wherein the one or more processing devices are further configured to: transmit the manipulation instruction in real-time or near real-time; and cause the slave pressure system to activate in real-time or near real-time.

[0321] Clause 18. The system of any clause herein, wherein the master device comprises at least one of a glove device, a joystick, and a model of the treatment device.

[0322] Clause 19. The system of any clause herein, wherein the treatment device comprises at least one of a brace, a cap, a mat, and a wrap.

[0323] Clause 20. The system of any clause herein, further comprising one or more memory devices operatively coupled to the one or more processing devices, wherein the one or more memory devices stores instructions, and wherein the one or more processing devices are configured to execute the instructions.

[0324] Clause 21. A method for operating a system for remote examination of a patient, comprising: receiving slave sensor data from one or more slave sensors; based on a manipulation of a master device, generating a manipulation instruction; transmitting the manipulation instruction; and based on the manipulation instruction, causing a slave pressure system to activate.

[0325] Clause 22. The method of any clause herein, wherein the master device comprises master sensors for detecting master sensor data associated with the manipulation; and wherein the manipulation instruction is based on the master sensor data. [0326] Clause 23. The method of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the master device comprises a master pressure system; and wherein, based on the slave force measurements, activating the master pressure system.

[0327] Clause 24. The method of any clause herein, further comprising: a second master device comprising a second master pressure system; wherein the slave sensor data comprises slave force measurements; and wherein, using the slave force measurements, the one or more processing devices are further configured to activate the second master pressure system.

[0328] Clause 25. The method of any clause herein, further comprising: use the slave sensor data to transmitting an augmented image.

[0329] Clause 26. The method of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the augmented image comprises one or more pressure indicators; and wherein the one or more pressure indicators are based on the slave force measurements.

[0330] Clause 27. The method of any clause herein, wherein the slave sensor data comprises slave temperature measurements; wherein the augmented image comprises one or more temperature indicators; and wherein the one or more temperature indicators are based on the slave temperature measurements. [0331] Clause 28. The method of any clause herein, wherein the master device comprises a pressure gradient; and wherein, using the pressure gradient, causing the slave pressure system to apply one or more measured levels of force to one or more sections of the treatment device.

[0332] Clause 29. The method of any clause herein, wherein the augmented image comprises a representation of at least one of the treatment device and a body part of the patient, and wherein the representation is in 2D or 3D.

[0333] Clause 30. The method of any clause herein, wherein the manipulation instruction comprises a measured level of force; and wherein the measured level of force is based on a proximity of the master device to the representation. [0334] Clause 31. The method of any clause herein, further comprising: transmitting the manipulation instruction in real-time or near real-time; and causing the slave pressure system to activate in real-time or near real-time.

[0335] Clause 32. The method of any clause herein, wherein the master device comprises at least one of a glove device, a joystick, and a model of the treatment device.

[0336] Clause 33. The method of any clause herein, wherein the treatment device comprises at least one of a brace, a cap, a mat, and a wrap.

[0337] Clause 34. A tangible, non-transitoiy computer-readable storage medium storing instructions that, when executed, cause a processing device to: receive slave sensor data from one or more slave sensors; based on a manipulation of a master device, generate a manipulation instruction; transmit the manipulation instruction; and use the manipulation instruction to cause a slave pressure system to activate.

[0338] Clause 35. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the master device comprises master sensors for detecting master sensor data associated with the manipulation; and wherein the manipulation instruction is based on the master sensor data.

[0339] Clause 36. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the master device comprises a master pressure system; and wherein, based on the slave force measurements, activate the master pressure system.

[0340] Clause 37. The tangible, non-transitoiy computer-readable storage medium of any clause herein, further comprising: a second master device comprising a second master pressure system; wherein the slave sensor data comprises slave force measurements; and wherein, using the slave force measurements, the one or more processing devices are further configured to activate the second master pressure system.

[0341] Clause 38. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the instructions further cause the processing device to: use the slave sensor data to transmit an augmented image.

[0342] Clause 39. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the augmented image comprises one or more pressure indicators; and wherein the one or more pressure indicators are based on the slave force measurements.

[0343] Clause 40. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the slave sensor data comprises slave temperature measurements; wherein the augmented image comprises one or more temperature indicators; and wherein the one or more temperature indicators are based on the slave temperature measurements. [0344] Clause 41. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the master device comprises a pressure gradient; and wherein, using the pressure gradient, cause the slave pressure system to apply one or more measured levels of force to one or more sections of the treatment device.

[0345] Clause 42. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the augmented image comprises a representation of at least one of the treatment device and a body part of the patient, and wherein the representation is in 2D or 3D.

[0346] Clause 43. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the manipulation instruction comprises a measured level of force; and wherein the measured level of force is based on a proximity of the master device to the representation. [0347] Clause 44. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the instructions further cause the processing device to: transmit the manipulation instruction in real-time or near real-time; and cause the slave pressure system to activate in real-time or near real-time.

[0348] Clause 45. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the master device comprises at least one of a glove device, a joystick, and a model of the treatment device.

[0349] Clause 46. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the treatment device comprises at least one of a brace, a cap, a mat, and a wrap.

[0350] Clause 47. A system for a remote examination of a patient, comprising: a master console comprising a master device; a treatment device comprising one or more slave sensors and a slave pressure system; and a control system comprising one or more processing devices operatively coupled to the master console and the treatment device, wherein the one or more processing devices are configured to: receive slave sensor data from the one or more slave sensors; transmit the slave sensor data; receive a manipulation instruction; and use the manipulation instruction to activate the slave pressure system.

[0351] Clause 48. The system of any clause herein, wherein the manipulation instruction is based on a manipulation of the master device.

[0352] Clause 49. The system of any clause herein, wherein the master device comprises master sensors for detecting master sensor data associated with the manipulation; and wherein the manipulation instruction is based on the master sensor data.

[0353] Clause 50. The system of any clause herein, further comprising: a second master device comprising a second master pressure system; wherein the slave sensor data comprises slave force measurements; and wherein, using the slave force measurements, the one or more processing devices are further configured to activate the second master pressure system.

[0354] Clause 51. The system of any clause herein, wherein the one or more processing devices are further configured to: use the slave sensor data to transmit an augmented image to the master console.

[0355] Clause 52. The system of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the master device comprises a master pressure system; and wherein, using the slave force measurements, the one or more processing devices are further configured to cause the master pressure system to activate.

[0356] Clause 53. The system of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the augmented image comprises one or more pressure indicators; and wherein the one or more pressure indicators are based on the slave force measurements.

[0357] Clause 54. The system of any clause herein, wherein the slave sensor data comprises slave temperature measurements; wherein the augmented image comprises one or more temperature indicators; and wherein the one or more temperature indicators are based on the slave temperature measurements. [0358] Clause 55. The system of any clause herein, wherein the master device comprises a pressure gradient; and wherein, using the pressure gradient, activating the slave pressure system comprises applying one or more measured levels of force to one or more sections of the treatment device.

[0359] Clause 56. The system of any clause herein, wherein the augmented image comprises a representation of at least one of the treatment device and a body part of the patient, and wherein the representation is in 2D or 3D.

[0360] Clause 57. The system of any clause herein, wherein the manipulation instruction comprises a measured level of force; and wherein the measured level of force is based on a proximity of the master device to the representation. [0361] Clause 58. The system of any clause herein, wherein the one or more processing devices are further configured to: receive the manipulation instruction in real-time or near real-time; and activate the slave pressure system in real-time or near real-time.

[0362] Clause 59. The system of any clause herein, wherein the master device comprises at least one of a glove device, a joystick, and a model of the treatment device.

[0363] Clause 60. The system of any clause herein, wherein the treatment device comprises at least one of a brace, a cap, a mat, and a wrap.

[0364] Clause 61. The system of any clause herein, further comprising one or more memory devices operatively coupled to the one or more processing devices, wherein the one or more memory devices stores instructions, and wherein the one or more processing devices are configured to execute the instructions.

[0365] Clause 62. A method for operating a system for remote examination of a patient, comprising: receiving slave sensor data from one or more slave sensors; transmitting the slave sensor data; receiving a manipulation instruction; and based on the manipulation instruction, activating a slave pressure system.

[0366] Clause 63. The method of any clause herein, wherein the manipulation instruction is based on a manipulation of a master device.

[0367] Clause 64. The method of any clause herein, wherein the master device comprises master sensors for detecting master sensor data associated with the manipulation; and wherein the manipulation instruction is based on the master sensor data.

[0368] Clause 65. The method of any clause herein, further comprising: a second master device comprising a second master pressure system; wherein the slave sensor data comprises slave force measurements; and wherein, using the slave force measurements, the one or more processing devices are further configured to activate the second master pressure system.

[0369] Clause 66. The method of any clause herein, further comprising: use the slave sensor data to transmitting an augmented image to the master console.

[0370] Clause 67. The method of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the master device comprises a master pressure system; and wherein, based on the slave force measurements, causing the master pressure system to activate. [0371] Clause 68. The method of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the augmented image comprises one or more pressure indicators; and wherein the one or more pressure indicators are based on the slave force measurements.

[0372] Clause 69. The method of any clause herein, wherein the slave sensor data comprises slave temperature measurements; wherein the augmented image comprises one or more temperature indicators; and wherein the one or more temperature indicators are based on the slave temperature measurements. [0373] Clause 70. The method of any clause herein, wherein the master device comprises a pressure gradient; and wherein, using the pressure gradient, activating the slave pressure system comprises applying one or more measured levels of force to one or more sections of the treatment device.

[0374] Clause 71. The method of any clause herein, wherein the augmented image comprises a representation of at least one of the treatment device and a body part of the patient, and wherein the representation is in 2D or 3D.

[0375] Clause 72. The method of any clause herein, wherein the manipulation instruction comprises a measured level of force; and wherein the measured level of force is based on a proximity of the master device to the representation. [0376] Clause 73. The method of any clause herein, further comprising: receiving the manipulation instruction in real-time or near real-time; and activating the slave pressure system in real-time or near real-time.

[0377] Clause 74. The method of any clause herein, wherein the master device comprises at least one of a glove device, a joystick, and a model of the treatment device.

[0378] Clause 75. The method of any clause herein, wherein the treatment device comprises at least one of a brace, a cap, a mat, and a wrap.

[0379] Clause 76. A tangible, non-transitoiy computer-readable storage medium storing instructions that, when executed, cause a processing device to: receive slave sensor data from one or more slave sensors; transmit the slave sensor data; receive a manipulation instruction; and use the manipulation instruction to activate a slave pressure system. [0380] Clause 77. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the manipulation instruction is based on a manipulation of a master device.

[0381] Clause 78. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the master device comprises master sensors for detecting master sensor data associated with the manipulation; and wherein the manipulation instruction is based on the master sensor data.

[0382] Clause 79. The tangible, non-transitoiy computer-readable storage medium of any clause herein, further comprising: a second master device comprising a second master pressure system; wherein the slave sensor data comprises slave force measurements; and wherein, using the slave force measurements, the one or more processing devices are further configured to activate the second master pressure system.

[0383] Clause 80. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the instructions further cause the processing device to: use the slave sensor data to transmit an augmented image to the master console.

[0384] Clause 81. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the master device comprises a master pressure system; and wherein, based on the slave force measurements, cause the master pressure system to activate.

[0385] Clause 82. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the augmented image comprises one or more pressure indicators; and wherein the one or more pressure indicators are based on the slave force measurements.

[0386] Clause 83. The method of any clause herein, wherein the slave sensor data comprises slave temperature measurements; wherein the augmented image comprises one or more temperature indicators; and wherein the one or more temperature indicators are based on the slave temperature measurements. [0387] Clause 84. The method of any clause herein, wherein the master device comprises a pressure gradient; and wherein, using the pressure gradient, activating the slave pressure system comprises applying one or more measured levels of force to one or more sections of the treatment device.

[0388] Clause 85. The method of any clause herein, wherein the augmented image comprises a representation of at least one of the treatment device and a body part of the patient, and wherein the representation is in 2D or 3D.

[0389] Clause 86. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the manipulation instruction comprises a measured level of force; and wherein the measured level of force is based on a proximity of the master device to the representation. [0390] Clause 87. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the instructions further cause the processing device to: receive the manipulation instruction in real-time or near real-time; and activate the slave pressure system in real-time or near real-time.

[0391] Clause 88. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the master device comprises at least one of a glove device, a joystick, and a model of the treatment device.

[0392] Clause 89. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the treatment device comprises at least one of a brace, a cap, a mat, and a wrap.

[0393] Clause 90. A system for a remote examination of a patient, comprising: a master console comprising a master device; a treatment device comprising one or more slave sensors and a slave pressure system; and a control system comprising a master processing device and a slave processing device, wherein the master processing device is operatively coupled to the master console and the slave processing device is operatively coupled to the treatment device; wherein the master processing device is configured to: receive slave sensor data from the slave processing device; use a manipulation of the master device to generate a manipulation instruction; and transmit the manipulation instruction to the slave processing device; and wherein the slave processing device is configured to: receive the slave sensor data from the one or more slave sensors; transmit the slave sensor data to the master processing device; receive the manipulation instruction from the master processing device; and use the manipulation instruction to activate the slave pressure system.

[0394] Clause 91. The system of any clause herein, wherein the master device comprises master sensors for detecting master sensor data associated with the manipulation; and wherein the manipulation instruction is based on the master sensor data.

[0395] Clause 92. The system of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the master device comprises a master pressure system; and wherein, using the slave force measurements, the master processing device is further configured to activate the master pressure system.

[0396] Clause 93. The system of any clause herein, further comprising: a second master device comprising a second master pressure system; wherein the slave sensor data comprises slave force measurements; and wherein, using the slave force measurements, the master processing device is further configured to activate the second master pressure system.

[0397] Clause 94. The system of any clause herein, wherein the master processing device is further configured to: use the slave sensor data to transmit an augmented image to a master display. [0398] Clause 95. The system of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the augmented image comprises one or more pressure indicators; and wherein the one or more pressure indicators are based on the slave force measurements.

[0399] Clause 96. The system of any clause herein, wherein the slave sensor data comprises slave temperature measurements; wherein the augmented image comprises one or more temperature indicators; and wherein the one or more temperature indicators are based on the slave temperature measurements. [0400] Clause 97. The system of any clause herein, wherein the master device comprises a pressure gradient; and wherein, using the pressure gradient, activating the slave pressure system comprises applying one or more measured levels of force to one or more sections of the treatment device.

[0401] Clause 98. The system of any clause herein, wherein the augmented image comprises a representation of at least one of the treatment device and a body part of the patient, and wherein the representation is in 2D or 3D.

[0402] Clause 99. The system of any clause herein, wherein the manipulation instruction comprises a measured level of force; and wherein the measured level of force is based on a proximity of the master device to the representation. [0403] Clause 100. The system of any clause herein, wherein the manipulation instruction is transmitted in real-time or near real-time; and wherein the slave pressure system is activated in real-time or near real-time.

[0404] Clause 101. The system of any clause herein, wherein the master device comprises at least one of a glove device, a joystick, and a model of the treatment device.

[0405] Clause 102. The system of any clause herein, wherein the treatment device comprises at least one of a brace, a cap, a mat, and a wrap.

[0406] Clause 103. The system of any clause herein, further comprising: a master memory device operatively coupled to the master processing device, wherein the master memory device stores master instructions, and wherein the master processing device is configured to execute the master instructions; and a slave memory device operatively coupled to the slave processing device, wherein the slave memory device stores slave instructions, and wherein the slave processing device is configured to execute the slave ins tractions.

[0407] Clause 104. A method for operating a remote examination of a patient, comprising: causing a master processing device to: receive slave sensor data from the slave processing device; use a manipulation of a master device to generate a manipulation instruction; and transmit the manipulation instruction to the slave processing device; and causing a slave processing device to: receive the slave sensor data from the one or more slave sensors; transmit the slave sensor data to the master processing device; receive the manipulation instruction from the master processing device; and use the manipulation instruction to activate the slave pressure system.

[0408] Clause 105. The method of any clause herein, wherein the master device comprises master sensors for detecting master sensor data associated with the manipulation; and wherein the manipulation instruction is based on the master sensor data.

[0409] Clause 106. The method of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the master device comprises a master pressure system; and causing the master processing device, based on the slave force measurements, to activate the master pressure system.

[0410] Clause 107. The method of any clause herein, further comprising: a second master device comprising a second master pressure system; wherein the slave sensor data comprises slave force measurements; and wherein, using the slave force measurements, the master processing device is further configured to activate the second master pressure system.

[0411] Clause 108. The method of any clause herein, further causing the master processing device to: use the slave sensor data to transmit an augmented image to a master display.

[0412] Clause 109. The method of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the augmented image comprises one or more pressure indicators; and wherein the one or more pressure indicators are based on the slave force measurements.

[0413] Clause 110. The method of any clause herein, wherein the slave sensor data comprises slave temperature measurements; wherein the augmented image comprises one or more temperature indicators; and wherein the one or more temperature indicators are based on the slave temperature measurements. [0414] Clause 111. The method of any clause herein, wherein the master device comprises a pressure gradient; and wherein, using the pressure gradient, activating the slave pressure system comprises applying one or more measured levels of force to one or more sections of the treatment device.

[0415] Clause 112. The method of any clause herein, wherein the augmented image comprises a representation of at least one of the treatment device and a body part of the patient, and wherein the representation is in 2D or 3D.

[0416] Clause 113. The method of any clause herein, wherein the manipulation instruction comprises a measured level of force; and wherein the measured level of force is based on a proximity of the master device to the representation. [0417] Clause 114. The method of any clause herein, wherein the manipulation instruction is transmitted in real-time or near real-time; and wherein the slave pressure system is activated in real-time or near real-time. [0418] Clause 115. The method of any clause herein, wherein the master device comprises at least one of a glove device, a joystick, and a model of the treatment device.

[0419] Clause 116. The method of any clause herein, wherein the treatment device comprises at least one of a brace, a cap, a mat, and a wrap.

[0420] Clause 117. A tangible, non-transitoiy computer-readable storage medium storing instructions that, when executed, cause a master processing device to: receive slave sensor data from the slave processing device; use a manipulation of a master device to generate a manipulation instruction; and transmit the manipulation instruction to the slave processing device; and cause a slave processing device to: receive the slave sensor data from the one or more slave sensors; transmit the slave sensor data to the master processing device; receive the manipulation instruction from the master processing device; and use the manipulation instruction to activate the slave pressure system.

[0421] Clause 118. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the master device comprises master sensors for detecting master sensor data associated with the manipulation; and wherein the manipulation instruction is based on the master sensor data.

[0422] Clause 119. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the master device comprises a master pressure system; and wherein, using the slave force measurements, the master processing device is further configured to activate the master pressure system.

[0423] Clause 120. The tangible, non-transitoiy computer-readable storage medium of any clause herein, further comprising: a second master device comprising a second master pressure system; wherein the slave sensor data comprises slave force measurements; and wherein, using the slave force measurements, the master processing device is further configured to activate the second master pressure system.

[0424] Clause 121. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein instructions further cause the master processing device to: use the slave sensor data to transmit an augmented image to a master display.

[0425] Clause 122. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the augmented image comprises one or more pressure indicators; and wherein the one or more pressure indicators are based on the slave force measurements.

[0426] Clause 123. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the slave sensor data comprises slave temperature measurements; wherein the augmented image comprises one or more temperature indicators; and wherein the one or more temperature indicators are based on the slave temperature measurements. [0427] Clause 124. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the master device comprises a pressure gradient; and wherein, using the pressure gradient, activating the slave pressure system comprises applying one or more measured levels of force to one or more sections of the treatment device.

[0428] Clause 125. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the augmented image comprises a representation of at least one of the treatment device and a body part of the patient, and wherein the representation is in 2D or 3D.

[0429] Clause 126. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the manipulation instruction comprises a measured level of force; and wherein the measured level of force is based on a proximity of the master device to the representation. [0430] Clause 127. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the manipulation instruction is transmitted in real-time or near real-time; and wherein the slave pressure system is activated in real-time or near real-time.

[0431] Clause 128. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the master device comprises at least one of a glove device, a joystick, and a model of the treatment device.

[0432] Clause 129. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the treatment device comprises at least one of a brace, a cap, a mat, and a wrap.

[0433] Clause 130. The tangible, non-transitoiy computer-readable storage medium of any clause herein, further comprising: a master memory device operatively coupled to the master processing device, wherein the master memory device stores master instructions, and wherein the master processing device is configured to execute the master instructions; and a slave memory device operatively coupled to the slave processing device, wherein the slave memory device stores slave instructions, and wherein the slave processing device is configured to execute the slave ins tractions.

[0434] Clause 131. A system for enabling a remote adjustment of a device, comprising: a control system comprising one or more processing devices operatively coupled to the device, wherein the one or more processing devices are configured to: receive a treatment plan for a patient; use the treatment plan to generate at least one parameter; and responsive to at least one trigger condition occurring, control at least one operation of the device. [0435] Clause 132. The system of any clause herein, wherein the device comprises a sensor for detecting data associated with the at least one operation.

[0436] Clause 133. The system of any clause herein, wherein the one or more processing devices are configured to receive the data from the sensor in real-time or near real-time. [0437] Clause 134. The system of any clause herein, wherein, to determine the at least one trigger condition, the one or more processing devices are configured to use at least one of the data, the at least one parameter, and a patient input.

[0438] Clause 135. The system of any clause herein, wherein the controlling of the at least one operation of the device comprises causing the device to modify at least one of a volume, a pressure, a resistance, an angle, a speed, an angular or rotational velocity, and a time period.

[0439] Clause 136. The system of any clause herein, wherein the at least one parameter is at least one of a force parameter, a resistance parameter, a range of motion parameter, a temperature parameter, a pain level parameter, an exercise session parameter, a vital sign parameter, and a time parameter.

[0440] Clause 137. The system of any clause herein, wherein the one or more processing devices are configured to receive the treatment plan from a clinical portal.

[0441] Clause 138. The system of any clause herein, wherein the one or more processing devices are further configured to: transmit a notification to a clinical portal in real-time or near real-time; receive at least one adjusted parameter in real-time or near real-time; and using the at least one adjusted parameter, control the at least one operation of the device in real-time or near real-time.

[0442] Clause 139. The system of any clause herein, wherein the one or more processing devices are further configured to: transmit a notification to a clinical portal; receive at least one adjusted parameter; and using the at least one adjusted parameter, control the at least one operation of the device at a time subsequent to receiving the at least one adjusted parameter.

[0443] Clause 140. The system of any clause herein, wherein the device comprises at least one of a physical therapy device, a brace, a cap, a mat, and a wrap.

[0444] Clause 141. A method for enabling a remote adjustment of a device, comprising: receiving a treatment plan for a patient; using the treatment plan to generate at least one parameter; and responsive to at least one trigger condition occurring, controlling at least one operation of the device. [0445] Clause 142. The method of any clause herein, wherein the device comprises a sensor for detecting data associated with the at least one operation.

[0446] Clause 143. The method of any clause herein, wherein the data is received from the sensor in real time or near real-time.

[0447] Clause 144. The method of any clause herein, further comprising: to determine the at least one trigger condition, using at least one of the data, the at least one parameter, and a patient input.

[0448] Clause 145. The method of any clause herein, wherein the controlling of the at least one operation of the device comprises causing the device to modify at least one of a volume, a pressure, a resistance, an angle, a speed, an angular or rotational velocity, and a time period. [0449] Clause 146. The method of any clause herein, wherein the at least one parameter is at least one of a force parameter, a resistance parameter, a range of motion parameter, a temperature parameter, a pain level parameter, an exercise session parameter, a vital sign parameter, and a time parameter.

[0450] Clause 147. The method of any clause herein, wherein the treatment plan is received from a clinical portal.

[0451] Clause 148. The method of any clause herein, further comprising: transmitting a notification to a clinical portal in real-time or near real-time; receiving at least one adjusted parameter in real-time or near real-time; and using the at least one adjusted parameter to control the at least one operation of the device in real-time or near real-time.

[0452] Clause 149. The method of any clause herein, further comprising: transmitting a notification to a clinical portal; receiving at least one adjusted parameter; and using the at least one adjusted parameter to control the at least one operation of the device at a time subsequent to receiving the at least one adjusted parameter.

[0453] Clause 150. The method of any clause herein, wherein the device comprises at least one of a physical therapy device, a brace, a cap, a mat, and a wrap.

[0454] Clause 151. A tangible, non-transitoiy computer-readable storage medium storing instructions that, when executed, cause a processor to: receive a treatment plan for a patient; use the treatment plan to generate at least one parameter; and responsive to at least one trigger condition occurring, control at least one operation of a device. [0455] Clause 152. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the device comprises a sensor for detecting data associated with the at least one operation.

[0456] Clause 153. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the instructions further cause the processor to receive the data from the sensor in real-time or near real time.

[0457] Clause 154. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein, to determine the at least one trigger condition, the instructions further cause the processor to use at least one of the data, the at least one parameter, and a patient input.

[0458] Clause 155. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the controlling of the at least one operation of the device comprises causing the device to modify at least one of a volume, a pressure, a resistance, an angle, a speed, an angular or rotational velocity, and a time period.

[0459] Clause 156. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the at least one parameter is at least one of a force parameter, a resistance parameter, a range of motion parameter, a temperature parameter, a pain level parameter, an exercise session parameter, a vital sign parameter, and a time parameter. [0460] Clause 157. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the treatment plan is received from a clinical portal.

[0461] Clause 158. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the instructions further cause the processor to: transmit a notification to a clinical portal in real-time or near real-time; receive at least one adjusted parameter in real-time or near real-time; and using the at least one adjusted parameter, control the at least one operation of the device in real-time or near real-time.

[0462] Clause 159. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the instructions further cause the processor to: transmit a notification to a clinical portal; receive at least one adjusted parameter; and using the at least one adjusted parameter, control the at least one operation of the device at a time subsequent to receiving the at least one adjusted parameter.

[0463] Clause 160. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the device comprises at least one of a physical therapy device, a brace, a cap, a mat, and a wrap. [0464] Consistent with the above disclosure, the examples of assemblies enumerated in the following clauses are specifically contemplated and are intended as a non-limiting set of examples.

[0465] No part of the description in this application should be read as implying that any particular element, step, or function is an essential element that must be included in the claim scope. The scope of patented subject matter is defined only by the claims. Moreover, none of the claims is intended to invoke 25 U.S.C. § 104(f) unless the exact words “means for” are followed by a participle.

[0466] The foregoing description, for purposes of explanation, use specific nomenclature to provide a thorough understanding of the described embodiments. However, it should be apparent to one skilled in the art that the specific details are not required to practice the described embodiments. Thus, the foregoing descriptions of specific embodiments are presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the described embodiments to the precise forms disclosed. It should be apparent to one of ordinary skill in the art that many modifications and variations are possible in view of the above teachings. [0467] The above discussion is meant to be illustrative of the principles and various embodiments of the present invention. Once the above disclosure is fully appreciated, numerous variations and modifications will become apparent to those skilled in the art. It is intended that the following claims be interpreted to embrace all such variations and modifications.

METHOD AND SYSTEM FOR USING ARTIFICIAL INTELLIGENCE TO ASSIGN PATIENTS TO COHORTS AND DYNAMICALLY CONTROLLING A TREATMENT APPARATUS BASED ON THE ASSIGNMENT DURING AN ADAPTIVE TELEMEDICAL SESSION

[0468] Determining a treatment plan for a patient having certain characteristics (e.g., vital-sign or other measurements; performance; demographic; geographic; diagnostic; measurement- or test-based; medically historic; etiologic; cohort-associative; differentially diagnostic; surgical, physically therapeutic, pharmacologic and other treatment(s) recommended; etc.) may be a technically challenging problem. For example, a multitude of information may be considered when determining a treatment plan, which may result in inefficiencies and inaccuracies in the treatment plan selection process. In a rehabilitative setting, some of the multitude of information considered may include characteristics of the patient such as personal information, performance information, and measurement information. The personal information may include, e.g., demographic, psychographic or other information, such as an age, a weight, a gender, a height, a body mass index, a medical condition, a familial medication history, an injury, a medical procedure, a medication prescribed, or some combination thereof. The performance information may include, e.g., an elapsed time of using a treatment apparatus, an amount of force exerted on a portion of the treatment apparatus, a range of motion achieved on the treatment apparatus, a movement speed of a portion of the treatment apparatus, an indication of a plurality of pain levels using the treatment apparatus, or some combination thereof. The measurement information may include, e.g., a vital sign, a respiration rate, a heartrate, a temperature, a blood pressure, or some combination thereof. It may be desirable to process the characteristics of a multitude of patients, the treatment plans performed for those patients, and the results of the treatment plans for those patients.

[0469] Further, another technical problem may involve distally treating, via a computing device during a telemedicine or telehealth session, a patient from a location different than a location at which the patient is located. An additional technical problem is controlling or enabling the control of, from the different location, a treatment apparatus used by the patient at the location at which the patient is located. Oftentimes, when a patient undergoes rehabilitative surgery (e.g., knee surgery), a physical therapist or other medical professional may prescribe a treatment apparatus to the patient to use to perform a treatment protocol at their residence or any mobile location or temporary domicile. A medical professional may refer to a doctor, physician assistant, nurse, chiropractor, dentist, physical therapist, acupuncturist, physical trainer, or the like. A medical professional may refer to any person with a credential, license, degree, or the like in the field of medicine, physical therapy, rehabilitation, or the like.

[0470] Since the physical therapist or other medical professional is located in a different location from the patient and the treatment apparatus, it may be technically challenging for the physical therapist or other medical professional to monitor the patient’s actual progress (as opposed to relying on the patient’s word about their progress) using the treatment apparatus, modify the treatment plan according to the patient’s progress, adapt the treatment apparatus to the personal characteristics of the patient as the patient performs the treatment plan, and the like.

[0471] Accordingly, embodiments of the present disclosure pertain to using artificial intelligence and/or machine learning to assign patients to cohorts and to dynamically control a treatment apparatus based on the assignment during an adaptive telemedical session. In some embodiments, numerous treatment apparatuses may be provided to patients. The treatment apparatuses may be used by the patients to perform treatment plans in their residences, at a gym, at a rehabilitative center, at a hospital, or any suitable location, including permanent or temporary domiciles. In some embodiments, the treatment apparatuses may be communicatively coupled to a server. Characteristics of the patients may be collected before, during, and/or after the patients perform the treatment plans. For example, the personal information, the performance information, and the measurement information may be collected before, during, and/or after the person performs the treatment plans. The results (e.g., improved performance or decreased performance) of performing each exercise may be collected from the treatment apparatus throughout the treatment plan and after the treatment plan is performed. The parameters, settings, configurations, etc. (e.g., position of pedal, amount of resistance, etc.) of the treatment apparatus may be collected before, during, and/or after the treatment plan is performed.

[0472] Each characteristic of the patient, each result, and each parameter, setting, configuration, etc. may be timestamped and may be correlated with a particular step in the treatment plan. Such a technique may enable determining which steps in the treatment plan lead to desired results (e.g., improved muscle strength, range of motion, etc.) and which steps lead to diminishing returns (e.g., continuing to exercise after 3 minutes actually delays or harms recovery).

[0473] Data may be collected from the treatment apparatuses and/or any suitable computing device (e.g., computing devices where personal information is entered, such as a clinician interface or patient interface) over time as the patients use the treatment apparatuses to perform the various treatment plans. The data that may be collected may include the characteristics of the patients, the treatment plans performed by the patients, and the results of the treatment plans.

[0474] In some embodiments, the data may be processed to group certain people into cohorts. The people may be grouped by people having certain or selected similar characteristics, treatment plans, and results of performing the treatment plans. For example, athletic people having no medical conditions who perform a treatment plan (e.g., use the treatment apparatus for 30 minutes a day 5 times a week for 3 weeks) and who fully recover may be grouped into a first cohort. Older people who are classified obese and who perform a treatment plan (e.g., use the treatment plan for 10 minutes a day 3 times a week for 4 weeks) and who improve their range of motion by 75 percent may be grouped into a second cohort.

[0475] In some embodiments, an artificial intelligence engine may include one or more machine learning models that are trained using the cohorts. For example, the one or more machine learning models may be trained to receive an input of characteristics of a new patient and to output a treatment plan for the patient that results in a desired result. The machine learning models may match a pattern between the characteristics of the new patient and at least one patient of the patients included in a particular cohort. When a pattern is matched, the machine learning models may assign the new patient to the particular cohort and select the treatment plan associated with the at least one patient. The artificial intelligence engine may be configured to control, distally and based on the treatment plan, the treatment apparatus while the new patient uses the treatment apparatus to perform the treatment plan.

[0476] As may be appreciated, the characteristics of the new patient may change as the new patient uses the treatment apparatus to perform the treatment plan. For example, the performance of the patient may improve quicker than expected for people in the cohort to which the new patient is currently assigned. Accordingly, the machine learning models may be trained to dynamically reassign, based on the changed characteristics, the new patient to a different cohort that includes people having characteristics similar to the now -changed characteristics as the new patient. For example, a clinically obese patient may lose weight and no longer meet the weight criterion for the initial cohort, result in the patient’ s being reassigned to a different cohort with a different weight criterion. A different treatment plan may be selected for the new patient, and the treatment apparatus may be controlled, distally and based on the different treatment plan, the treatment apparatus while the new patient uses the treatment apparatus to perform the treatment plan. Such techniques may provide the technical solution of distally controlling a treatment apparatus. Further, the techniques may lead to faster recovery times and/or better results for the patients because the treatment plan that most accurately fits their characteristics is selected and implemented, in real-time, at any given moment. “Real-time” may also refer to near real-time, which may be less than 10 seconds. As described herein, the term “results” may refer to medical results or medical outcomes. Results and outcomes may refer to responses to medical actions.

[0477] Depending on what result is desired, the artificial intelligence engine may be trained to output several treatment plans. For example, one result may include recovering to a threshold level (e.g., 75% range of motion) in a fastest amount of time, while another result may include fully recovering (e.g., 100% range of motion) regardless of the amount of time. The data obtained from the patients and sorted into cohorts may indicate that a first treatment plan provides the first result for people with characteristics similar to the patient’ s, and that a second treatment plan provides the second result for people with characteristics similar to the patient. [0478] Further, the artificial intelligence engine may also be trained to output treatment plans that are not optimal or sub-optimal or even inappropriate (all referred to, without limitation, as “excluded treatment plans”) for the patient. For example, if a patient has high blood pressure, a particular exercise may not be approved or suitable for the patient as it may put the patient at unnecessary risk or even induce a hypertensive crisis and, accordingly, that exercise may be flagged in the excluded treatment plan for the patient.

[0479] In some embodiments, the treatment plans and/or excluded treatment plans may be presented, during a telemedicine or telehealth session, to a medical professional. The medical professional may select a particular treatment plan for the patient to cause that treatment plan to be transmitted to the patient and/or to control, based on the treatment plan, the treatment apparatus. In some embodiments, to facilitate telehealth or telemedicine applications, including remote diagnoses, determination of treatment plans and rehabilitative and/or pharmacologic prescriptions, the artificial intelligence engine may receive and/or operate distally from the patient and the treatment apparatus. In such cases, the recommended treatment plans and/or excluded treatment plans may be presented simultaneously with a video of the patient in real-time or near real-time during a telemedicine or telehealth session on a user interface of a computing device of a medical professional. The video may also be accompanied by audio, text and other multimedia information. Real-time may refer to less than or equal to 2 seconds. Near real-time may refer to any interaction of a sufficiently short time to enable two individuals to engage in a dialogue via such user interface, and will generally be less than 10 seconds but greater than 2 seconds.

[0480] Presenting the treatment plans generated by the artificial intelligence engine concurrently with a presentation of the patient video may provide an enhanced user interface because the medical professional may continue to visually and/or otherwise communicate with the patient while also reviewing the treatment plans on the same user interface. The enhanced user interface may improve the medical professional’s experience using the computing device and may encourage the medical professional to reuse the user interface. Such a technique may also reduce computing resources (e.g., processing, memory, network) because the medical professional does not have to switch to another user interface screen to enter a query for a treatment plan to recommend based on the characteristics of the patient. The artificial intelligence engine provides, dynamically on the fly, the treatment plans and excluded treatment plans.

[0481] In some embodiments, the treatment apparatus may be adaptive and/or personalized because its properties, configurations, and positions may be adapted to the needs of a particular patient. For example, the pedals may be dynamically adjusted on the fly (e.g., via a telemedicine session or based on programmed configurations in response to certain measurements being detected) to increase or decrease a range of motion to comply with a treatment plan designed for the user. In some embodiments, a medical professional may adapt, remotely during a telemedicine session, the treatment apparatus to the needs of the patient by causing a control instruction to be transmitted from a server to treatment apparatus. Such adaptive nature may improve the results of recovery for a patient, furthering the goals of personalized medicine, and enabling personalization of the treatment plan on a per-individual basis.

[0482] FIG. 14 shows a block diagram of a computer-implemented system 2010, hereinafter called “the system” for managing a treatment plan. Managing the treatment plan may include using an artificial intelligence engine to recommend treatment plans and/or provide excluded treatment plans that should not be recommended to a patient.

[0483] The system 2010 also includes a server 2030 configured to store and to provide data related to managing the treatment plan. The server 2030 may include one or more computers and may take the form of a distributed and/or virtualized computer or computers. The server 2030 also includes a first communication interface 2032 configured to communicate with the clinician interface 2020 via a first network 2034.1n some embodiments, the first network 2034 may include wired and/or wireless network connections such as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC), cellular data network, etc. The server 2030 includes a first processor 2036 and a first machine -readable storage memory 2038, which may be called a “memory” for short, holding first instructions 2040 for performing the various actions of the server 2030 for execution by the first processor 2036. The server 2030 is configured to store data regarding the treatment plan. For example, the memory 2038 includes a system data store 2042 configured to hold system data, such as data pertaining to treatment plans for treating one or more patients. The server 2030 is also configured to store data regarding performance by a patient in following a treatment plan. For example, the memory 2038 includes a patient data store 2044 configured to hold patient data, such as data pertaining to the one or more patients, including data representing each patient’s performance within the treatment plan.

[0484] In addition, the characteristics (e.g., personal, performance, measurement, etc.) of the people, the treatment plans followed by the people, the level of compliance with the treatment plans, and the results of the treatment plans may use correlations and other statistical or probabilistic measures to enable the partitioning of or to partition the treatment plans into different patient cohort-equivalent databases in the patient data store 2044. For example, the data for a first cohort of first patients having a first similar injury, a first similar medical condition, a first similar medical procedure performed, a first treatment plan followed by the first patient, and a first result of the treatment plan may be stored in a first patient database. The data for a second cohort of second patients having a second similar injury, a second similar medical condition, a second similar medical procedure performed, a second treatment plan followed by the second patient, and a second result of the treatment plan may be stored in a second patient database. Any single characteristic or any combination of characteristics may be used to separate the cohorts of patients. In some embodiments, the different cohorts of patients may be stored in different partitions or volumes of the same database. There is no specific limit to the number of different cohorts of patients allowed, other than as limited by mathematical combinatoric and/or partition theory.

[0485] This characteristic data, treatment plan data, and results data may be obtained from numerous treatment apparatuses and/or computing devices over time and stored in the database 2044. The characteristic data, treatment plan data, and results data may be correlated in the patient-cohort databases in the patient data store 2044. The characteristics of the people may include personal information, performance information, and/or measurement information.

[0486] In addition to the historical information about other people stored in the patient cohort-equivalent databases, real-time or near-real-time information based on the current patient’s characteristics about a current patient being treated may be stored in an appropriate patient cohort-equivalent database. The characteristics of the patient may be determined to match or be similar to the characteristics of another person in a particular cohort (e.g., cohort A) and the patient may be assigned to that cohort.

[0487] In some embodiments, the server 2030 may execute an artificial intelligence (AI) engine 2011 that uses one or more machine learning models 2013 to perform at least one of the embodiments disclosed herein. The server 2030 may include a training engine 2009 capable of generating the one or more machine learning models 2013. The machine learning models 2013 may be trained to assign people to certain cohorts based on their characteristics, select treatment plans using real-time and historical data correlations involving patient cohort-equivalents, and control a treatment apparatus 2070, among other things. The one or more machine learning models 2013 may be generated by the training engine 2009 and may be implemented in computer instructions executable by one or more processing devices of the training engine 2009 and/or the servers 2030. To generate the one or more machine learning models 2013, the training engine 2009 may train the one or more machine learning models 2013. The one or more machine learning models 2013 may be used by the artificial intelligence engine 2011.

[0488] The training engine 2009 may be a rackmount server, a router computer, a personal computer, a portable digital assistant, a smartphone, a laptop computer, a tablet computer, a netbook, a desktop computer, an Internet of Things (IoT) device, any other desired computing device, or any combination of the above. The training engine 2009 may be cloud-based or a real-time software platform, and it may include privacy software or protocols, and/or security software or protocols.

[0489] To train the one or more machine learning models 2013, the training engine 2009 may use a training data set of a corpus of the characteristics of the people that used the treatment apparatus 2070 to perform treatment plans, the details (e.g., treatment protocol including exercises, amount of time to perform the exercises, how often to perform the exercises, a schedule of exercises, parameters/configurations/settings of the treatment apparatus 2070 throughout each step of the treatment plan, etc.) of the treatment plans performed by the people using the treatment apparatus 2070, and the results of the treatment plans performed by the people. The one or more machine learning models 2013 may be trained to match patterns of characteristics of a patient with characteristics of other people in assigned to a particular cohort. The term “match” may refer to an exact match, a correlative match, a substantial match, etc. The one or more machine learning models 2013 may be trained to receive the characteristics of a patient as input, map the characteristics to characteristics of people assigned to a cohort, and select a treatment plan from that cohort. The one or more machine learning models 2013 may also be trained to control, based on the treatment plan, the machine learning apparatus 2070.

[0490] Different machine learning models 2013 may be trained to recommend different treatment plans for different desired results. For example, one machine learning model may be trained to recommend treatment plans for most effective recovery, while another machine learning model may be trained to recommend treatment plans based on speed of recovery. [0491] Using training data that includes training inputs and corresponding target outputs, the one or more machine learning models 2013 may refer to model artifacts created by the training engine 2009. The training engine 9 may find patterns in the training data wherein such patterns map the training input to the target output, and generate the machine learning models 2013 that capture these patterns. In some embodiments, the artificial intelligence engine 2011, the database 2033, and/or the training engine 2009 may reside on another component (e.g., assistant interface 2094, clinician interface 2020, etc.) depicted in FIG. 14.

[0492] The one or more machine learning models 2013 may comprise, e.g., a single level of linear or non linear operations (e.g., a support vector machine [SVM]) or the machine learning models 2013 may be a deep network, i.e., a machine learning model comprising multiple levels of non-linear operations. Examples of deep networks are neural networks including generative adversarial networks, convolutional neural networks, recurrent neural networks with one or more hidden layers, and fully connected neural networks (e.g., each neuron may transmit its output signal to the input of the remaining neurons, as well as to itself). For example, the machine learning model may include numerous layers and/or hidden layers that perform calculations (e.g., dot products) using various neurons.

[0493] The system 2010 also includes a patient interface 2050 configured to communicate information to a patient and to receive feedback from the patient. Specifically, the patient interface includes an input device 2052 and an output device 2054, which may be collectively called a patient user interface 2052, 2054. The input device 52 may include one or more devices, such as a keyboard, a mouse, a touch screen input, a gesture sensor, and/or a microphone and processor configured for voice recognition. The output device 2054 may take one or more different forms including, for example, a computer monitor or display screen on a tablet, smartphone, or a smart watch. The output device 2054 may include other hardware and/or software components such as a projector, virtual reality capability, augmented reality capability, etc. The output device 2054 may incorporate various different visual, audio, or other presentation technologies. For example, the output device 2054 may include a non-visual display, such as an audio signal, which may include spoken language and/or other sounds such as tones, chimes, and/or melodies, which may signal different conditions and/or directions. The output device 2054 may comprise one or more different display screens presenting various data and/or interfaces or controls for use by the patient. The output device 2054 may include graphics, which may be presented by a web- based interface and/or by a computer program or application (App.).

[0494] As shown in FIG. 14, the patient interface 2050 includes a second communication interface 2056, which may also be called a remote communication interface configured to communicate with the server 2030 and/or the clinician interface 2020 via a second network 2058. In some embodiments, the second network 2058 may include a local area network (LAN), such as an Ethernet network. In some embodiments, the second network 2058 may include the Internet, and communications between the patient interface 2050 and the server 2030 and/or the clinician interface 2020 may be secured via encryption, such as, for example, by using a virtual private network (VPN). In some embodiments, the second network 2058 may include wired and/or wireless network connections such as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC), cellular data network, etc. In some embodiments, the second network 58 may be the same as and/or operationally coupled to the first network 2034.

[0495] The patient interface 2050 includes a second processor 2060 and a second machine -readable storage memory 2062 holding second instructions 2064 for execution by the second processor 2060 for performing various actions of patient interface 2050. The second machine-readable storage memory 2062 also includes a local data store 2066 configured to hold data, such as data pertaining to a treatment plan and/or patient data, such as data representing a patient’s performance within a treatment plan. The patient interface 2050 also includes a local communication interface 2068 configured to communicate with various devices for use by the patient in the vicinity of the patient interface 2050. The local communication interface 2068 may include wired and/or wireless communications. In some embodiments, the local communication interface 2068 may include a local wireless network such as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC), cellular data network, etc.

[0496] The system 2010 also includes a treatment apparatus 2070 configured to be manipulated by the patient and/or to manipulate a body part of the patient for performing activities according to the treatment plan. In some embodiments, the treatment apparatus 2070 may take the form of an exercise and rehabilitation apparatus configured to perform and/or to aid in the performance of a rehabilitation regimen, which may be an orthopedic rehabilitation regimen, and the treatment includes rehabilitation of a body part of the patient, such as a joint or a bone or a muscle group. The treatment apparatus 2070 may be any suitable medical, rehabilitative, therapeutic, etc. apparatus configured to be controlled distally via another computing device to treat a patient and/or exercise the patient. The treatment apparatus 2070 may be an electromechanical machine including one or more weights, an electromechanical bicycle, an electromechanical spin-wheel, a smart-mirror, a treadmill, or the like. The body part may include, for example, a spine, a hand, a foot, a knee, or a shoulder. The body part may include a part of a joint, a bone, or a muscle group, such as one or more vertebrae, a tendon, or a ligament. As shown in FIG. 14, the treatment apparatus 2070 includes a controller 2072, which may include one or more processors, computer memory, and/or other components. The treatment apparatus 2070 also includes a fourth communication interface 2074 configured to communicate with the patient interface 2050 via the local communication interface 2068. The treatment apparatus 2070 also includes one or more internal sensors 2076 and an actuator 2078, such as a motor. The actuator 2078 may be used, for example, for moving the patient’s body part and/or for resisting forces by the patient.

[0497] The internal sensors 2076 may measure one or more operating characteristics of the treatment apparatus 2070 such as, for example, a force a position, a speed, and /or a velocity. In some embodiments, the internal sensors 2076 may include a position sensor configured to measure at least one of a linear motion or an angular motion of a body part of the patient. For example, an internal sensor 2076 in the form of a position sensor may measure a distance that the patient is able to move a part of the treatment apparatus 2070, where such distance may correspond to a range of motion that the patient’s body part is able to achieve. In some embodiments, the internal sensors 2076 may include a force sensor configured to measure a force applied by the patient. For example, an internal sensor 2076 in the form of a force sensor may measure a force or weight the patient is able to apply, using a particular body part, to the treatment apparatus 2070.

[0498] The system 2010 shown in FIG. 14 also includes an ambulation sensor 2082, which communicates with the server 30 via the local communication interface 2068 of the patient interface 2050. The ambulation sensor 2082 may track and store a number of steps taken by the patient. In some embodiments, the ambulation sensor 2082 may take the form of a wristband, wristwatch, or smart watch. In some embodiments, the ambulation sensor 2082 may be integrated within a phone, such as a smartphone. [0499] The system 2010 shown in FIG. 14 also includes a goniometer 2084, which communicates with the server 30 via the local communication interface 2068 of the patient interface 2050. The goniometer 2084 measures an angle of the patient’s body part. For example, the goniometer 2084 may measure the angle of flex of a patient’s knee or elbow or shoulder.

[0500] The system 2010 shown in FIG. 14 also includes a pressure sensor 2086, which communicates with the server 2030 via the local communication interface 2068 of the patient interface 2050. The pressure sensor 2086 measures an amount of pressure or weight applied by a body part of the patient. For example, pressure sensor 2086 may measure an amount of force applied by a patient’s foot when pedaling a stationary bike. [0501] The system 2010 shown in FIG. 14 also includes a supervisory interface 2090 which may be similar or identical to the clinician interface 2020. In some embodiments, the supervisory interface 2090 may have enhanced functionality beyond what is provided on the clinician interface 2020. The supervisory interface 2090 may be configured for use by a person having responsibility for the treatment plan, such as an orthopedic surgeon.

[0502] The system 2010 shown in FIG. 14 also includes a reporting interface 2092 which may be similar or identical to the clinician interface 2020. In some embodiments, the reporting interface 2092 may have less functionality from what is provided on the clinician interface 2020. For example, the reporting interface 2092 may not have the ability to modify a treatment plan. Such a reporting interface 2092 may be used, for example, by a biller to determine the use of the system 2010 for billing purposes. In another example, the reporting interface 2092 may not have the ability to display patient identifiable information, presenting only pseudonymized data and/or anonymized data for certain data fields concerning a data subject and/or for certain data fields concerning a quasi-identifier of the data subject. Such a reporting interface 2092 may be used, for example, by a researcher to determine various effects of a treatment plan on different patients.

[0503] The system 2010 includes an assistant interface 2094 for an assistant, such as a doctor, a nurse, a physical therapist, or a technician, to remotely communicate with the patient interface 2050 and/or the treatment apparatus 2070. Such remote communications may enable the assistant to provide assistance or guidance to a patient using the system 2010. More specifically, the assistant interface 2094 is configured to communicate a telemedicine signal 2096, 2097, 2098a, 2098b, 2099a, 2099b with the patient interface 2050 via a network connection such as, for example, via the first network 2034 and/or the second network 2058. The telemedicine signal 2096, 2097, 2098a, 2098b, 2099a, 2099b comprises one of an audio signal 2096, an audiovisual signal 2097, an interface control signal 2098a for controlling a function of the patient interface 2050, an interface monitor signal 2098b for monitoring a status of the patient interface 2050, an apparatus control signal 2099a for changing an operating parameter of the treatment apparatus 2070, and/or an apparatus monitor signal 2099b for monitoring a status of the treatment apparatus 2070. In some embodiments, each of the control signals 2098a, 2099a may be unidirectional, conveying commands from the assistant interface 2094 to the patient interface 2050. In some embodiments, in response to successfully receiving a control signal 2098a, 2099a and/or to communicate successful and/or unsuccessful implementation of the requested control action, an acknowledgement message may be sent from the patient interface 2050 to the assistant interface 2094. In some embodiments, each of the monitor signals 2098b, 2099b may be unidirectional, status-information commands from the patient interface 2050 to the assistant interface 94. In some embodiments, an acknowledgement message may be sent from the assistant interface 2094 to the patient interface 2050 in response to successfully receiving one of the monitor signals 2098b, 2099b.

[0504] In some embodiments, the patient interface 2050 may be configured as a pass-through for the apparatus control signals 2099a and the apparatus monitor signals 2099b between the treatment apparatus 2070 and one or more other devices, such as the assistant interface 2094 and/or the server 2030. For example, the patient interface 2050 may be configured to transmit an apparatus control signal 2099a in response to an apparatus control signal 2099a within the telemedicine signal 2096, 2097, 2098a, 2098b, 2099a, 2099b from the assistant interface 2094.

[0505] In some embodiments, the assistant interface 2094 may be presented on a shared physical device as the clinician interface 2020. For example, the clinician interface 2020 may include one or more screens that implement the assistant interface 2094. Alternatively or additionally, the clinician interface 2020 may include additional hardware components, such as a video camera, a speaker, and/or a microphone, to implement aspects of the assistant interface 2094.

[0506] In some embodiments, one or more portions of the telemedicine signal 2096, 2097, 2098a, 2098b, 2099a, 2099b may be generated from a prerecorded source (e.g., an audio recording, a video recording, or an animation) for presentation by the output device 2054 of the patient interface 2050. For example, a tutorial video may be streamed from the server 2030 and presented upon the patient interface 2050. Content from the prerecorded source may be requested by the patient via the patient interface 2050. Alternatively, via a control on the assistant interface 2094, the assistant may cause content from the prerecorded source to be played on the patient interface 2050.

[0507] The assistant interface 2094 includes an assistant input device 2022 and an assistant display 2024, which may be collectively called an assistant user interface 2022, 2024. The assistant input device 2022 may include one or more of a telephone, a keyboard, a mouse, a trackpad, or a touch screen, for example. Alternatively or additionally, the assistant input device 2022 may include one or more microphones. In some embodiments, the one or more microphones may take the form of a telephone handset, headset, or wide-area microphone or microphones configured for the assistant to speak to a patient via the patient interface 2050. In some embodiments, assistant input device 2022 may be configured to provide voice-based functionalities, with hardware and/or software configured to interpret spoken instructions by the assistant by using the one or more microphones. The assistant input device 2022 may include functionality provided by or similar to existing voice- based assistants such as Siii by Apple, Alexaby Amazon, Google Assistant, or Bixby by Samsung. The assistant input device 2022 may include other hardware and/or software components. The assistant input device 2022 may include one or more general purpose devices and/or special-purpose devices.

[0508] The assistant display 2024 may take one or more different forms including, for example, a computer monitor or display screen on a tablet, a smartphone, or a smart watch. The assistant display 2024 may include other hardware and/or software components such as projectors, virtual reality capabilities, or augmented reality capabilities, etc. The assistant display 2024 may incorporate various different visual, audio, or other presentation technologies. For example, the assistant display 2024 may include a non-visual display, such as an audio signal, which may include spoken language and/or other sounds such as tones, chimes, melodies, and/or compositions, which may signal different conditions and/or directions. The assistant display 2024 may comprise one or more different display screens presenting various data and/or interfaces or controls for use by the assistant. The assistant display 2024 may include graphics, which may be presented by a web-based interface and/or by a computer program or application (App.).

[0509] In some embodiments, the system 2010 may provide computer translation of language from the assistant interface 2094 to the patient interface 2050 and/or vice-versa. The computer translation of language may include computer translation of spoken language and/or computer translation of text. Additionally or alternatively, the system 2010 may provide voice recognition and/or spoken pronunciation of text. For example, the system 2010 may convert spoken words to printed text and/or the system 2010 may audibly speak language from printed text. The system 2010 may be configured to recognize spoken words by any or all of the patient, the clinician, and/or the assistant. In some embodiments, the system 2010 may be configured to recognize and react to spoken requests or commands by the patient. For example, the system 2010 may automatically initiate a telemedicine session in response to a verbal command by the patient (which may be given in any one of several different languages).

[0510] In some embodiments, the server 2030 may generate aspects of the assistant display 2024 for presentation by the assistant interface 2094. For example, the server 2030 may include a web server configured to generate the display screens for presentation upon the assistant display 2024. For example, the artificial intelligence engine 2011 may generate recommended treatment plans and/or excluded treatment plans for patients and generate the display screens including those recommended treatment plans and/or external treatment plans for presentation on the assistant display 2024 of the assistant interface 2094. In some embodiments, the assistant display 2024 may be configured to present a virtualized desktop hosted by the server 2030. In some embodiments, the server 2030 may be configured to communicate with the assistant interface 2094 via the first network 2034. In some embodiments, the first network 2034 may include a local area network (LAN), such as an Ethernet network. In some embodiments, the first network 2034 may include the Internet, and communications between the server 2030 and the assistant interface 2094 may be seemed via privacy enhancing technologies, such as, for example, by using encryption over a virtual private network (VPN). Alternatively or additionally, the server 2030 may be configured to communicate with the assistant interface 2094 via one or more networks independent of the first network 2034 and/or other communication means, such as a direct wired or wireless communication channel. In some embodiments, the patient interface 2050 and the treatment apparatus 2070 may each operate from a patient location geographically separate from a location of the assistant interface 2094. For example, the patient interface 2050 and the treatment apparatus 2070 may be used as part of an in-home rehabilitation system, which may be aided remotely by using the assistant interface 2094 at a centralized location, such as a clinic or a call center.

[0511] In some embodiments, the assistant interface 2094 may be one of several different terminals (e.g., computing devices) that may be grouped together, for example, in one or more call centers or at one or more clinicians’ offices. In some embodiments, a plurality of assistant interfaces 2094 may be distributed geographically. In some embodiments, a person may work as an assistant remotely from any conventional office infrastructure. Such remote work may be performed, for example, where the assistant interface 94 takes the form of a computer and/or telephone. This remote work functionality may allow for work-from-home arrangements that may include part time and/or flexible work hours for an assistant.

[0512] FIGS. 15-16 show an embodiment of atreatment apparatus 2070. More specifically, FIG. 15 shows a treatment apparatus 2070 in the form of a stationary cycling machine 2100, which may be called a stationary bike, for short. The stationary cycling machine 2100 includes a set of pedals 2102 each attached to a pedal arm 2104 for rotation about an axle 2106. In some embodiments, and as shown in FIG. 15, the pedals 2102 are movable on the pedal arms 2104 in order to adjust a range of motion used by the patient in pedaling. For example, the pedals being located inwardly toward the axle 2106 corresponds to a smaller range of motion than when the pedals are located outwardly away from the axle 2106. A pressure sensor 2086 is attached to or embedded within one of the pedals 2102 for measuring an amount of force applied by the patient on the pedal 2102. The pressure sensor 2086 may communicate wirelessly to the treatment apparatus 2070 and/or to the patient interface 2050. [0513] FIG. 17 shows a person (a patient) using the treatment apparatus of FIG. 15, and showing sensors and various data parameters connected to a patient interface 2050. The example patient interface 2050 is a tablet computer or smartphone, or a phablet, such as an iPad, an iPhone, an Android device, or a Surface tablet, which is held manually by the patient. In some other embodiments, the patient interface 2050 may be embedded within or attached to the treatment apparatus 2070. FIG. 17 shows the patient wearing the ambulation sensor 2082 on his wrist, with a note showing “STEPS TODAY 1355”, indicating that the ambulation sensor 2082 has recorded and transmitted that step count to the patient interface 2050. FIG. 17 also shows the patient wearing the goniometer 2084 on his right knee, with a note showing “KNEE ANGLE 72°”, indicating that the goniometer 2084 is measuring and transmitting that knee angle to the patient interface 2050. FIG. 17 also shows a right side of one of the pedals 2102 with a pressure sensor 2086 showing “FORCE 12.5 lbs.,” indicating that the right pedal pressure sensor 2086 is measuring and transmitting that force measurement to the patient interface 2050. FIG. 17 also shows a left side of one of the pedals 2102 with a pressure sensor 2086 showing “FORCE 27 lbs.”, indicating that the left pedal pressure sensor 2086 is measuring and transmitting that force measurement to the patient interface 2050. FIG. 17 also shows other patient data, such as an indicator of “SESSION TIME 0:04: 13”, indicating that the patient has been using the treatment apparatus 2070 for 4 minutes and 13 seconds. This session time may be determined by the patient interface 2050 based on information received from the treatment apparatus 2070. FIG. 17 also shows an indicator showing “PAIN LEVEL 3”. Such a pain level may be obtained from the patent in response to a solicitation, such as a question, presented upon the patient interface 2050. [0514] FIG. 18 is an example embodiment of an overview display 2120 of the assistant interface 2094. Specifically, the overview display 2120 presents several different controls and interfaces for the assistant to remotely assist a patient with using the patient interface 2050 and/or the treatment apparatus 2070. This remote assistance functionality may also be called telemedicine or telehealth.

[0515] Specifically, the overview display 2120 includes a patient profile display 2130 presenting biographical information regarding a patient using the treatment apparatus 2070. The patient profile display 2130 may take the form of a portion or region of the overview display 2120, as shown in FIG. 18, although the patient profile display 2130 may take other forms, such as a separate screen or a popup window. In some embodiments, the patient profile display 2130 may include a limited subset of the patient’s biographical information. More specifically, the data presented upon the patient profile display 2130 may depend upon the assistant’s need for that information. For example, a medical professional that is assisting the patient with a medical issue may be provided with medical history information regarding the patient, whereas a technician troubleshooting an issue with the treatment apparatus 2070 may be provided with a much more limited set of information regarding the patient. The technician, for example, may be given only the patient’s name. The patient profile display 2130 may include pseudonymized data and/or anonymized data or use any privacy enhancing technology to prevent confidential patient data from being communicated in a way that could violate patient confidentiality requirements. Such privacy enhancing technologies may enable compliance with laws, regulations, or other rules of governance such as, but not limited to, the Health Insurance Portability and Accountability Act (HIPAA), or the General Data Protection Regulation (GDPR), wherein the patient may be deemed a “data subject”.

[0516] In some embodiments, the patient profile display 2130 may present information regarding the treatment plan for the patient to follow in using the treatment apparatus 2070. Such treatment plan information may be limited to an assistant who is a medical professional, such as a doctor or physical therapist. For example, a medical professional assisting the patient with an issue regarding the treatment regimen may be provided with treatment plan information, whereas a technician troubleshooting an issue with the treatment apparatus 2070 may not be provided with any information regarding the patient’s treatment plan.

[0517] In some embodiments, one or more recommended treatment plans and/or excluded treatment plans may be presented in the patient profile display 2130 to the assistant. The one or more recommended treatment plans and/or excluded treatment plans may be generated by the artificial intelligence engine 2011 of the server 2030 and received from the server 2030 in real-time during, inter alia, a telemedicine or telehealth session. An example of presenting the one or more recommended treatment plans and/or mled-out treatment plans is described below with reference to FIG. 20.

[0518] The example overview display 2120 shown in FIG. 18 also includes a patient status display 2134 presenting status information regarding a patient using the treatment apparatus. The patient status display 2134 may take the form of a portion or region of the overview display 2120, as shown in FIG. 18, although the patient status display 2134 may take other forms, such as a separate screen or a popup window. The patient status display 2134 includes sensor data 2136 from one ormore of the external sensors 2082, 2084, 2086, and/orfrom one or more internal sensors 2076 of the treatment apparatus 2070. In some embodiments, the patient status display 2134 may present other data 2138 regarding the patient, such as last reported pain level, or progress within a treatment plan.

[0519] User access controls may be used to limit access, including what data is available to be viewed and/or modified, on any or all of the user interfaces 2020, 2050, 2090, 2092, 2094 of the system 2010. In some embodiments, user access controls may be employed to control what information is available to any given person using the system 2010. For example, data presented on the assistant interface 2094 may be controlled by user access controls, with permissions set depending on the assistant/user’s need for and/or qualifications to view that information.

[0520] The example overview display 2120 shown in FIG. 18 also includes a help data display 2140 presenting information for the assistant to use in assisting the patient. The help data display 2140 may take the form of a portion or region of the overview display 2120, as shown in FIG. 18. The help data display 2140 may take other forms, such as a separate screen or a popup window. The help data display 2140 may include, for example, presenting answers to frequently asked questions regarding use of the patient interface 2050 and/or the treatment apparatus 2070. The help data display 2140 may also include research data or best practices. In some embodiments, the help data display 2140 may present scripts for answers or explanations in response to patient questions. In some embodiments, the help data display 2140 may present flow charts or walk-throughs for the assistant to use in determining a root cause and/or solution to a patient’ s problem. In some embodiments, the assistant interface 2094 may present two or more help data displays 2140, which may be the same or different, for simultaneous presentation of help data for use by the assistant for example, a first help data display may be used to present a troubleshooting flowchart to determine the source of a patient’s problem, and a second help data display may present script information for the assistant to read to the patient, such information to preferably include directions for the patient to perform some action, which may help to narrow down or solve the problem. In some embodiments, based upon inputs to the troubleshooting flowchart in the first help data display, the second help data display may automatically populate with script information.

[0521] The example overview display 2120 shown in FIG. 18 also includes apatient interface control 2150 presenting information regarding the patient interface 2050, and/or to modify one or more settings of the patient interface 2050. The patient interface control 2150 may take the form of a portion or region of the overview display 2120, as shown in FIG. 18. The patient interface control 2150 may take other forms, such as a separate screen or a popup window. The patient interface control 2150 may present information communicated to the assistant interface 2094 via one or more of the interface monitor signals 2098b. As shown in FIG. 18, the patient interface control 2150 includes a display feed 2152 of the display presented by the patient interface 2050. In some embodiments, the display feed 2152 may include a live copy of the display screen currently being presented to the patient by the patient interface 2050. In other words, the display feed 2152 may present an image of what is presented on a display screen of the patient interface 2050. In some embodiments, the display feed 2152 may include abbreviated information regarding the display screen currently being presented by the patient interface 2050, such as a screen name or a screen number. The patient interface control 2150 may include a patient interface setting control 2154 for the assistant to adjust or to control one or more settings or aspects of the patient interface 2050. In some embodiments, the patient interface setting control 2154 may cause the assistant interface 2094 to generate and/or to transmit an interface control signal 2098 for controlling a function or a setting of the patient interface 2050.

[0522] In some embodiments, the patient interface setting control 2154 may include collaborative browsing or co-browsing capability for the assistant to remotely view and/or control the patient interface 2050. For example, the patient interface setting control 2154 may enable the assistant to remotely enter text to one or more text entry fields on the patient interface 2050 and/or to remotely control a cursor on the patient interface 2050 using a mouse or touchscreen of the assistant interface 2094.

[0523] In some embodiments, using the patient interface 50, the patient interface setting control 2154 may allow the assistant to change a setting that cannot be changed by the patient. For example, the patient interface 2050 may be precluded from accessing a language setting to prevent a patient from inadvertently switching, on the patient interface 2050, the language used for the displays, whereas the patient interface setting control 2154 may enable the assistant to change the language setting of the patient interface 2050. In another example, the patient interface 2050 may not be able to change a font size setting to a smaller size in order to prevent a patient from inadvertently switching the font size used for the displays on the patient interface 2050 such that the display would become illegible to the patient, whereas the patient interface setting control 2154 may provide for the assistant to change the font size setting of the patient interface 2050.

[0524] The example overview display 2120 shown in FIG. 18 also includes an interface communications display 2156 showing the status of communications between the patient interface 2050 and one or more other devices 2070, 2082, 2084, such as the treatment apparatus 2070, the ambulation sensor 2082, and/or the goniometer 2084. The interface communications display 2156 may take the form of a portion or region of the overview display 2120, as shown in FIG. 18. The interface communications display 2156 may take otherforms, such as a separate screen or a popup window. The interface communications display 2156 may include controls for the assistant to remotely modify communications with one or more of the other devices 2070, 2082, 2084. For example, the assistant may remotely command the patient interface 2050 to reset communications with one of the other devices 2070, 2082, 2084, or to establish communications with a new one of the other devices 2070, 2082, 2084. This functionality may be used, for example, where the patient has a problem with one of the other devices 2070, 82, 84, or where the patient receives a new or a replacement one of the other devices 2070, 2082, 2084.

[0525] The example overview display 2120 shown in FIG. 18 also includes an apparatus control 2160 for the assistant to view and/or to control information regarding the treatment apparatus 2070. The apparatus control 2160 may take the form of a portion or region of the overview display 2120, as shown in FIG. 18. The apparatus control 2160 may take other forms, such as a separate screen or a popup window. The apparatus control 2160 may include an apparatus status display 2162 with information regarding the current status of the apparatus. The apparatus status display 2162 may present information communicated to the assistant interface 94 via one or more of the apparatus monitor signals 2099b. The apparatus status display 2162 may indicate whether the treatment apparatus 2070 is currently communicating with the patient interface 2050. The apparatus status display 2162 may present other current and/or historical information regarding the status of the treatment apparatus 2070.

[0526] The apparatus control 2160 may include an apparatus setting control 2164 for the assistant to adjust or control one or more aspects of the treatment apparatus 2070. The apparatus setting control 2164 may cause the assistant interface 2094 to generate and/or to transmit an apparatus control signal 2099 for changing an operating parameter of the treatment apparatus 2070, (e.g., a pedal radius setting, a resistance setting, a target RPM, etc.). The apparatus setting control 2164 may include a mode button 2166 and a position control 2168, which may be used in conjunction for the assistant to place an actuator 2078 of the treatment apparatus 2070 in a manual mode, after which a setting, such as a position or a speed of the actuator 2078, can be changed using the position control 2168. The mode button 2166 may provide for a setting, such as a position, to be toggled between automatic and manual modes. In some embodiments, one or more settings may be adjustable at any time, and without having an associated auto/manual mode. In some embodiments, the assistant may change an operating parameter of the treatment apparatus 2070, such as a pedal radius setting, while the patient is actively using the treatment apparatus 2070. Such “on the fly” adjustment may or may not be available to the patient using the patient interface 2050. In some embodiments, the apparatus setting control 2164 may allow the assistant to change a setting that cannot be changed by the patient using the patient interface 2050. For example, the patient interface 2050 may be precluded from changing a preconfigured setting, such as a height or a tilt setting of the treatment apparatus 2070, whereas the apparatus setting control 2164 may provide forthe assistant to change the height or tilt setting of the treatment apparatus 2070.

[0527] The example overview display 2120 shown in FIG. 18 also includes a patient communications control 170 for controlling an audio or an audiovisual communications session with the patient interface 2050. The communications session with the patient interface 2050 may comprise a live feed from the assistant interface 2094 for presentation by the output device of the patient interface 2050. The live feed may take the form of an audio feed and/or a video feed. In some embodiments, the patient interface 2050 may be configured to provide two-way audio or audiovisual communications with a person using the assistant interface 2094. Specifically, the communications session with the patient interface 2050 may include bidirectional (two-way) video or audiovisual feeds, with each of the patient interface 2050 and the assistant interface 2094 presenting video of the other one. In some embodiments, the patient interface 2050 may present video from the assistant interface 94, while the assistant interface 2094 presents only audio or the assistant interface 2094 presents no live audio or visual signal from the patient interface 2050. In some embodiments, the assistant interface 2094 may present video from the patient interface 2050, while the patient interface 2050 presents only audio or the patient interface 2050 presents no live audio or visual signal from the assistant interface 2094.

[0528] In some embodiments, the audio or an audiovisual communications session with the patient interface 2050 may take place, at least in part, while the patient is performing the rehabilitation regimen upon the body part. The patient communications control 2170 may take the form of a portion or region of the overview display 2120, as shown in FIG. 18. The patient communications control 2170 may take other forms, such as a separate screen or a popup window. The audio and/or audiovisual communications may be processed and/or directed by the assistant interface 2094 and/or by another device or devices, such as a telephone system, or a videoconferencing system used by the assistant while the assistant uses the assistant interface 2094. Alternatively or additionally, the audio and/or audiovisual communications may include communications with a third party. For example, the system 2010 may enable the assistant to initiate a 3-way conversation regarding use of a particular piece of hardware or software, with the patient and a subject matter expert, such as a medical professional or a specialist. The example patient communications control 2170 shown in FIG. 18 includes call controls 2172 for the assistant to use in managing various aspects of the audio or audiovisual communications with the patient. The call controls 2172 include a disconnect button 2174 for the assistant to end the audio or audiovisual communications session. The call controls 2172 also include a mute button 2176 to temporarily silence an audio or audiovisual signal from the assistant interface 2094. In some embodiments, the call controls 2172 may include other features, such as a hold button (not shown). The call controls 2172 also include one or more record/playback controls 2178, such as record, play, and pause buttons to control, with the patient interface 50, recording and/or playback of audio and/or video from the teleconference session. The call controls 2172 also include a video feed display 2180 for presenting still and/or video images from the patient interface 2050, and a self-video display 2182 showing the current image of the assistant using the assistant interface. The self -video display 2182 may be presented as a picture-in-picture format, within a section of the video feed display 2180, as shown in FIG. 18. Alternatively or additionally, the self-video display 2182 may be presented separately and/or independently from the video feed display 2180.

[0529] The example overview display 2120 shown in FIG. 18 also includes a third party communications control 2190 for use in conducting audio and/or audiovisual communications with a third party. The third party communications control 2190 may take the form of a portion or region of the overview display 2120, as shown in FIG. 18. The third party communications control 2190 may take other forms, suchas a display ona separate screen or a popup window. The third party communications control 2190 may include one or more controls, such as a contact list and/or buttons or controls to contact a third party regarding use of a particular piece of hardware or software, e.g., a subject matter expert, such as a medical professional or a specialist. The third party communications control 2190 may include conference calling capability for the third party to simultaneously communicate with both the assistant via the assistant interface 2094, and with the patient via the patient interface 2050. For example, the system 2010 may provide for the assistant to initiate a 3-way conversation with the patient and the third party.

[0530] FIG. 19 shows an example block diagram of training a machine learning model 2013 to output, based on data 2600 pertaining to the patient, a treatment plan 2602 for the patient according to the present disclosure. Data pertaining to other patients may be received by the server 2030. The other patients may have used various treatment apparatuses to perform treatment plans. The data may include characteristics of the other patients, the details of the treatment plans performed by the other patients, and/or the results of performing the treatment plans (e.g., a percent of recovery of a portion of the patients’ bodies, an amount of recovery of a portion of the patients’ bodies, an amount of increase or decrease in muscle strength of a portion of patients’ bodies, an amount of increase or decrease in range of motion of a portion of patients’ bodies, etc.).

[0531] As depicted, the data has been assigned to different cohorts. Cohort A includes data for patients having similar first characteristics, first treatment plans, and first results. Cohort B includes data for patients having similar second characteristics, second treatment plans, and second results. For example, cohort A may include first characteristics of patients in their twenties without any medical conditions who underwent surgery for a broken limb; their treatment plans may include a certain treatment protocol (e.g., use the treatment apparatus 70 for 30 minutes 5 times a weekfor 3 weeks, wherein values forthe properties, configurations, and/or settings of the treatment apparatus 70 are set to X (where X is a numerical value) for the first two weeks and to Y (where Y is a numerical value) for the last week).

[0532] Cohort A and cohort B may be included in a training dataset used to train the machine learning model 13. The machine learning model 2013 may be trained to match a pattern between characteristics for each cohort and output the treatment plan that provides the result. Accordingly, when the data 2600 for a new patient is input into the trained machine learning model 2013, the trained machine learning model 2013 may match the characteristics included in the data 2600 with characteristics in either cohort A or cohort B and output the appropriate treatment plan 2602. In some embodiments, the machine learning model 2013 may be trained to output one or more excluded treatment plans that should not be performed by the new patient.

[0533] FIG. 20 shows an embodiment of an overview display 2120 of the assistant interface 2094 presenting recommended treatment plans and excluded treatment plans in real-time during a telemedicine session according to the present disclosure. As depicted, the overview display 2120 just includes sections for the patient profile 2130 and the video feed display 2180, including the self-video display 2182. Any suitable configuration of controls and interfaces of the overview display 2120 described with reference to FIG. 18 may be presented in addition to or instead of the patient profile 2130, the video feed display 2180, and the self-video display 2182.

[0534] The assistant (e.g., medical professional) using the assistant interface 94 (e.g., computing device) during the telemedicine session may be presented in the self-video 2182 in a portion of the overview display 2120 (e.g., user interface presented on a display screen 2024 of the assistant interface 2094) that also presents a video from the patient in the video feed display 2180. Further, the video feed display 2180 may also include a graphical user interface (GUI) object 2700 (e.g., a button) that enables the medical professional to share, in real time or near real-time during the telemedicine session, the recommended treatment plans and/or the excluded treatment plans with the patient on the patient interface 2050. The medical professional may select the GUI object 2700 to share the recommended treatment plans and/or the excluded treatment plans. As depicted, another portion of the overview display 2120 includes the patient profile display 2130.

[0535] The patient profile display 2130 is presenting two example recommended treatment plans 2600 and one example excluded treatment plan 2602. As described herein, the treatment plans may be recommended in view of characteristics of the patient being treated. To generate the recommended treatment plans 2600 the patient should follow to achieve a desired result, a pattern between the characteristics of the patient being treated and a cohort of other people who have used the treatment apparatus 2070 to perform a treatment plan may be matched by one or more machine learning models 2013 of the artificial intelligence engine 2011. Each of the recommended treatment plans may be generated based on different desired results.

[0536] For example, as depicted, the patient profile display 2130 presents “The characteristics of the patient match characteristics of users in Cohort A. The following treatment plans are recommended for the patient based on his characteristics and desired results.” Then, the patient profile display 2130 presents recommended treatment plans from cohort A, and each treatment plan provides different results.

[0537] As depicted, treatment plan “A” indicates “Patient X should use treatment apparatus for 30 minutes a day for 4 days to achieve an increased range of motion of Y%; Patient X has Type 2 Diabetes; and Patient X should be prescribed medication Z for pain management during the treatment plan (medication Z is approved for people having Type 2 Diabetes).” Accordingly, the treatment plan generated achieves increasing the range of motion of Y%. As may be appreciated, the treatment plan also includes a recommended medication (e.g., medication Z) to prescribe to the patient to manage pain in view of a known medical disease (e.g., Type 2 Diabetes) of the patient. That is, the recommended patient medication not only does not conflict with the medical condition of the patient but thereby improves the probability of a superior patient outcome. This specific example and all such examples elsewhere herein are not intended to limit in any way the generated treatment plan from recommending multiple medications, or from handling the acknowledgement, view, diagnosis and/or treatment of comorbid conditions or diseases.

[0538] Recommended treatment plan “B” may specify, based on a different desired result of the treatment plan, a different treatment plan including a different treatment protocol for a treatment apparatus, a different medication regimen, etc.

[0539] As depicted, the patient profile display 2130 may also present the excluded treatment plans 2602. These types of treatment plans are shown to the assistant using the assistant interface 2094 to alert the assistant not to recommend certain portions of a treatment plan to the patient. For example, the excluded treatment plan could specify the following: “Patient X should not use treatment apparatus for longer than 30 minutes a day due to a heart condition; Patient X has Type 2 Diabetes; and Patient X should not be prescribed medication M for pain management during the treatment plan (in this scenario, medication M can cause complications for people having Type 2 Diabetes) . Specifically, the excluded treatment plan points out a limitation of a treatment protocol where, due to a heart condition, Patient X should not exercise for more than 30 minutes a day. The ruled-out treatment plan also points out that Patient X should not be prescribed medication M because it conflicts with the medical condition Type 2 Diabetes.

[0540] The assistant may select the treatment plan for the patient on the overview display 2120. For example, the assistant may use an input peripheral (e.g., mouse, touchscreen, microphone, keyboard, etc.) to select from the treatment plans 2600 for the patient. In some embodiments, during the telemedicine session, the assistant may discuss the pros and cons of the recommended treatment plans 2600 with the patient.

[0541] In any event, the assistant may select the treatment plan for the patient to follow to achieve the desired result. The selected treatment plan may be transmitted to the patient interface 2050 for presentation. The patient may view the selected treatment plan on the patient interface 2050. In some embodiments, the assistant and the patient may discuss during the telemedicine session the details (e.g., treatment protocol using treatment apparatus 2070, diet regimen, medication regimen, etc.) in real-time or in near real-time. In some embodiments, the server 2030 may control, based on the selected treatment plan and during the telemedicine session, the treatment apparatus 2070 as the user uses the treatment apparatus 2070.

[0542] FIG. 21 shows an embodiment of the overview display 2120 of the assistant interface 2094 presenting, in real-time during a telemedicine session, recommended treatment plans that have changed as a result of patient data changing according to the present disclosure. As may be appreciated, the treatment apparatus 2070 and/or any computing device (e.g., patient interface 2050) may transmit data while the patient uses the treatment apparatus 2070 to perform a treatment plan. The data may include updated characteristics of the patient. For example, the updated characteristics may include new performance information and/or measurement information. The performance information may include a speed of a portion of the treatment apparatus 2070, a range of motion achieved by the patient, a force exerted on a portion of the treatment apparatus 2070, a heartrate of the patient, a blood pressure of the patient, a respiratory rate of the patient, and so forth. [0543] In one embodiment, the data received at the server 2030 may be input into the trained machine learning model 2013, which may determine that the characteristics indicate the patient is on track for the current treatment plan. Determining the patient is on track for the current treatment plan may cause the trained machine learning model 2013 to adjust a parameter of the treatment apparatus 2070. The adjustment may be based on a next step of the treatment plan to further improve the performance of the patient.

[0544] In one embodiment, the data received at the server 2030 may be input into the trained machine learning model 2013, which may determine that the characteristics indicate the patient is not on track (e.g., behind schedule, not able to maintain a speed, not able to achieve a certain range of motion, is in too much pain, etc.) for the current treatment plan or is ahead of schedule (e.g., exceeding a certain speed, exercising longer than specified with no pain, exerting more than a specified force, etc.) for the current treatment plan. The trained machine learning model 2013 may determine that the characteristics of the patient no longer match the characteristics of the patients in the cohort to which the patient is assigned. Accordingly, the trained machine learning model 2013 may reassign the patient to another cohort that includes qualifying characteristics the patient’s characteristics. As such, the trained machine learning model 2013 may select a new treatment plan from the new cohort and control, based on the new treatment plan, the treatment apparatus 2070.

[0545] In some embodiments, prior to controlling the treatment apparatus 2070, the server 2030 may provide the new treatment plan 2800 to the assistant interface 2094 for presentation in the patient profile 2130. As depicted, the patient profile 2130 indicates “The characteristics of the patient have changed and now match characteristics of users in Cohort B. The following treatment plan is recommended for the patient based on his characteristics and desired results.” Then, the patient profile 2130 presents the new treatment plan 2800 (“Patient X should use treatment apparatus for 10 minutes a day for 3 days to achieve an increased range of motion of L%” The assistant (medical professional) may select the new treatment plan 2800, and the server 2030 may receive the selection. The server 2030 may control the treatment apparatus 2070 based on the new treatment plan 2800. In some embodiments, the new treatment plan 2800 may be transmitted to the patient interface 2050 such that the patient may view the details of the new treatment plan 2800.

[0546] FIG. 22 shows an example embodiment of a method 2900 for selecting, based on assigning a patient to a cohort, a treatment plan for the patient and controlling, based on the treatment plan, a treatment apparatus according to the present disclosure. The method 2900 is performed by processing logic that may include hardware (circuitry, dedicated logic, etc.), software (such as is ran on a general-purpose computer system or a dedicated machine), or a combination of both. The method 2900 and/or each of its individual functions, routines, subroutines, or operations may be performed by one or more processors of a computing device (e.g., any component of FIGURE 14, such as server 2030 executing the artificial intelligence engine 2011). In certain implementations, the method 2900 may be performed by a single processing thread. Alternatively, the method 2800 may be performed by two or more processing threads, each thread implementing one or more individual functions, routines, subroutines, or operations of the methods.

[0547] For simplicity of explanation, the method 2900 is depicted and described as a series of operations. However, operations in accordance with this disclosure can occur in various orders and/or concurrently, and/or with other operations not presented and described herein. For example, the operations depicted in the method 2900 may occur in combination with any other operation of any other method disclosed herein. Furthermore, not all illustrated operations may be required to implement the method 2900 in accordance with the disclosed subject matter. In addition, those skilled in the art will understand and appreciate that the method 2900 could alternatively be represented as a series of interrelated states via a state diagram or events.

[0548] At 2902, the processing device may receive first data pertaining to a first user that uses a treatment apparatus 2070 to perform a treatment plan. The first data may include characteristics of the first user, the treatment plan, and a result of the treatment plan.

[0549] At 2904, the processing device may assign, based on the first data, the first user to a first cohort representing people having similarities to at least some of the characteristics of the first user, the treatment plan, and the result of the treatment plan.

[0550] At 2906, the processing device may receive second data pertaining to a second user. The second data may include characteristics of the second user. The characteristics of the first user and the second user may include personal information, performance information, measurement information, or some combination thereof. In some embodiments, the personal information may include an age, a weight, a gender, a height, a body mass index, a medical condition, a familial medication history, an injury, or a medical procedure. In some embodiments, the performance information may include an elapsed time of using the treatment apparatus, an amount of force exerted on a portion of the treatment apparatus, a range of motion achieved on the treatment apparatus, a movement speed of a portion of the treatment apparatus, an indication of a set of pain levels using the treatment apparatus, or some combination thereof. In some embodiments, the measurement information may include a vital sign, a respiration rate, a heartrate, a temperature, or some combination thereof.

[0551] At 2908, the processing device may determine whether at least some of the characteristics of the second user match with at least some of the characteristics of the first user assigned to the first cohort. In some embodiments, one or more machine learning models may be trained to determine whether at least the characteristics of the second user match the characteristics of the first user assigned to the first cohort. [0552] At 2910, responsive to determining the at least some of the characteristics of the second user match with at least some of the characteristics of the first user, the processing device may assign the second user to the first cohort and select, via a trained machine learning model, the treatment plan for the second user. In some embodiments, the trained machine learning model is trained, using at least the first data, to compare, in real time or near real-time, the second data of the second user to a set of data stored in a set of cohorts and select the treatment plan that leads to a desired result and that includes characteristics that match the second characteristics of the second user. The set of cohorts may include the first cohort.

[0553] The treatment plan may include a treatment protocol that specifies using the treatment apparatus 2070 to perform certain exercises for certain lengths of time and a periodicity for performing the exercises. The treatment protocol may also specify parameters of the treatment apparatus 2070 for each of the exercises. For example, a two-week treatment protocol for a person having certain characteristics (e.g., respiration, weight, age, injury, current range of motion, heartrate, etc.) may specify the exercises for a first week and a second week. The exercise for the first week may include pedaling a bicycle for a 10-minute time period where the pedals gradually increase or decrease a range of motion every 1 minute throughout the 10-minute time period. The exercise for the second week may include pedaling a bicycle for a 5 -minute time period where the pedals aggressively increase or decrease a range of motion every 1 minute throughout the 10-minute time period. [0554] At 2912, the processing device may control, based on the treatment plan, the treatment apparatus 2070 while the second user uses the treatment apparatus. In some embodiments, the controlling may be performed by the server 2030 distal from the treatment apparatus 2070 (e.g., during a telemedicine session). Controlling the treatment apparatus 2070 distally may include the server 2030 transmitting, based on the treatment plan, a control instruction to change a parameter of the treatment apparatus 2070 at a particular time to increase a likelihood of a positive effect of continuing to use the treatment apparatus or to decrease a likelihood of a negative effect of continuing to use the treatment apparatus. For example, the treatment plan may include information (based on historical information of people having certain characteristics and performing exercises in the treatment plan) indicating there may be diminishing returns after a certain amount of time of performing a certain exercise. Accordingly, the server 2030, executing one or more machine learning models 2013, may transmit a control signal to the treatment apparatus 2070 to cause the treatment apparatus 2070 to change a parameter (e.g., slow down, stop, etc.).

[0555] In some embodiments, the treatment apparatus used by the first user and the treatment apparatus used by the second user may be the same, or the treatment apparatus used by the first user and the treatment apparatus used by the second user may be different. For example, if the first user and the second user are members of a family, then they may use the same treatment apparatus. If the first user and the second user live in different residences, then the first user and the second user may use different treatment apparatuses.

[0556] In some embodiments, the processing device may continue to receive data while the second user uses the treatment apparatus 2070 to perform the treatment plan. The data received may include characteristics of the second user while the second user uses the treatment apparatus 2070 to perform the treatment plan. The characteristics may include information pertaining to measurements (e.g., respiration, heartrate, temperature, perspiration) and performance (e.g., range of motion, force exerted on a portion of the treatment apparatus 2070, speed of actuating a portion of the treatment apparatus 2070, etc.). The data may indicate that the second user is improving (e.g., maintaining a desired speed of the treatment plan, range of motion, and/or force) as expected in view of the treatment plan for a person having similar data. Accordingly, the processing device may adjust, via a trained machine learning model 2013, based on the data and the treatment plan, a parameter of the treatment apparatus 2070. For example, the data may indicate the second user is pedaling a portion of the treatment apparatus 2070 for 3 minutes at a certain speed. Thus, the machine learning model may adjust, based on the data and the treatment plan, an amount of resistance of the pedals to attempt to cause the second user to achieve a certain result (e.g., strengthen one or more muscles). The certain result may have been achieved by other users with similar data (e.g., characteristics including performance, measurements, etc.) exhibited by the second user at a particular point in a treatment plan.

[0557] In some embodiments, the processing device may receive, from the treatment apparatus 2070, data pertaining to second characteristics of the second user while the second user uses the treatment apparatus 2070 to perform the treatment plan. The second characteristics may include information pertaining to measurements (e.g., respiration, heartrate, temperature, perspiration) and performance (e.g., range of motion, force exerted on a portion of the treatment apparatus 2070, speed of actuating a portion of the treatment apparatus 2070, etc.) of the second user as the second user uses the treatment apparatus 2070 to perform the treatment plan. In some embodiments, the processing device may determine, based on the characteristics, that the second user is improving faster than expected for the treatment plan or is not improving (e.g., unable to maintain a desired speed of the treatment plan, range of motion, and/or force) as expected for the treatment plan.

[0558] The processing device may determine that the second characteristics of the second user match characteristics of a third user assigned to a second cohort. The second cohort may include data for people having different characteristics than the cohort to which the second user was initially assigned. Responsive to determining the second characteristics of the second user match the characteristics of the third user, the processing device may assign the second user to the second cohort and select, via the trained machine learning model, a second treatment plan for the second user. Accordingly, the treatment plans for a user using the treatment apparatus 2070 may be dynamically adjusted, in real-time while the user is using the treatment apparatus 2070, to best fit the characteristics of the second user and enhance a likelihood the second user achieves a desired result experienced by other people in a particular cohort to which the second user is assigned. The second treatment plan may have been performed by the third user with similar characteristics to the second user, and as a result of performing the second treatment plan, the third user may have achieved a desired result. The processing device may control, based on the second treatment plan, the treatment apparatus 2070 while the second user uses the treatment apparatus.

[0559] In some embodiments, responsive to determining the characteristics of the second user do not match the characteristics of the first user, the processing device may determine whether at least the characteristics of the second user match characteristics of a third user assigned to a second cohort. Responsive to determining the characteristics of the second user match the characteristics of the third user, the processing device may assign the second user to the second cohort and select, via the trained machine learning model, a second treatment plan for the second user. The second treatment plan may have been performed by the third user with similar characteristics to the second user, and as a result of performing the second treatment plan, the third user may have achieved a desired result. The processing device may control, based on the second treatment plan, the treatment apparatus 2070 while the second user uses the treatment apparatus. [0560] FIG. 23 shows an example embodiment of a method 21000 for presenting, during a telemedicine session, the recommended treatment plan to a medical professional according to the present disclosure. Method 21000 includes operations performed by processors of a computing device (e.g., any component of FIG. 14, such as server 2030 executing the artificial intelligence engine 2011). In some embodiments, one or more operations of the method 21000 are implemented in computer instructions stored on a memory device and executed by a processing device. The method 21000 may be performed in the same or a similar manner as described above in regard to method 2900. The operations of the method 21000 may be performed in some combination with any of the operations of any of the methods described herein.

[0561] In some embodiments, the method 21000 may occur after 2910 and prior to 2912 in the method 2900 depicted in FIG. 22. That is, the method 21000 may occur prior to the server 2030 executing the one or more machine learning models 2013 controlling the treatment apparatus 2070.

[0562] Regarding the method 21000, at 21002, prior to controlling the treatment apparatus 2070 while the second user uses the treatment apparatus 2070, the processing device may provide, during a telemedicine or telehealth session, a recommendation pertaining to the treatment plan to a computing device (e.g., assistant interface 2094) of a medical professional. The recommendation may be presented on a display screen of the computing device in real-time (e.g., less than 2 seconds) in a portion of the display screen while another portion of the display screen presents video of a user (e.g., patient).

[0563] At 21004, the processing device may receive, from the computing device of the medical professional, a selection of the treatment plan. The medical professional may use any suitable input peripheral (e.g., mouse, keyboard, microphone, touchpad, etc.) to select the recommended treatment plan. The computing device may transmit the selection to the processing device of the server 2030, which receives the selection. There may any suitable number of treatment plans presented on the display screen. Each of the treatment plans recommended may provide different results and the medical professional may consult, during the telemedicine session, with the user to discuss which result the user desires. In some embodiments, the recommended treatment plans may only be presented on the computing device of the medical professional and not on the computing device of the user (patient interface 2050). In some embodiments, the medical professional may choose an option presented on the assistant interface 94. The option may cause the treatment plans to be transmitted to the patient interface 2050 for presentation. In this way, during the telemedicine session, the medical professional and the user may view the treatment plans at the same time in real-time or in near real-time, which may provide for an enhanced user experience for the user using the computing device. After the selection of the treatment plan is received at the server 2030, at 21006, the processing device may control, based on the selected treatment plan, the treatment apparatus while the second user uses the treatment apparatus 70.

[0564] FIG. 24 shows an example computer system 21100 which can perform any one or more of the methods described herein, in accordance with one or more aspects of the present disclosure. In one example, computer system 21100 may include a computing device and correspond to the assistance interface 2094, reporting interface 2092, supervisory interface 2090, clinician interface 2020, server 2030 (including the AI engine 2011), patient interface 2050, ambulatory sensor 2082, goniometer 2084, treatment apparatus 2070, pressure sensor 2086, or any suitable component of FIG. 14. The computer system 21100 may be capable of executing instructions implementing the one or more machine learning models 2013 of the artificial intelligence engine 2011 of FIG. 14. The computer system may be connected (e.g., networked) to other computer systems in a LAN, an intranet, an extranet, or the Internet, including via the cloud or a peer-to-peer network. The computer system may operate in the capacity of a server in a client-server network environment. The computer system may be a personal computer (PC), a tablet computer, a wearable (e.g., wristband), a set-top box (STB), a personal Digital Assistant (PDA), a mobile phone, a camera, a video camera, an Internet of Things (IoT) device, or any device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that device. Further, while only a single computer system is illustrated, the term “computer” shall also be taken to include any collection of computers that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methods discussed herein.

[0565] The computer system 21100 includes a processing device 21102, a main memory 21104 (e.g., read only memory (ROM), flash memory, solid state drives (SSDs), dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM)), a static memory 21106 (e.g., flash memory, solid state drives (SSDs), static random access memory (SRAM)), and a data storage device 21108, which communicate with each other via a bus 21110.

[0566] Processing device 21102 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device 21102 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets orprocessors implementing a combination of instruction sets. The processing device 21402 may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a system on a chip, a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processing device 21402 is configured to execute instructions for performing any of the operations and steps discussed herein.

[0567] The computer system 21100 may further include a network interface device 21112. The computer system 21100 also may include a video display 21114 (e.g., a liquid crystal display (LCD), a light-emitting diode (LED), an organic light-emitting diode (OLED), a quantum LED, a cathode ray tube (CRT), a shadow mask CRT, an aperture grille CRT, a monochrome CRT), one or more input devices 21116 (e.g., a keyboard and/or a mouse or a gaming-like control), and one or more speakers 21118 (e.g., a speaker). In one illustrative example, the video display 21114 and the input device(s) 21116 may be combined into a single component or device (e.g., an LCD touch screen).

[0568] The data storage device 21116 may include a computer-readable medium 21120 on which the instructions 21122 embodying any one or more of the methods, operations, or functions described herein is stored. The instructions 21122 may also reside, completely or at least partially, within the main memory 21104 and/or within the processing device 21102 during execution thereof by the computer system 21100. As such, the main memory 21104 and the processing device 21102 also constitute computer-readable media. The instructions 21122 may further be transmitted or received over a network via the network interface device 21112. [0569] While the computer-readable storage medium 21120 is shown in the illustrative examples to be a single medium, the term “computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer-readable storage medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure. The term “computer-readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media.

[0570] Clause 1.1. A method comprising:

[0571] receiving first data pertaining to a first user that uses a treatment apparatus to perform a treatment plan, wherein the first data comprises characteristics of the first user, the treatment plan, and a result of the treatment plan;

[0572] assigning, based on the first data, the first user to a first cohort representing people having similarities to the characteristics of the first user;

[0573] receiving second data pertaining to a second user, wherein the second data comprises characteristics of the second user;

[0574] determining whether at least some of the characteristics of the second user match with at least some of the characteristics of the first user assigned to the first cohort; and

[0575] responsive to determining at least some of the characteristics of the second user match at least some of the characteristics of the first user, assigning the second user to the first cohort and selecting, via a trained machine learning model, the treatment plan for the second user.

[0576] Clause 2.1. The method of any preceding clause, further comprising controlling, based on the treatment plan, the treatment apparatus while the second user uses the treatment apparatus.

[0577] Clause 3.1. The method of any preceding clause, further comprising:

[0578] prior to controlling the treatment apparatus while the second user uses the treatment apparatus, providing to a computing device of a medical professional, during a telemedicine session, a recommendation pertaining to the treatment plan;

[0579] receiving, from the computing device, a selection of the treatment plan; and

[0580] controlling, based on the treatment plan, the treatment apparatus while the second user uses the treatment apparatus.

[0581] Clause 4.1. The method of any preceding clause, further comprising:

[0582] receiving, from the treatment apparatus, third data pertaining to at least some of second characteristics of the second user while the second user uses the treatment apparatus to perform the treatment plan; and

[0583] adjusting, via the trained machine learning model, based at least in part upon the third data and the treatment plan, a parameter of the treatment apparatus.

[0584] Clause 5.1. The method of any preceding clause, further comprising:

[0585] receiving, from the treatment apparatus, third data pertaining to at least some of second characteristics of the second user while the second user uses the treatment apparatus to perform the treatment plan;

[0586] determining that the at least some of second characteristics of the second user match at least some of characteristics of a third user assigned to a second cohort; [0587] responsive to determining the at least some of second characteristics of the second user match the at least some of characteristics of the third user, assigning the second user to the second cohort and selecting, via the trained machine learning model, a second treatment plan for the second user, wherein the second treatment plan was performed by the third user; and

[0588] controlling, based on the second treatment plan, the treatment apparatus while the second user uses the treatment apparatus.

[0589] Clause 6.1. The method of any preceding clause, wherein the treatment apparatus used by the user and the treatment apparatus used by the second user are the same, or the treatment apparatus used by the user and the treatment apparatus used by the second user are different.

[0590] Clause 7.1. The method of any preceding clause, wherein the controlling is performed by a server distal from the treatment apparatus.

[0591] Clause 8.1. The method of any preceding clause, wherein the characteristics of the first user and the second user comprises personal information, performance information, measurement information, or some combination thereof, wherein:

[0592] the personal information comprises an age, a weight, a gender, a height, a body mass index, a medical condition, a familial medication history, an injury, a medical procedure, or some combination thereof, [0593] the performance information comprises an elapsed time of using the treatment apparatus, an amount of force exerted on a portion of the treatment apparatus, a range of motion achieved on the treatment apparatus, a movement speed of a portion of the treatment apparatus, an indication of a plurality of pain levels using the treatment apparatus, or some combination thereof, and

[0594] the measurement information comprises a vital sign, a respiration rate, a heartrate, a temperature, or some combination thereof.

[0595] Clause 9.1. The method of any preceding clause, wherein the trained machine learning model is trained, using at least the first data, to compare, in real-time, the second data of the second user to a plurality of data stored in a plurality of cohorts and select the treatment plan that leads to a desired result and that includes characteristics that match at least some of the second characteristics of the second user, wherein the plurality of cohorts includes the first cohort.

[0596] Clause 10.1. The method of any preceding clause, wherein controlling, based on the second treatment plan, the treatment apparatus while the second user uses the treatment apparatus further comprises: [0597] transmitting, based on the treatment plan, a control instruction to change a parameter of the treatment apparatus at a particular time to increase a likelihood of a positive effect of continuing to use the treatment apparatus or to decrease a likelihood of a negative effect of continuing to use the treatment apparatus. [0598] Clause 11.1. The method of any preceding clause, further comprising:

[0599] responsive to determining the at least some of the characteristics of the second user do not match with the at least some of the characteristics of the first user, determining whether at least the at least some of the characteristics of the second user match at least some of the characteristics of a third user assigned to a second cohort;

[0600] responsive to determining the at least some of the characteristics of the second user match the at least some of the characteristics of the third user, assigning the second user to the second cohort and selecting, via the trained machine learning model, a second treatment plan for the second user, wherein the second treatment plan was performed by the third user; and

[0601] controlling, based on the second treatment plan, the treatment apparatus while the second user uses the treatment apparatus.

[0602] Clause 12..1 A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to:

[0603] receive first data pertaining to a first user that uses a treatment apparatus to perform a treatment plan, wherein the first data comprises characteristics of the first user, the treatment plan, and a result of the treatment plan;

[0604] assign, based on the first data, the first user to a first cohort representing people having similarities to the characteristics of the first user;

[0605] receive second data pertaining to a second user, wherein the second data comprises characteristics of the second user;

[0606] determine whether at least some of the characteristics of the second user match with at least some of the characteristics of the first user assigned to the first cohort; and

[0607] responsive to determining at least some of the characteristics of the second user match at least some of the characteristics of the first user, assign the second user to the first cohort and selecting, via a trained machine learning model, the treatment plan for the second user.

[0608] Clause 13.1. The computer-readable medium of any preceding clause, wherein the processing device is further to control, based on the treatment plan, the treatment apparatus while the second user uses the treatment apparatus.

[0609] Clause 14.1. The computer-readable medium of any preceding clause, wherein the processing device is further to:

[0610] prior to controlling the treatment apparatus while the second user uses the treatment apparatus, provide to a computing device of a medical professional, during a telemedicine session, a recommendation pertaining to the treatment plan;

[0611] receive, from the computing device, a selection of the treatment plan; and

[0612] control, based on the treatment plan, the treatment apparatus while the second user uses the treatment apparatus.

[0613] Clause 15.1. The computer-readable medium of any preceding clause, wherein the processing device is further to:

[0614] receive, from the treatment apparatus, third data pertaining to at least some of second characteristics of the second user while the second user uses the treatment apparatus to perform the treatment plan; and [0615] adjust, via the trained machine learning model, based at least in part upon the third data and the treatment plan, a parameter of the treatment apparatus.

[0616] Clause 16.1. The computer-readable medium of any preceding clause, wherein the processing device is further to: [0617] receive, from the treatment apparatus, third data pertaining to at least some of second characteristics of the second user while the second user uses the treatment apparatus to perform the treatment plan;

[0618] determine that the at least some of second characteristics of the second user match at least some of characteristics of a third user assigned to a second cohort;

[0619] responsive to determining the at least some of second characteristics of the second user match the at least some of characteristics of the third user, assign the second user to the second cohort and selecting, via the trained machine learning model, a second treatment plan for the second user, wherein the second treatment plan was performed by the third user; and

[0620] control, based on the second treatment plan, the treatment apparatus while the second user uses the treatment apparatus.

[0621] Clause 17.1. The method of any preceding clause, wherein the treatment apparatus used by the user and the treatment apparatus used by the second user are the same, or the treatment apparatus used by the user and the treatment apparatus used by the second user are different.

[0622] Clause 18.1. A system comprising:

[0623] a memory device storing instructions;

[0624] a processing device communicatively coupled to the memory device, the processing device executes the instructions to:

[0625] receive first data pertaining to a first user that uses a treatment apparatus to perform a treatment plan, wherein the first data comprises characteristics of the first user, the treatment plan, and a result of the treatment plan;

[0626] assign, based on the first data, the first user to a first cohort representing people having similarities to the characteristics of the first user;

[0627] receive second data pertaining to a second user, wherein the second data comprises characteristics of the second user;

[0628] determine whether at least some of the characteristics of the second user match with at least some of the characteristics of the first user assigned to the first cohort; and

[0629] responsive to determining at least some of the characteristics of the second user match at least some of the characteristics of the first user, assign the second user to the first cohort and selecting, via a trained machine learning model, the treatment plan for the second user.

[0630] Clause 19.1. The system of any preceding clause, wherein the processing device is further to control, based on the treatment plan, the treatment apparatus while the second user uses the treatment apparatus. [0631] Clause 20.1. The system of any preceding clause, wherein the processing device is further to : [0632] prior to controlling the treatment apparatus while the second user uses the treatment apparatus, provide to a computing device of a medical professional, during a telemedicine session, a recommendation pertaining to the treatment plan;

[0633] receive, from the computing device, a selection of the treatment plan; and

[0634] control, based on the treatment plan, the treatment apparatus while the second user uses the treatment apparatus. REMOTE EXAMINATION THROUGH AUGMENTED REALITY

[0635] Determining optimal remote examination procedures to create an optimal treatment plan for a patient having certain characteristics (e.g., vital-sign or other measurements; performance; demographic; psychographic; geographic; diagnostic; measurement- or test-based; medically historic; behavioral historic; cognitive; etiologic; cohort-associative; differentially diagnostic; surgical, physically therapeutic, pharmacologic and other treatment(s) recommended; etc.) may be a technically challenging problem. For example, a multitude of information may be considered when determining a treatment plan, which may result in inefficiencies and inaccuracies in the treatment plan selection process. In a rehabilitative setting, some of the multitude of information considered may include characteristics of the patient such as personal information, performance information, and measurement information. The personal information may include, e.g., demographic, psychographic or other information, such as an age, a weight, a gender, a height, a body mass index, a medical condition, a familial medication history, an injury, a medical procedure, a medication prescribed, or some combination thereof. The performance information may include, e.g., an elapsed time of using a treatment device, an amount of force exerted on a portion of the treatment device, a range of motion achieved on the treatment device, a movement speed of a portion of the treatment device, a duration of use of the treatment device, an indication of a plurality of pain levels using the treatment device, or some combination thereof. The measurement information may include, e.g., a vital sign, a respiration rate, a heartrate, a temperature, a blood pressure, a glucose level or other biomarker, or some combination thereof. It may be desirable to process and analyze the characteristics of a multitude of patients, the treatment plans performed for those patients, and the results of the treatment plans for those patients.

[0636] Further, another technical problem may involve distally treating, via a computing device during a telemedicine session, a patient from a location different than a location at which the patient is located. An additional technical problem is controlling or enabling, from the different location, the control of a treatment apparatus used by the patient at the patient’s location. Oftentimes, when a patient undergoes rehabilitative surgery (e.g., knee surgery), a medical professional may prescribe a treatment apparatus to the patient to use to perform a treatment protocol at their residence or at any mobile location or temporary domicile. A medical professional may refer to a doctor, physician assistant, nurse, chiropractor, dentist, physical therapist, acupuncturist, physical trainer, or the like. A medical professional may refer to any person with a credential, license, degree, or the like in the field of medicine, physical therapy, rehabilitation, or the like.

[0637] When the healthcare provider is located in a location different from the patient and the treatment device, it may be technically challenging for the healthcare provider to monitor the patient’s actual progress (as opposed to relying on the patient’ s word about their progress) in using the treatment device, modify the treatment plan according to the patient’s progress, adapt the treatment device to the personal characteristics of the patient as the patient performs the treatment plan, and the like.

[0638] Further, in addition to the information described above, determining optimal examination procedures for a particular ailment (e.g., injury, disease, any applicable medical condition, etc.) may include physically examining the injured body part of a patient. The healthcare provider, such as a physician or a physical therapist, may visually inspect the injured body part (e.g., a knee joint). The inspection may include looking for signs of inflammation or injury (e.g., swelling, redness, and warmth), deformity (e.g., symmetrical joints and abnormal contours and/or appearance), or any other suitable observation. To determine limitations of the injured body part, the healthcare provider may observe the injured body part as the patient attempts to perform normal activity (e.g., bending and extending the knee and gauging any limitations to the range of motion of the injured knee). The healthcare provide may use one or more hands and/or fingers to touch the injured body part. By applying pressure to the injured body part, the healthcare provider can obtain information pertaining to the extent of the injury. For example, the healthcare provider’s fingers may palpate the injured body part to determine if there is point tenderness, warmth, weakness, strength, or to make any other suitable observation. [0639] It may be desirable to compare characteristics of the injured body part with characteristics of a corresponding non-injured body part to determine what an optimal treatment plan for the patient may be such that the patient can obtain a desired result. Thus, the healthcare provider may examine a corresponding non- injured body part of the patient. For example, the healthcare provider’s fingers may palpate a non-injured body part (e.g., a left knee) to determine a baseline of how the patient’s non-injured body part feels and functions. The healthcare provider may use the results of the examination of the non-injured body part to determine the extent of the injury to the corresponding injured body part (e.g., a right knee). Additionally, injured body parts may affect other body parts (e.g., a knee injury may limit the use of the affected leg, leading to atrophy of leg muscles). Thus, the healthcare provider may also examine additional body parts of the patient for evidence of atrophy of or injury to surrounding ligaments, tendons, bones, and muscles, examples of muscles being such as quadriceps, hamstrings, or calf muscle groups of the leg with the knee injury. The healthcare provider may also obtain information as to a pain level of the patient before, during, and/or after the examination.

[0640] The healthcare provider can use the information obtained from the examination (e.g., the results of the examination) to determine a proper treatment plan for the patient. If the healthcare provider cannot conduct a physical examination of the one or more body parts of the patient, the healthcare provider may not be able to fully assess the patient’s injury and the treatment plan may not be optimal. Accordingly, embodiments of the present disclosure pertain to systems and methods for conducting a remote examination of a patient. The remote examination system provides the healthcare provider with the ability to conduct a remote examination of the patient, not only by communicating with the patient, but by virtually observing and/or feeling the patient’s one or more body parts.

[0641] In some embodiments, the systems and methods described herein may be configured for remote examination of a patient. For example, the systems and methods may be configured to use a treatment device configured to be manipulated by an individual while performing a treatment plan. The individual may include a user, patient, or other a person using the treatment device to perform various exercises for prehabilitation, rehabilitation, stretch training, and the like. The systems and methods described herein may be configured to use and/or provide a patient interface comprising an output device configured to present telemedicine information associated with a telemedicine session.

[0642] In some embodiments, the systems and methods described herein may be configured for remote examination of a patient. For example, the systems and methods may be configured to use a treatment device configured to be manipulated by a healthcare provider while the patient is performing a treatment plan. The systems and methods described herein may be configured to receive slave sensor data from the one or more slave sensors, use a manipulation of the master device to generate a manipulation instruction, transmit the manipulation instruction, and use the manipulation instruction to cause the slave pressure system to activate. Any or all of the methods described may be implemented during a telemedicine session or at any other desired time.

[0643] In some embodiments, the treatment devices may be communicatively coupled to a server. Characteristics of the patients, including the treatment data, may be collected before, during, and/or after the patients perform the treatment plans. For example, any or each of the personal information, the performance information, and the measurement information may be collected before, during, and/or after a patient performs the treatment plans. The results (e.g., improved performance or decreased performance) of performing each exercise may be collected from the treatment device throughout the treatment plan and after the treatment plan is performed. The parameters, settings, configurations, etc. (e.g., position of pedal, amount of resistance, etc.) of the treatment device may be collected before, during, and/or after the treatment plan is performed.

[0644] Each characteristic of the patient, each result, and each parameter, setting, configuration, etc. may be timestamped and may be correlated with a particular step or set of steps in the treatment plan. Such a technique may enable the determination of which steps in the treatment plan lead to desired results (e.g., improved muscle strength, range of motion, etc.) and which steps lead to diminishing returns (e.g., continuing to exercise after 3 minutes actually delays or harms recovery).

[0645] Data may be collected from the treatment devices and/or any suitable computing device (e.g., computing devices where personal information is entered, such as the interface of the computing device described herein, a clinician interface, patient interface, and the like) over time as the patients use the treatment devices to perform the various treatment plans. The data that may be collected may include the characteristics of the patients, the treatment plans performed by the patients, the results of the treatment plans, any of the data described herein, any other suitable data, or a combination thereof.

[0646] In some embodiments, the data may be processed to group certain people into cohorts. The people may be grouped by people having certain or selected similar characteristics, treatment plans, and results of performing the treatment plans. For example, athletic people having no medical conditions who perform a treatment plan (e.g., use the treatment device for 30 minutes a day 5 times a week for 3 weeks) and who fully recover may be grouped into a first cohort. Older people who are classified obese and who perform a treatment plan (e.g., use the treatment plan for 10 minutes a day 3 times a week for 4 weeks) and who improve their range of motion by 75 percent may be grouped into a second cohort.

[0647] In some embodiments, an artificial intelligence engine may include one or more machine learning models that are trained using the cohorts. In some embodiments, the artificial intelligence engine may be used to identify trends and/or patterns and to define new cohorts based on achieving desired results from the treatment plans and machine learning models associated therewith may be trained to identify such trends and/or patterns and to recommend and rank the desirability of the new cohorts. For example, the one or more machine learning models may be trained to receive an input of characteristics of a new patient and to output a treatment plan for the patient that results in a desired result. The machine learning models may match a pattern between the characteristics of the new patient and at least one patient of the patients included in a particular cohort. When a pattern is matched, the machine learning models may assign the new patient to the particular cohort and select the treatment plan associated with the at least one patient. The artificial intelligence engine may be configured to control, distally and based on the treatment plan, the treatment device while the new patient uses the treatment device to perform the treatment plan.

[0648] As may be appreciated, the characteristics of the new patient (e.g., a new user) may change as the new patient uses the treatment device to perform the treatment plan. For example, the performance of the patient may improve quicker than expected for people in the cohort to which the new patient is currently assigned. Accordingly, the machine learning models may be trained to dynamically reassign, based on the changed characteristics, the new patient to a different cohort that includes people having characteristics similar to the now-changed characteristics as the new patient. For example, a clinically obese patient may lose weight and no longer meet the weight criterion for the initial cohort, result in the patient’ s being reassigned to a different cohort with a different weight criterion.

[0649] A different treatment plan may be selected for the new patient, and the treatment device may be controlled, distally (e.g., which may be referred to as remotely) and based on the different treatment plan, while the new patient uses the treatment device to perform the treatment plan. Such techniques may provide the technical solution of distally controlling a treatment device.

[0650] Further, the systems and methods described herein may lead to faster recovery times and/or better results for the patients because the treatment plan that most accurately fits their characteristics is selected and implemented, in real-time, at any given moment. “Real-time” may also refer to near real-time, which may be less than 10 seconds or any reasonably proximate difference between two different times. As described herein, the term “results” may refer to medical results or medical outcomes. Results and outcomes may refer to responses to medical actions. The term “medical action(s)” may refer to any suitable action performed by the medical professional, and such action or actions may include diagnoses, prescription of treatment plans, prescription of treatment devices, and the making, composing and/or executing of appointments, telemedicine sessions, prescription of medicines, telephone calls, emails, text messages, and the like.

[0651] Depending on what result is desired, the artificial intelligence engine may be trained to output several treatment plans. For example, one result may include recovering to a threshold level (e.g., 75% range of motion) in a fastest amount of time, while another result may include fully recovering (e.g., 100% range of motion) regardless of the amount of time. The data obtained from the patients and sorted into cohorts may indicate that a first treatment plan provides the first result for people with characteristics similar to the patient’ s, and that a second treatment plan provides the second result for people with characteristics similar to the patient. [0652] Further, the artificial intelligence engine may be trained to output treatment plans that are not optimal i.e., sub-optimal, nonstandard, or otherwise excluded (all referred to, without limitation, as “excluded treatment plans”) for the patient. For example, if a patient has high blood pressure, a particular exercise may not be approved or suitable for the patient as it may put the patient at unnecessary risk or even induce a hypertensive crisis and, accordingly, that exercise may be flagged in the excluded treatment plan for the patient. In some embodiments, the artificial intelligence engine may monitor the treatment data received while the patient (e.g., the user) with, for example, high blood pressure, uses the treatment device to perform an appropriate treatment plan and may modify the appropriate treatment plan to include features of an excluded treatment plan that may provide beneficial results for the patient if the treatment data indicates the patient is handling the appropriate treatment plan without aggravating, for example, the high blood pressure condition of the patient. In some embodiments, the artificial intelligence engine may modify the treatment plan if the monitored data shows the plan to be inappropriate or counterproductive for the user.

[0653] In some embodiments, the treatment plans and/or excluded treatment plans may be presented, during a telemedicine or telehealth session, to a healthcare provider. The healthcare provider may select a particular treatment plan for the patient to cause that treatment plan to be transmitted to the patient and/or to control, based on the treatment plan, the treatment device. In some embodiments, to facilitate telehealth or telemedicine applications, including remote diagnoses, determination of treatment plans and rehabilitative and/or pharmacologic prescriptions, the artificial intelligence engine may receive and/or operate distally from the patient and the treatment device.

[0654] In such cases, the recommended treatment plans and/or excluded treatment plans may be presented simultaneously with a video of the patient in real-time or near real-time during a telemedicine or telehealth session on a user interface of a computing device of a medical professional. The video may also be accompanied by audio, text and other multimedia information and/or other sensorial or perceptive (e.g., tactile, gustatory, haptic, pressure-sensing-based or electromagnetic (e.g., neurostimulation). Real-time may refer to less than or equal to 2 seconds. Near real-time may refer to any interaction of a sufficiently short time to enable two individuals to engage in a dialogue via such user interface, and will generally be less than 10 seconds (or any suitably proximate difference between two different times) but greater than 2 seconds.

[0655] Presenting the treatment plans generated by the artificial intelligence engine concurrently with a presentation of the patient video may provide an enhanced user interface because the healthcare provider may continue to visually and/or otherwise communicate with the patient while also reviewing the treatment plans on the same user interface. The enhanced user interface may improve the healthcare provider’s experience using the computing device and may encourage the healthcare provider to reuse the user interface. Such a technique may also reduce computing resources (e.g., processing, memory, network) because the healthcare provider does not have to switch to another user interface screen to enter a query for a treatment plan to recommend based on the characteristics of the patient. The artificial intelligence engine may be configured to provide, dynamically on the fly, the treatment plans and excluded treatment plans.

[0656] In some embodiments, the treatment device may be adaptive and/or personalized because its properties, configurations, and positions may be adapted to the needs of a particular patient. For example, the pedals may be dynamically adjusted on the fly (e.g., via a telemedicine session or based on programmed configurations in response to certain measurements being detected) to increase or decrease a range of motion to comply with a treatment plan designed for the user. In some embodiments, a healthcare provider may adapt, remotely during a telemedicine session, the treatment device to the needs of the patient by causing a control instruction to be transmitted from a server to treatment device. Such adaptive nature may improve the results of recovery for a patient, furthering the goals of personalized medicine, and enabling personalization of the treatment plan on a per-individual basis.

[0657] FIGS. 25-35, discussed below, and the various embodiments used to describe the principles of this disclosure are by way of illustration only and should not be construed in any way to limit the scope of the disclosure.

[0658] FIG. 25 illustrates a high-level component diagram of an illustrative remote examination system 3100 according to certain embodiments of this disclosure. In some embodiments, the remote examination system 3100 may include a slave computing device 3102 communicatively coupled to a slave device, such as a treatment device 3106. The treatment device can include a slave sensor 3108 and a slave pressure system 3110. The slave pressure system can include a slave motor 3112. The remote examination system may also be communicatively coupled to an imaging device 3116. Each of the slave computing device 3102, the treatment device 3106, and the imaging device 3116 may include one or more processing devices, memory devices, and network interface cards. The network interface cards may enable communication via a wireless protocol for transmitting data over short distances, such as Bluetooth, ZigBee, etc. In some embodiments, the slave computing device 3102 is communicatively coupled to the treatment device 3106 and the imaging device 3116 via Bluetooth.

[0659] Additionally, the network interface cards may enable communicating data over long distances, and in one example, the slave computing device 3102 may communicate with a network 3104. The network 3104 may be a public network (e.g., connected to the Internet via wired (Ethernet) or wireless (WiFi)), a private network (e.g., a local area network (LAN) or wide area network (WAN)), or a combination thereof. The slave computing device 3102 may be communicatively coupled with one or more master computing devices 122 and a cloud-based computing system 3142.

[0660] The slave computing device 3102 may be any suitable computing device, such as a laptop, tablet, smartphone, or computer. The slave computing device 3102 may include a display that is capable of presenting a user interface, such as a patient portal 3114. The patient portal 3114 may be implemented in computer instructions stored on the one or more memory devices of the slave computing device 3102 and executable by the one or more processing devices of the slave computing device 3102. The patient portal 3114 may present various screens to a patient that enable the patient to view his or her medical records, a treatment plan, or progress during the treatment plan; to initiate a remote examination session; to control parameters of the treatment device 3106; to view progress of rehabilitation during the remote examination session; or combination thereof. The slave computing device 3102 may also include instructions stored on the one or more memory devices that, when executed by the one or more processing devices of the slave computing device 3102, perform operations to control the treatment device 3106.

[0661] The slave computing device 3102 may execute the patient portal 3114. The patient portal 3114 may be implemented in computer instructions stored on the one or more memory devices of the slave computing device 3102 and executable by the one or more processing devices of the slave computing device 3102. The patient portal 3114 may present various screens to a patient which enable the patient to view a remote examination provided by a healthcare provider, such as a physician or a physical therapist. The patient portal 3114 may also provide remote examination information for a patient to view. The examination information can include a summary of the examination and/or results of the examination in real-time or near real-time, such as measured properties (e.g., angles of bend/extension, pressure exerted on the treatment device 3106, images of the examined/treated body part, vital signs of the patient, such as heartrate, temperature, etc.) of the patient during the examination. The patient portal 3114 may also provide the patient’s health information, such as a health history, a treatment plan, and a progress of the patient throughout the treatment plan. So the examination of the patient may begin, the examination information specific to the patient may be transmitted via the network 3104 to the cloud-based computing system 3142 for storage and/or to the slave computing device 3102. [0662] The treatment device 3106 may be an examination device for a body part of a patient. As illustrated in FIGS. 26A-D, the treatment device 3106 can be configured in alternative arrangements and is not limited to the example embodiments described in this disclosure. Although not illustrated, the treatment device 3106 can include a slave motor 3112 and a motor controller 3118. The treatment device 3106 can include a slave pressure system 3110. The slave pressure system 3110 is any suitable pressure system configured to increase and/or decrease the pressure in the treatment device 3106. For example, the slave pressure system 3110 can comprise the slave motor 3112, the motor controller 3118, and a pump. The motor controller 3118 can activate the slave motor 3112 to cause a pump or any other suitable device to inflate or deflate one or more sections 3210 of the treatment device 3106. The treatment device 3106 can be operatively coupled to one or more slave processing devices. The one or more slave processing devices can be configured to execute instructions in accordance with aspects of this disclosure.

[0663] As illustrated in FIG. 26A, the treatment device 3106 may comprise a brace 3202 (e.g., a knee brace) configured to fit on the patient’ s body part, such as an arm, a wrist, a neck, a torso, a leg, a knee, an ankle, hips, or any other suitable body part. The brace 3202 may include slave sensors 3108. The slave sensors 3108 can be configured to detect information correlating with the patient. For example, the slave sensors 3108 can detect a measured level of force exerted from the patient to the treatment device 3106, a temperature of the one or more body parts in contact with the patient, a movement of the treatment device 3106, any other suitable information, or any combination thereof. The brace 3202 may include sections 3210. The sections 3210 can be formed as one or more chambers. The sections 3210 may be configured to be filled with a liquid (e.g., a gel, air, water, etc.). The sections 3210 may be configured in one or more shapes, such as, but not limited to rectangles, squares, diamonds circles, trapezoids, any other suitable shape, or combination thereof. The sections 3210 may be the same or different sizes. The sections 3210 may be positioned throughout the treatment device 3106. The sections 3210 can be positioned on the brace 3202 above a knee portion, below the knee portion, and along the sides of the knee portion. In some embodiments, the brace 3202 may include sections 3210 positioned adjacent to each other and positioned throughout the brace 3202. The sections 3210 are not limited to the exemplary illustrations in FIG. 28. The brace 3202 may include the one or more materials for the brace 202 and, in some embodiments, straps coupled to the brace 3202. The brace 3202 be formed from metal, foam, plastic, elastic, or any suitable material or combination of materials. The brace 3202 may be formed in any suitable shape, size, or design.

[0664] As illustrated in FIG. 26B, the treatment device 3106 may comprise a cap 3204 that can be configured to fit onto the patient’s head. FIG. 26B illustrates exemplary layers of the treatment device 3106. The treatment device 3106 may include a first layer 3212 and a second layer 3214. The first layer may be an outer later and the second layer 3214 may be an inner layer. The second layer 3214 may include the sections 3210 and one or more sensors 3108. In this example embodiment, the sections 3210 are coupled to and/or from portions of the second layer 3214. The sections 3210 can be configured in a honeycomb pattern. The one or more sensors 3108 may be coupled to the first layer 3212. The first layer 3212 can be coupled to the second layer 3214. The first layer 3212 can be designed to protect the sections 3210 and the sensors 3108. The cap 3204 may include a strap. The cap 3204 and/or the strap be formed from metal, foam, plastic, elastic, or any suitable material or combination of materials. The cap 3204 may be formed in any suitable shape, size, or design. [0665] As illustrated in FIG. 26C, the slave may comprise a mat 3206. The mat 3206 may be configured for a patient to lie or sit down, or to stand upon. The mat 3206 may include one or more sensors 3108. The mat 3206 may include one or more sections 3210. The sections 3210 in the treatment device 3106 can be configured in a square grid pattern. The one or more sensors 3 i08 may be coupled to and/or positioned within the one or more sections 3210. The mat 3206 can be rectangular, circular, square, or any other suitable configuration. The mat 3206 be formed from metal, foam, plastic, elastic, or any suitable material or combination of materials. The mat 3206 may include one or more layers, such as a top layer.

[0666] As illustrated in FIG. 26D, the treatment device 3106 may comprise a wrap 3208. The wrap 3208 may be configured to wrap the wrap 3208 around one or more portions and/or one or more body parts of the patient. For example, the wrap 3208 may be configured to wrap around a person’s torso. The wrap 3208 may include one or more sensors 3108. The wrap 3208 may include one or more sections 3210. The sections 3210 in the treatment device 3106 can be configured in a diamond grid pattern. The one or more sensors 3108 may be coupled to and/or positioned within the one or more sections 3210. The wrap 3208 can be rectangular, circular, square, or any other suitable configuration. The wrap 3208 may include a strap. The wrap 3208 and/or the strap be formed from metal, foam, plastic, elastic, or any suitable material or combination of materials. [0667] As illustrated in FIGS. 32-34, the treatment device may comprise an electromechanical device, such as a physical therapy device. FIG. 32 illustrates a perspective view of an example of a treatment device 3800 according to certain aspects of this disclosure. Specifically, the treatment device 3800 illustrated is an electromechanical device 3802, such as an exercise and rehabilitation device (e.g., a physical therapy device or the like). The electromechanical device 3802 is shown having pedal 3810 on opposite sides that are adjustably positionable relative to one another on respective radially-adjustable couplings 3808. The depicted electromechanical device 3802 is configured as a small and portable unit so that it is easily transported to different locations at which rehabilitation or treatment is to be provided, such as at patients’ homes, alternative care facilities, or the like. The patient may sit in a chair proximate the electromechanical device 3802 to engage the electromechanical device 3802 with the patient’s feet, for example. The electromechanical device 3802 includes a rotary device such as radially-adjustable couplings 3808 or flywheel or the like rotatably mounted such as by a central hub to a frame or other support. The pedals 3810 are configured for interacting with a patient to be rehabilitated and may be configured for use with lower body extremities such as the feet, legs, or upper body extremities, such as the hands, arms, and the like. For example, the pedal 3810 may be a bicycle pedal of the type having a foot support rotatably mounted onto an axle with bearings. The axle may or may not have exposed end threads for engaging a mount on the radially-adjustable coupling 3808 to locate the pedal on the radially-adjustable coupling 3808. The radially-adjustable coupling 3808 may include an actuator configured to radially adjust the location of the pedal to various positions on the radially-adjustable coupling 3808.

[0668] Alternatively, the radially-adjustable coupling 3808 may be configured to have both pedals 3810 on opposite sides of a single coupling 3808. In some embodiments, as depicted, a pair of radially-adjustable couplings 3808 may be spaced apart from one another but interconnected to the electric motor 3806. In the depicted example, the computing device 3102 may be mounted on the frame of the electromechanical device 3802 and may be detachable and held by the user while the user operates the electromechanical device 3802. The computing device 3102 may present the patient portal 3114 and control the operation of the electric motor 3806, as described herein. [0669] In some embodiments, as described in U.S. Patent No. 10,173,094 (U.S. Appl. No. 15/700,293), which is incorporated by reference herein in its entirety for all purposes, the device 3106 may take the form of a traditional exercise/rehabilitation device which is more or less non-portable and remains in a fixed location, such as a rehabilitation clinic or medical practice. The device 3106 may include a seat and be less portable than the device 3106 shown in FIGURE 32. FIG. 32 is not intended to be limiting: the treatment device 3800 may include more or fewer components than those illustrated in FIG. 32.

[0670] FIGS. 33-34 generally illustrate an embodiment of a treatment device, such as a treatment device 3010. More specifically, FIG. 33 generally illustrates a treatment device 3010 in the form of an electromechanical device, such as a stationary cycling machine 3014, which may be called a stationary bike, for short. The stationary cycling machine 3014 includes a set of pedals 3012 each attached to a pedal arm 3020 for rotation about an axle 3016. In some embodiments, and as generally illustrated in FIG. 33, the pedals 3012 are movable on the pedal arm 3020 in order to adjust a range of motion used by the patient in pedaling. For example, the pedals being located inwardly toward the axle 3016 corresponds to a smaller range of motion than when the pedals are located outwardly away from the axle 3016. A pressure sensor 3018 is attached to or embedded within one of the pedals 3012 for measuring an amount of force applied by the patient on the pedal 3102. The pressure sensor 18 may communicate wirelessly to the treatment device 3010 and/or to the patient interface 3026. FIGS. 33-34 are not intended to be limiting: the treatment device 3010 may include more or fewer components than those illustrated in FIGS. 33-34.

[0671] FIG. 35 generally illustrates a person (a patient) using the treatment device of FIG. 33, and showing sensors and various data parameters connected to a patient interface 3026. The example patient interface 3026 is a tablet computer or smartphone, or a phablet, such as an iPad, an iPhone, an Android device, or a Surface tablet, which is held manually by the patient. In some other embodiments, the patient interface 3026 may be embedded within or attached to the treatment device 3010. FIG. 35 generally illustrates the patient wearing the ambulation sensor 3022 on his wrist, with a note showing “STEPS TODAY 1355”, indicating that the ambulation sensor 3022 has recorded and transmitted that step count to the patient interface 3026. FIG. 35 also generally illustrates the patient wearing the goniometer 3024 on his right knee, with a note showing “KNEE ANGLE 72°”, indicating that the goniometer 3024 is measuring and transmitting that knee angle to the patient interface 3026. FIG. 35 generally illustrates a right side of one of the pedals 3012 with a pressure sensor 3018 showing “FORCE 12.5 lbs.”, indicating that the right pedal pressure sensor 3018 is measuring and transmitting that force measurement to the patient interface 3026. FIG. 35 also generally illustrates a left side of one of the pedals 3012 with a pressure sensor 3018 showing “FORCE 27 lbs.”, indicating that the left pedal pressure sensor 3018 is measuring and transmitting that force measurement to the patient interface 3026. FIG. 35 also generally illustrates other patient data, such as an indicator of “SESSION TIME 0:04: 13”, indicating that the patient has been using the treatment device 10 for 4 minutes and 13 seconds. This session time may be determined by the patient interface 3026 based on information received from the treatment device 3010. FIG. 35 also generally illustrates an indicator showing “PAIN LEVEL 3”. Such a pain level may be obtained from the patient in response to a solicitation, such as a question, presented upon the patient interface 3026. The treatment device 3106 may include at least one or more motor controllers 3118 and one or more motors 3112, such as an electric motor. A pump, not illustrated, may be operatively coupled to the motor. The pump may be a hydraulic pump or any other suitable pump. The pump may be configured to increase or decrease pressure within the treatment device 3106. The size and speed of the pump may determine the flow rate (i.e., the speed that the load moves) and the load at the slave motor 3112 may determine the pressure in one or more sections 3210 of the treatment device 3106. The pump can be activated to increase or decrease pressure in the one or more sections 3210. One or more of the sections 3210 may include a sensor 3108. The sensor 3108 can be a sensor for detecting signals, such as a measured level of force, a temperature, or any other suitable signal. The motor controller 3118 may be operatively coupled to the motor 3112 and configured to provide commands to the motor 3112 to control operation of the motor 3112. The motor controller 3118 may include any suitable microcontroller including a circuit board having one or more processing devices, one or more memory devices (e.g., read-only memory (ROM) and/or random access memory (RAM)), one or more network interface cards, and/or programmable input/output peripherals. The motor controller 3118 may provide control signals or commands to drive the motor 3112. The motor 3112 may be powered to drive the pump of the treatment device 106. The motor 3112 may provide the driving force to the pump to increase or decrease pressure at configurable speeds. Further, the treatment device 3106 may include a current shunt to provide resistance to dissipate energy from the motor 3112. In some embodiments, the treatment device 3106 may comprise a haptic system, a pneumatic system, any other suitable system, or combination thereof. For example, the haptic system can include a virtual touch by applying forces, vibrations, or motions to the patient through the treatment device 3106.

[0672] The slave computing device 3102 may be communicatively connected to the treatment device 3106 via a network interface card on the motor controller 3118. The slave computing device 3102 may transmit commands to the motor controller 3118 to control the motor 3112. The network interface card of the motor controller 3118 may receive the commands and transmit the commands to the motor 3112 to drive the motor 3112. In this way, the slave computing device 3102 is operatively coupled to the motor 3112.

[0673] The slave computing device 3102 and/or the motor controller 3118 may be referred to as a control system (e.g., a slave control system) herein. The patient portal 3114 may be referred to as a patient user interface of the control system. The control system may control the motor 3112 to operate in a number of modes: standby, inflate, and deflate. The standby mode may refer to the motor 3112 powering off so it does not provide a driving force to the one or more pumps. For example, if the pump does not receive instructions to inflate or deflate the treatment device 3106, the motor 3112 may remain turned off. In this mode, the treatment device 3106 may not provide additional pressure to the patient’s body part(s).

[0674] The inflate mode may refer to the motor 3112 receiving manipulation instructions comprising measurements of pressure, causing the motor 3112 to drive the one or more pumps coupled to the one or more sections of the treatment device 3106 to inflate the one or more sections. The manipulation instruction may be configurable by the healthcare provider. For example, as the healthcare provider moves a master device 3126, the movement is provided in a manipulation instruction for the motor 3112 to drive the pump to inflate one or more sections of the treatment device 3106. The manipulation instruction may include a pressure gradient to inflate first and second sections in a right side of a knee brace to first and second measured levels of force and inflate a third section in a left side of the knee brace to a third measured level of force. The first measured level of force correlates with the amount of pressure applied to the master device 3126 by the healthcare provider’s first finger. The second measured level of force correlates with the amount of pressure applied to the master device 3126 by the healthcare provider’s second finger. The third measured level of force correlates with the amount of pressure applied to the master device 3126 by the healthcare provider’s third finger. [0675] The deflation mode may refer to the motor 3112 receiving manipulation instructions comprising measurements of pressure, causing the motor 3112 to drive the one or more pumps coupled to the one or more sections of the treatment device 3106 to deflate the one or more sections. The manipulation instruction may be configurable by the healthcare provider. For example, as the healthcare provider moves the master device 3126, the movement is provided in a manipulation instruction for the motor 3112 to drive the pump to deflate one or more sections of the treatment device 3106. The manipulation instruction may include a pressure gradient to deflate the first and second sections in the right side of the knee brace to fourth and fifth measured levels of force and deflate the third section in the left side of the knee brace to the third measured level of force. The fourth measured level of force correlates with the amount of pressure applied to the master device 3126 by the healthcare provider’ s first finger. The fifth measured level of force correlates with the amount of pressure applied to the master device 3126 by the healthcare provider’s second finger. The sixth measured level of force correlates with the amount of pressure applied to the master device 3126 by the healthcare provider’s third finger. In this example, the healthcare provider loosened a grip (e.g., applied less pressure to each of the three fingers) applied to the treatment device 3106 virtually via the master device 3126.

[0676] During one or more of the modes, the one or more slave sensors 3108 may measure force (i.e., pressure or weight) exerted by a part of the body of the patient. For example, the each of the one or more sections 3310 of the treatment device 3106 may contain any suitable sensor (e.g., strain gauge load cell, piezoelectric crystal, hydraulic load cell, etc.) for measuring force exerted on the treatment device 3106. Further, the each of the one or more sections 3310 of the treatment device 3106 may contain any suitable sensor for detecting whether the body part of the patient separates from contact with the treatment device 3106. The force detected may be transmitted via the network interface card of the treatment device 3106 to the control system (e.g., slave computing device 3102 and/or the slave controller 3118). As described further below, the control system may modify a parameter of operating the slave motor 3112 using the measured force. Further, the control system may perform one or more preventative actions (e.g., locking the slave motor 3112 to stop the pump from activating, slowing down the slave motor 3112, presenting a notification to the patient such as via the patient portal 114, etc.) when the body part is detected as separated from the treatment device 3106, among other things. [0677] In some embodiments, the remote examination system 3100 includes the imaging device 3116. The imaging device 3116 may be configured to capture and/or measure angles of extension and/or bend of body parts and transmit the measured angles to the slave computing device 3102 and/or the master computing device 3122. The imaging device 3116 may be included in an electronic device that includes the one or more processing devices, memory devices, and/or network interface cards. The imaging device 3116 may be disposed in a cavity of the treatment device 3106 (e.g., in a mechanical brace). The cavity of the mechanical brace may be located near a center of the mechanical brace such that the mechanical brace affords to bend and extend. The mechanical brace may be configured to secure to an upper body part (e.g., leg, arm, etc.) and a lower body part (e.g., leg, arm, etc.) to measure the angles of bend as the body parts are extended away from one another or retracted closer to one another.

[0678] The imaging device 3116 can be a wristband. The wristband may include a 2-axis accelerometer to track motion in the X, Y, and Z directions, an altimeter for measuring altitude, and/or a gyroscope to measure orientation and rotation. The accelerometer, altimeter, and/or gyroscope may be operatively coupled to a processing device in the wristband and may transmit data to the processing device. The processing device may cause a network interface card to transmit the data to the slave computing device 3102 and the slave computing device 3102 may use the data representing acceleration, frequency, duration, intensity, and patterns of movement to track measurements taken by the patient over certain time periods (e.g., days, weeks, etc.). Executing a clinical portal 3134, the slave computing device 3102 may transmit the measurements to the master computing device 3122. Additionally, in some embodiments, the processing device of the wristband may determine the measurements taken and transmit the measurements to the slave computing device 3102. In some embodiments, the wristband may use photoplethysmography (PPG), which detects an amount of red light or green light on the skin of the wrist, to measure heartrate. For example, blood may absorb green light so that when the heart beats, the blood flow may absorb more green light, thereby enabling the detection of heartrate. The heartrate may be sent to the slave computing device 3102 and/or the master computing device 3122. [0679] The slave computing device 3102 may present the measurements (e.g., measured level of force or temperature) of the body part of the patient taken by the treatment device 3106 and/or the heartrate of the patient via a graphical indicator (e.g., a graphical element) on the patient portal 3114, as discussed further below. The slave computing device 3102 may also use the measurements and/or the heart rate to control a parameter of operating the treatment device 3106. For example, if the measured level of force exceeds a target pressure level for an examination session, the slave computing device 3102 may control the motor 3112 to reduce the pressure being applied to the treatment device 3106.

[0680] In some embodiments, the remote examination system 3100 may include a master computing device 3122 communicatively coupled to a master console 3124. The master console 3124 can include a master device 3126. The master device 3126 can include a master sensor 3128 and a master pressure system 3130. The master pressure system can include a master motor 3132. The remote examination system may also be communicatively coupled to a master display 3136. Each of the master computing device 3122, the master device 3126, and the master display 3136 may include one or more processing devices, memory devices, and network interface cards. The network interface cards may enable communication via a wireless protocol for transmitting data over short distances, such as Bluetooth, ZigBee, Near-Field Communications (NFC), etc. In some embodiments, the master computing device 3122 is communicatively coupled to the master device 3126 and the master display 3136 via Bluetooth.

[0681] Additionally, the network interface cards may enable communicating data over long distances, and in one example, the master computing device 3122 may communicate with a network 3104. The master computing device 3122 may be communicatively coupled with the slave computing device 3102 and the cloud- based computing system 3142.

[0682] The master computing device 3122 may be any suitable computing device, such as a laptop, tablet, smartphone, or computer. The master computing device 3122 may include a display capable of presenting a user interface, such as a clinical portal 3134. The clinical portal 3134 may be implemented in computer instructions stored on the one or more memory devices of the master computing device 3122 and executable by the one or more processing devices of the master computing device 3122. The clinical portal 3134 may present various screens to a user (e.g., a healthcare provider), the screens configured to enable the user to view a patient’s medical records, a treatment plan, or progress during the treatment plan; to initiate a remote examination session; to control parameters of the master device 3126; to view progress of rehabilitation during the remote examination session, or combination thereof. The master computing device 3122 may also include instructions stored on the one or more memory devices that, when executed by the one or more processing devices of the master computing device 3122, perform operations to control the master device 3126.

[0683] The master computing device 3122 may execute the clinical portal 3134. The clinical portal 3134 may be implemented in computer instructions stored on the one or more memory devices of the master computing device 3122 and executable by the one or more processing devices of the master computing device 3122. The clinical portal 3134 may present various screens to a healthcare provider (e.g., a clinician), the screens configured to enables the clinician to view a remote examination of a patient, such as a patient rehabilitating from a surgery (e.g., knee replacement surgery) or from an injury (e.g., sprained ankle). During a telemedicine session, an augmented image representing one or more body parts of the patient may be presented simultaneously with a video of the patient on the clinical portal 3134 in real-time or in near real-time. For example, the clinical portal 3134 may, at the same time, present the augmented image 3402 of the knee of the patient and portions of the patient’s leg extending from the knee and a video of the patient’s upper body (e.g., face), so the healthcare provider can engage in more personal communication with the patient (e.g., via a video call). The video may be of the patient’s full body, such that, during the telemedicine session, the healthcare provider may view the patient’s entire body. The augmented image 3402 can be displayed next to the video and/or overlaid onto the respective one or more body parts of the patient. For example, the augmented image 3402 may comprise a representation of the treatment device 3106 coupled to the patient’s knee and leg portions. The clinical portal 3134 may display the representation of the treatment device 3106 overlaid onto the respective one or more body parts of the patient. Real-time may refer to less than 2 seconds, or any other suitable amount of time. Near real-time may refer to 2 or more seconds. The video may also be accompanied by audio, text, and other multimedia information. The master display 3136 may also be configured to present the augmented image and/or the video as described herein.

[0684] Presenting the remote examination generated by the artificial intelligence engine concurrently with a presentation of the patient video may provide an enhanced user interface because the healthcare provider, while reviewing the examination on the same user interface, may also continue to visually and/or otherwise communicate with the patient. The enhanced user interface may improve the healthcare provider’s experience in using the computing device and may encourage the healthcare provider to reuse the user interface. Such a technique may also reduce computing resources (e.g., processing, memory, network), because the healthcare provider does not have to switch to another user interface screen and, using the characteristics of the patient, enter a query for examination guidelines to recommend. For example, the enhanced user interface may provide the healthcare provider with recommended procedures to conduct during the telemedicine session. The recommended procedures may comprise a guide map, including indicators of locations and measured amounts of pressure to apply on the patient’s one or more body parts. The artificial intelligence engine may analyze the examination results (e.g., measured levels of force exerted to and by the patient’s one or more body parts, the temperature of the patient, the pain level of the patient, a measured range of motion of the one or more body parts, etc.) and provide, dynamically on the fly, the optimal examination procedures and excluded examination procedures.

[0685] The clinical portal 3134 may also provide examination information generated during the telemedicine session for the healthcare provider to view. The examination information can include a summary of the examination and/or the results of the examination in real-time or near real-time, such as measured properties of the patient during the examination. Examples of the measured properties may include, but are not limited to, angles of bend/extension, pressure exerted on the master device 3126, pressure exerted by the patient on the treatment device 3106, images of the examined/treated body part, and vital signs of the patient, such as heartrate and temperature. The clinical portal 3134 may also provide the clinician’s notes and the patient’s health information, such as a health history, a treatment plan, and a progress of the patient throughout the treatment plan. So the healthcare provider may begin the remote examination, the examination information specific to the patient may be transmitted via the network 3104 to the cloud-based computing system 3142 for storage and/or to the master computing device 3122.

[0686] In some embodiments, the clinical portal 3134 may include a treatment plan that includes one or more examination procedures (e.g., manipulation instructions to manipulate one or more sections 3210 of the treatment device 3106). For example, a healthcare provider may input, to the clinical portal 3134, a treatment plan with pre-determined manipulation instructions for the treatment device 3106 to perform during the remote examination. The healthcare provider may input the pre-determined manipulation instructions prior the remote examination. The treatment device 3106 can be activated to perform the manipulations in accordance with the pre-determined manipulation instructions. The healthcare provider may observe the remote examination in real time and make modifications to the pre-determined manipulation instructions during the remote examination. Additionally, the system 3100 can store the results of the examination and the healthcare provider can complete the examination using the stored results (e.g., stored slave sensor data) and the master device 3126. In other words, the master processing device can use the slave sensor data to manipulate the master device 3126. This manipulation of the master device 3126 can allow the healthcare provider to virtually feel the patient’s one or more body parts and provide the healthcare provider with additional information to determine a personalized treatment plan for the patient.

[0687] The master device 3126 may be an examination device configured for control by a healthcare provider. The master device 3126 may be a joystick, a model treatment device (e.g., a knee brace to fit over a manikin knee), an examination device to fit over a body part of the healthcare provider (e.g., a glove device), any other suitable device, or combination thereof. The joystick may be configured to be used by a healthcare provider to provide manipulation instructions. The joystick may have one or more buttons (e.g., a trigger) to apply more or less pressure to one or more sections of the treatment device 3106. The joystick may be configured to control a moveable indicator (e.g., a cursor) displayed at the master display or any other suitable display. The moveable indicator can be moved over an augmented image 3400 of the treatment device 3106 and/or one or more body parts of the patient. The healthcare provider may be able to provide verbal commands to increase and/or decrease pressure based on where the moveable indicator is positioned relative to the augmented image 3400. The joystick may have master sensors 3128 within a stick of the joystick. The stick may be configured to provide feedback to the user (e.g., vibrations or pressure exerted by the stick to the user’s hand). [0688] The model of the treatment device may be formed similarly to the treatment device 3106. For example, if the treatment device 3106 is the knee brace 3202, the master device can be a model knee brace with similar characteristics of the knee brace 3202. The model can be configured for coupling to a manikin or any other suitable device. The model can comprise the master pressure system 3130 and master sensors 3128 and function as described in this disclosure. The model may be configured for a healthcare provider to manipulate (e.g., touch, move, and/or apply pressure) to one or more sections of the model and to generate master sensor data based on such manipulations. The model can be operatively coupled to the treatment device 3106. The master sensor data can be used to inflate and/or deflate one or more corresponding sections of the treatment device 3106 (e.g., as the healthcare provider is manipulating the model, the treatment device 3106 is being manipulated on the patient). Responsive to receiving the slave sensor data, the master pressure system 3130 can active and inflate and/or deflate one or more sections of the model (e.g., the pressure applied to the treatment device 3106 by the patient’s one or more body parts is similarly applied to the model for the healthcare provider to examine). The healthcare provider can essentially feel, with his or her bare (or appropriately gloved) hands, the patient’s one or more body parts (e.g., the knee) while the healthcare provider virtually manipulates the patient body part(s).

[0689] In some embodiments, the system 3100 may include one or more master computing devices 3122 and one or more master consoles 3124. For example, a second master console can include a second master device 3126 operatively coupled to a second master computing device. The second master device can comprise a second master pressure system 3130, and, using the slave force measurements, the one or more processing devices of system 3100 can be configured to activate the second master pressure system 3130. During and/or after a telemedicine session, one or more healthcare providers can manipulate the treatment device 3106 and/or use the slave sensor data to virtually feel the one or more body parts of the patient. For example, a physician and a physical therapist may virtually feel the one or more body parts of the patient at the same time or at different times. The physician may provide the manipulation instructions and the physical therapist may observe (e.g., virtually see and/or feel) how the patient’s one or more body parts respond to the manipulations. The physician and the physical therapist may use different examination techniques (e.g., locations of the manipulations and/or measure levels of force applied to the treatment device 3106) to obtain information for providing a treatment plan for the patient. Resulting from the physician using the master device 3106 and the physical therapist using the second master device, each can provide manipulation instructions to the treatment device 3106. The manipulation instructions from the master device 3106 and the second master device may be provided at the same time or at a different time (e.g., the physician provides a first manipulation instruction via the master device 3126 and the physical therapist provides a second manipulation instruction via the second master device). In another example, the physician may have input a pre -determined manipulation instruction for the remote examination and the physical therapist may use the second master device to adjust the pre-determined manipulation instructions. The physician and the physical therapist may be located remotely from each other (and remotely from the patient) and each can use the system 3100 to examine the patient and provide a personalized treatment plan for the patient. The system 3100 can allow for collaboration between one or more healthcare providers and provide the healthcare providers with information to make optimal adjustments to the patient’s treatment plan.

[0690] As illustrated in FIG. 27, the master device 3126 comprises a glove device 3300 configured to fit on a healthcare provider’s hand. The glove device 3300 can include fingers 3302. The glove may include one or more sensors (e.g., one or more master sensors 3128). The glove device 3300 may include the master sensors 3128 positioned along the fingers 3302, 3304, 3306, 3308, 3310 (collectively, fingers 3302), throughout the palm of the glove, in any other suitable location, or in any combination thereof. For example, each finger can include a series of master sensors 3128 positioned along the fingers 3302. Each of the series of master sensors 3128 can be operatively coupled to one or more master controllers 3138. The master device 3126 may include at least one or more master controllers 3138 and one or more master motors 3132, such as an electric motor (not illustrated).

[0691] A pump (not illustrated) may be operatively coupled to the motor. The pump may be configured to increase or decrease pressure within the master device 3126. The master device 3126 may include one or more sections and the pump can be activated to increase or decrease pressure (e.g., inflating or deflating fluid, such as water, gel, air) in the one or more sections (e.g., one or more fingertips). One or more of the sections may include a master sensor 3128. The master sensor 3128 can be a sensor for detecting signals, such as pressure, or any other suitable signal. The master controller 3138 may be operatively coupled to the master motor 3132 and configured to provide commands to the master motor 3132 to control operation of the master motor 3132. The master controller 3138 may include any suitable microcontroller including a circuit board having one or more processing devices, one or more memory devices (e.g., read-only memory (ROM) and/or random access memory (RAM)), one or more network interface cards, and/or programmable input/output peripherals. The master controller 3138 may provide control signals or commands to drive the master motor 3132. The master motor 3132 may be powered to drive the pump of the master device 3126. The master motor 3132 may provide the driving force to the pump to increase or decrease pressure at configurable speeds. Further, the master device 3126 may include a current shunt to provide resistance to dissipate energy from the master motor 3132. In some embodiments, the treatment device 3106 may comprise a haptic system, a pneumatic system, any other suitable system, or combination thereof. For example, the haptic system can include a virtual touch by applying forces, vibrations, or motions to the healthcare provider through the master device 3126.

[0692] The master computing device 3122 may be communicatively connected to the master device 3126 via a network interface card on the master controller 3138. The master computing device 3122 may transmit commands to the master controller 3138 to control the master motor 3132. The network interface card of the master controller 3138 may receive the commands and transmit the commands to the master controller 3138 to drive the master motor 3132. In this way, the master computing device 3122 is operatively coupled to the master motor 3132.

[0693] The master computing device 3122 and/or the master controller 3138 may be referred to as a control system (e.g., a master control system) herein. The clinical portal 3134 may be referred to as a clinical user interface of the control system. The master control system may control the master motor 3132 to operate in a number of modes, including: standby, inflate, and deflate. The standby mode may refer to the master motor 3132 powering off so that it does not provide any driving force to the one or more pumps. For example, when the healthcare provider is not touching an augmented image of the treatment device 3106, the pump of the master device 3126 may not receive instructions to inflate or deflate one or more sections of the master device 3126 and the master motor 3132 may remain turned off. In the standby mode, the master device 3126 may not apply pressure to the healthcare provider’s body part(s) (e.g., to the healthcare provider’s finger 3304 via the glove device 3300) because the healthcare provider is not in virtual contact with the treatment device 3106. Furthermore, in standby mode, the master device 3126 may not transmit the master sensor data based on manipulations of the master device 3126 (e.g., pressure virtually exerted from the healthcare care provider’s hand to the master device 3126) to the patient via the treatment device 3106.

[0694] The inflate mode may refer to the master motor 3132 receiving slave sensor data comprising measurements of pressure, causing the master motor 3132 to drive the one or more pumps coupled to the one or more sections of the master device 3126 (e.g., one or more fingers 3302, 3304, 3406, 3308, 3310) to inflate the one or more sections. The slave sensor data may be provided by the one or more slave sensors 3108 of the treatment device 3106 via the slave computing device 3102. For example, as the healthcare provider manipulates (e.g., moves) the master device 3126 to virtually contact one or more body parts of the patient using the treatment device 3106 in contact with the patient’ s one or more body parts, the treatment device 3106 is manipulated. The slave sensors 3108 are configured to detect the manipulation of the treatment device 3106. The detected information may include how the patient’ s one or more body parts respond to the manipulation. The one or more slave sensors 3108 may detect that one area of the patient’s body part exerts a first measured level of force and that another area of the patient’s body part exerts a second measured level of force (e.g., the one area may be swollen or inconsistent with baseline measurements or expectations as compared to the other area). The master computing device 3122 can receive the information from the slave sensor data and instruct the master motor 3132 to drive the pump to inflate one or more sections of the master device 3126. The level of inflation of the one or more sections of the master device 3126 may correlate with one or more measured levels of force detected by the treatment device 3106. The slave sensor data may include a pressure gradient. The master computing device 3122 may instruct the master pressure system 3130 to inflate a first section (e.g., the fingertips of the first finger 3302) correlating with the first measured level of force exerted from a left side of the knee brace 3202. The master computing device 3122 may instruct the master pressure system 3130 to inflate second and third sections (e.g., the fingertips of second and third fingers 3304, 3306) correlating with second and third measured levels of force exerted from a front side of the knee brace 3202. In other words, in response to the master device 3126 virtually touching the treatment device 3106, the first measured level of force may correlate with the amount of pressure applied to the healthcare provider’s first finger through the first finger 3302 of the master device 3126. Similarly, the second measured level of force may correlate with the amount of measured force applied by the healthcare provider’s second finger through the second finger 3304 of the master device 3126. The third measured level of force may correlate with the amount of measured force applied by the healthcare provider’s third finger through the third finger 3306 of the master device 3126. The glove device 3300 can include a fourth finger 3308 to provide a fourth measured level of force, a fifth finger 3310 to provide a fifth measured level of force, and/or other sections, such as a palm, or any combination thereof configured to provide measured levels of force to the healthcare provider. The sections of the glove device 3300 can be inflated or deflated to correlate with the same and/or different levels of measured force exerted on the treatment device 3106.

[0695] The deflation mode may refer to the master motor 3132 receiving slave sensor data comprising measurements of pressure, causing the master motor 3132 to drive the one or more pumps coupled to the one or more sections of the master device 3126 (e.g., one or more fingers 3302) to deflate the one or more sections. The deflation mode of the master pressure system 3130 can function similarly as the inflation mode; however, in the deflation mode, the master pressure system 3130 deflates, rather than inflates, the one or more sections of the master device 3126. For example, the one or more slave sensors 3108 may detect that one area of the patient’s body part exerts a first measured level of force and that another area of the patient’s body part exerts a second measured level of force (e.g., the one area may be less swollen or less inconsistent with baseline measurements or expectations as compared to the other area). The master computing device 3122 can receive the information from the slave sensor data and instruct the master motor 3132 to drive the pump to deflate one or more sections of the master device 3126. The level of deflation of the one or more sections of the master device 3126 may correlate with one or more measured levels of force detected by the treatment device 3106. [0696] The measured levels of force can be transmitted between the treatment device 3106 and the master device 3126 in real-time, near real-time, and/or at a later time. In other words, the healthcare provider can use the master device 3126 to virtually examine the patient’s body part using the healthcare provider’s hand and feel the patient’s body part (e.g., the pressure, etc.). Similarly, the patient can feel the healthcare provider virtually touching his or her body part (e.g., from the pressure exerted by the treatment device 3106). During the telemedicine session, the patient, via the patient portal 3114, can communicate to the healthcare provider via the clinical portal 3134,. For example, during the remote examination, the patient can inform the healthcare provider that the location of the body part that the healthcare provider is virtually touching (e.g., manipulating), is painful. The information can be communicated verbally and/or visually (e.g., input into the patient portal 3114 directly by the client and transmitted to the clinical portal 3134 and/or the master display 3136). The healthcare provider can receive additional information, such as temperature of the patient’s body part, vital signs of the patient, any other suitable information, or any combination thereof.

[0697] During one or more of the inflation and deflation modes, the one or more master sensors 3128 may measure force (i.e., pressure) exerted by the healthcare provider via the master device 3126. For example, one or more sections of the master device 3126 may contain any suitable sensor (e.g., strain gauge load cell, piezoelectric crystal, hydraulic load cell, etc.) for measuring force exerted on the master device 3126. Further, each section 3310 of the master device 3126 may contain any suitable sensor for detecting whether the body part of the healthcare provider separates from contact with the master device 3126. The measured level(s) of force detected may be transmitted via the network interface card of the master device 3126 to the control system (e.g., master computing device 3122 and/or the master controller 3138). As described further below, using the measured level(s) of force, the control system may modify a parameter of operating the master motor 3132. Further, the control system may perform one or more preventative actions (e.g., locking the master motor 3132 to stop the pump from activating, slowing down the master motor3 132, or presenting a notification to the healthcare provider (such as via the clinical portal 3134, etc.)) when the body part is detected as being separated from the master device 3126, among other things.

[0698] In some embodiments, the remote examination system 3100 includes the master display 3136. The master console 3124 and/or the clinical portal 3134 may comprise the master display 3136. The master display 3136 may be configured to display the treatment device 3106 and/or one or more body parts of a patient. For example, the slave computing device 3102 may be operatively coupled to an imaging device 3116 (e.g., a camera or any other suitable audiovisual device) and/or other sensorial or perceptive (e.g., tactile, gustatory, haptic, pressure-sensing-based or electromagnetic (e.g., neurostimulation) communication devices. Any reference herein to any particular sensorial modality shall be understood to include and to disclose by implication a different one or more sensory modalities. The slave computing device 3102 can transmit, via the network 3104, real images and/or a real live-streaming video of the treatment device 3106 and/or the patient, to the master display 3136. The real images and/or real video may include angles of extension and/or bend of body parts of the patient, or any other suitable characteristics of the patient. The treatment device 3106 may be operatively coupled to a medical device, such as a goniometer. The goniometer may detect angles of extension and/or bend of body parts of the patient and transmit the measured angles to the slave computing device 3102 and/or the treatment device 3106. The slave computing device 3102 can transmit the measured angles to the master computing device 3122, to the master display 3136, or any other suitable device. The master display 3136 can display the measured angles in numerical format, as an overlay image on the image of the treatment device 3106 and/or the patient’s one or more body parts, any other suitable format, or combination thereof. For example, as illustrated in FIG. 28A, body parts (e.g., a leg and a knee) are extended at a first angle. In FIG. 28B, the body parts are illustrated as being extended at a second angle. The master display 3136 may be included in an electronic device that includes the one or more processing devices, memory devices, and/or network interface cards.

[0699] Depending on what result is desired, the master computing device 3122 and/or a training engine 3146 may be trained to output a guide map. The guide map may be overlaid on the augmented image 3400. The guide map may include one or more indicators. To guide the master device 3126, the indicators can be positioned over one or more sections 3310 of the augmented image 3400 of the treatment device 3106. For example, the augmented image 3402 may include a first indicator (e.g., dotted lines in the shape of a fingertip) positioned over a top portion of patient’s knee and a second indicator positioned over a left side of the patient’s knee. The first indicator is a guide for the healthcare provider to place the first finger 3302 on the first indicator and the second finger 3304 on the second indicator. The guide map may comprise a pressure gradient map. The pressure gradient map can include the current measured levels of force at the location of the indicator and/or a desired measured level of force at the location of the indicator. For example, the first indicator may comprise a first color, a first size, or any other suitable characteristic to indicate a first measured level of force. The second indicator may comprise a second color, a second size, or any other suitable characteristic to indicate a second measured level of force. When the master device 3126 reaches the desired measured levels of force, an alert may be provided. The alert may be a visual, audio and/or another alert. For example, the alert may comprise the indicator changing colors when the measured level of force is provided. The guide map may include one or more configurations using characteristics of the injury, the patient, the treatment plan, the recovery results, the examination results, any other suitable factors, or any combinations thereof. One or more configurations may be displayed during the remote examination portion of a telemedicine session.

[0700] The master computing device 3122 and/or the training engine 3146 may include one or more thresholds, such as pressure thresholds. The one or more pressure thresholds may be based on characteristics of the injury, the patient, the treatment plan, the recovery results, the examination results, the pain level, any other suitable factors, or any combinations thereof. For example, one pressure threshold pertaining to the pain level of the patient may include a pressure threshold level for the slave pressure system 3110 not to inflate a particular section 3210 more than a first measured level of force. As the pain level of the patient decreases, the pressure threshold may change such that a second measured level of force may be applied to that particular section 3210. In this case, the patient’s decreased pain level may, for more optimal examination results (e.g., the second measured level of force is greater than the first measured level of force), allow for the healthcare provider to increase the measured amount of pressure applied to the patient’s body part. Similarly, the master computing device 3122 and/or the training engine 3146 may be configured to adjust any pre-determined manipulation instructions. In this way, the manipulation instructions can be adapted to the specific patient.

[0701] In other embodiments, the master display 3136 can display an augmented image (e.g., exemplary augmented images 3400 illustrated in FIG. 28), an augmented live-streaming video, a holographic image, any other suitable transmission, or any combination thereof of the treatment device 3106 and/or one or more body parts of the patient. For example, the master display 3136 may project an augmented image 3402 representing the treatment device 3106 (e.g., a knee brace 3202). The augmented image 3402 can include a representation 3410 of the knee brace 3202. The augmented image 3402 can include a representation 3412 of one or more body parts of a patient. Using the master device 3126, the healthcare provider can place a hand on the image and manipulate the image (e.g., apply pressure virtually to one or more sections of the patient’s knee via the treatment device 3106. The one or more processing devices may cause a network interface card to transmit the data to the master computing device 3122 and the master computing device 3122 may use the data representing pressure, temperature, and patterns of movement to track measurements taken by the patient’ s recovery over certain time periods (e.g., days, weeks, etc.). In FIG. 28, the augmented images 3400 are two dimensional, but the augmented images 3400 may be transmitted as three-dimensional images or as any other suitable image dimensionality. [0702] The master display 3136 can be configured to display information obtained from a wristband. The information may include motion measurements of the treatment device 3106 in the X, Y, and Z directions, altitude measurements, orientation measurements, rotation measurements, any other suitable measurements, or any combinations thereof. The wristband may be operatively coupled to an accelerometer, an altimeter, and/or a gyroscope. The accelerometer, the altimeter, and/or the gyroscope may be operatively coupled to a processing device in the wristband and may transmit data to the one or more processing devices. The one or more processing devices may cause a network interface card to transmit the data to the master computing device 3122 and the master computing device 3122 may use the data representing acceleration, frequency, duration, intensity, and patterns of movement to track measurements taken by the patient over certain time periods (e.g., days, weeks, etc.). Executing the clinical portal 3134, the master computing device 3122 may transmit the measurements to the master display 3136. Additionally, in some embodiments, the processing device of the wristband may determine the measurements taken and transmit the measurements to the slave computing device 3102. The measurements may be displayed on the patient portal 3114. In some embodiments, the wristband may measure heartrate by using photoplethysmography (PPG), which detects an amount of red light or green light on the skin of the wrist. For example, blood may absorb green light so when the heart beats, the blood volume flow may absorb more green light, thereby enabling heartrate detection. In some embodiments, the wristband may be configured to detect temperature of the patient. The heartrate, temperature, any other suitable measurement, or any combination thereof may be sent to the master computing device 3122.

[0703] The master computing device 3122 may present the measurements (e.g., pressure or temperature) of the body part of the patient taken by the treatment device 3106 and/or the heartrate of the patient via a graphical indicator (e.g., a graphical element) on the clinical portal 3134. The measurements may be presented as a gradient map, such as a pressure gradient map or a temperature gradient map. The map may be overlaid over the image of the treatment device 3106 and/or the image of the patient’s body part. For example, FIG. 28C illustrates an exemplary augmented image 3406 displaying a pressure gradient 3414 over the image of the patient’s body parts 3412 (e.g., feet). FIG. 28D illustrates an exemplary augmented image 3408 displaying a temperature gradient 3416 over the image of the patient’s body parts 3412 (e.g., feet).

[0704] Referring back to FIG. 25, the remote examination system 3100 may include a cloud-based computing system 3142. In some embodiments, the cloud-based computing system 3142 may include one or more servers 3144 that form a distributed computing architecture. Each of the servers 3144 may include one or more processing devices, memory devices, data storage devices, and/or network interface cards. The servers 3144 may be in communication with one another via any suitable communication protocol. The servers 3144 may store profiles for each of the users (e.g., patients) configured to use the treatment device 3106. The profiles may include information about the users such as a treatment plan, the affected body part, any procedure the user had had performed on the affected body part, health, age, race, measured data from the imaging device 3116, slave sensor data, measured data from the wristband, measured data from the goniometer, user input received at the patient portal 3114 during the telemedicine session, a level of discomfort the user experienced before and after the remote examination, before and after remote examination images of the affected body part(s), and so forth.

[0705] In some embodiments, the cloud-based computing system 3142 may include a training engine 3146 capable of generating one or more machine learning models 3148. The machine learning models 3148 may be trained to generate treatment plans, procedures for the remote examination, or any other suitable medical procedure for the patient in response to receiving various inputs (e.g., a procedure via a remote examination performed on the patient, an affected body part the procedure was performed on, other health characteristics (age, race, fitness level, etc.)). The one or more machine learning models 3148 may be generated by the training engine 3146 and may be implemented in computer instructions executable by one or more processing devices of the training engine 3146 and/or the servers 3144.

[0706] To generate the one or more machine learning models 3148, the training engine 3146 may train the one or more machine learning models 3148. The training engine 3146 may use a base data set of patient characteristics, results of remote examination(s), treatment plans followed by the patient, and results of the treatment plan followed by the patients. The results may include information indicating whether the remote examination led to an identification of the affected body part and whether the identification led to a partial recovery of the affected body part or lack of recovery of the affected body part. The results may include information indicating the measured levels of force applied to the one or more sections of the treatment device 3106.

[0707] The training engine 3146 may be a rackmount server, a router computer, a personal computer, an Internet of Things (IoT) device, a portable digital assistant, a smartphone, a laptop computer, a tablet computer, a camera, a video camera, a netbook, a desktop computer, a media center, any other desired computing device, or any combination of the above. The training engine 3146 may be cloud-based or a real-time software platform, and it may include privacy software or protocols, and/or security software or protocols.

[0708] The one or more machine learning models 3148 may also be trained to translate characteristics of patients received in real-time (e.g., from an electronic medical records (EMR) system, from the slave sensor data, etc.). The one or more machine learning models 3148 may refer to model artifacts that are created by the training engine 3146 using training data that includes training inputs and corresponding target outputs. The training engine 3146 may find patterns in the training data that map the training input to the target output, and generate the machine learning models 3148 that capture these patterns. Although depicted separately from the slave computing device 3102, in some embodiments, the training engine 3146 and/or the machine learning models 3148 may reside on the slave computing device 3102 and/or the master computing device 3122.

[0709] Different machine learning models 3148 may be trained to recommend different optimal examination procedures for different desired results. For example, one machine learning model may be trained to recommend optimal pressure maps for most effective examination of a patient, while another machine learning model may be trained to recommend optimal pressure maps using the current pain level and/or pain level tolerance of a patient.

[0710] The machine learning models 3148 may include one or more of a neural network, such as an image classifier, recurrent neural network, convolutional network, generative adversarial network, a fully connected neural network, or some combination thereof, for example. In some embodiments, the machine learning models 3148 may be composed of a single level of linear or non-linear operations or may include multiple levels of non-linear operations. For example, the machine learning model may include numerous layers and/or hidden layers that perform calculations (e.g., dot products) using various neurons.

[0711] FIGS. 25-28 are not intended to be limiting: the remote examination system 3100 may include more or fewer components than those illustrated in FIGS. 25-28.

[0712] FIG. 29 illustrates a computer-implemented method 3500 for remote examination. The method 3500 may be performed by the remote examination system 3100, such as at a master processing device. The processing device is described in more detail in FIG. 30. The steps of the method 3500 may be stored in a non-transient computer-readable storage medium. Any or all of the steps of method 3500 may be implemented during a telemedicine session or at any other desired time.

[0713] At step 3502, the method 3500 includes the master processing device receiving slave sensor data from one or more slave sensors 3108. The master processing device may receive, via the network 3104, the slave sensor data from a slave processing device.

[0714] At step 3504, the master processing device can transmit an augmented image 3400. The augmented image 3400 may be based on the slave sensor data.

[0715] At step 3506, the master processing device receives master sensor data correlating with a manipulation of the master device 3126. For example, the master sensor data may include a measured level of force that the user, such as a healthcare provider, applied to the master device 3126.

[0716] At step 3508, the master processing device can generate a manipulation instruction. The manipulation instruction is based on the master sensor data correlating with the manipulation of the master device 3126.

[0717] At step 3510, the master processing device transmits the manipulation instruction. The master processing device may transmit, via the network 3104, the manipulation instruction to the slave computing device 3102.

[0718] At step 3512, the master processing device causes the slave pressure system to activate. Using the manipulation instruction, the slave computing device 3102 can cause the treatment device 3106 to activate the slave pressure system 3110. For example, responsive to the manipulation instruction (e.g., to increase and/or decrease one or more measured levels of force in one or more sections of the treatment device), the slave pressure system 3110 can cause the slave controller 3118 to activate the slave motor 3112 to inflate and/or deflate the one or more sections 3210 to one or more measured levels of force.

[0719] At step 3514, the master processing device receives slave force measurements. The slave force measurements can include one or more measurements correlating with one or more measured levels of force that the patient’s body is applying to the treatment device 3106. [0720] At step 3516, the master processing device uses the pressure slave measurements to activate the master pressure system 3130. For example, the master pressure system 3130 can cause the master device 3126 to inflate and/or deflate one or more sections 3310 of the master device 3126 such that the measured levels of force of the one or more sections 3310 directly correlate with the one or more measured levels of force that the patient’s body is applying to the one or more sections 3210 of the treatment device 3106.

[0721] FIG. 30 illustrates a computer-implemented method 3600 for remote examination. The method 600 may be performed by the remote examination system 3100, such as at a slave processing device. The processing device is described in more detail in FIG. 30. The steps of the method 3600 may be stored in a non-transient computer-readable storage medium. Any or all of the steps of method 3600 may be implemented during a telemedicine session or at any other desired time.

[0722] At step 3602, the method 3600 includes the slave processing device receiving slave sensor data from one or more slave sensors 3108. The one or more slave sensors 3108 may include one or more measured levels of force that the patient’s body is applying to the treatment device 3106.

[0723] At step 3604, the slave processing device transmits the slave sensor data. The slave processing device may transmit, via the network 3104, the slave sensor data to the master computing device 3122.

[0724] At step 3606, the slave processing device may transmit an augmented image 3400. The augmented image 3400 is based on the slave sensor data. For example, the augmented image 3400 may include a representation of the treatment device 3 i06, one or more body parts of the patient, measured levels of force, measured levels of temperature, any other suitable information, or combination thereof.

[0725] At step 3608, the slave processing device receives a manipulation instruction. The manipulation instruction can be generated based on the master sensor data.

[0726] At step 3610, using the manipulation instruction, the slave processing device activates the slave pressure system 3110. For example, the manipulation instruction may cause the slave pressure system 3110 to inflate and/or deflate one or more sections 3210 of the treatment device 3106 to correlate with one or more levels of force applied to one or more sections 3310 of the master device 3126.

[0727] At step 3612, the slave processing device receives slave force measurements. The slave force measurements can include one or more measured levels of force exerted by the patient’s body to the treatment device 3106.

[0728] At step 3614, the slave processing device transmits the slave force measurements, such as to the master processing device.

[0729] At step 3616, using the slave force measurements, the slave processing device causes a master pressure system 3130 to activate. For example, the master pressure system 3130 can cause the master device 3126 to inflate and/or deflate one or more sections 3310 of the master device 3126 such that the measured levels of force of the one or more sections 3310 correlate with the one or more measured levels of force that the patient’s body is applying to the one or more sections 3210 of the treatment device 3106.

[0730] FIGS. 29-30 are not intended to be limiting: the methods 3500, 3600 can include more or fewer steps and/or processes than those illustrated in FIGS. 29-30. Further, the order of the steps of the methods 3500, 3600 is not intended to be limiting; the steps can be arranged in any suitable order. Any or all of the steps of methods 3500, 3600 may be implemented during a telemedicine session or at any other desired time. [0731] FIG. 31 illustrates, in accordance with one or more aspects of the present disclosure, an example computer system 3700 which can perform any one or more of the methods described herein. The computer system 3700 may correspond to the slave computing device 3102 (e.g., a patient’s computing device), the master computing device 3122 (e.g., a healthcare provider’s computing device), one or more servers of the cloud-based computing system 3142, the training engine 3146, the server 3144, the slave pressure system 3110, the master pressure system 3130, the slave controller 3118, the master controller 3138, the imaging device 3116, the master display 3136, the treatment device 3106, the master device 3126, and/or the master console 3124 of FIG. 15. The computer system 3700 may be capable of executing the patient portal 3114 and/or clinical portal 3134 of FIG. 25. The computer system 3700 may be connected (e.g., networked) to other computer systems in a LAN, an intranet, an extranet, or the Internet. The computer system 3700 may operate in the capacity of a server in a client-server network environment. The computer system may be a personal computer (PC), a tablet computer, a motor controller, a goniometer, a wearable (e.g., wristband), a set-top box (STB), a personal Digital Assistant (PDA), a mobile phone, a camera, a video camera, or any device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that device. Further, while only a single computer system is illustrated, the term “computer” shall also be taken to include any collection of computers that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methods discussed herein.

[0732] The computer system 3700 includes a processing device 3702 (e.g., the slave processing device, the master processing device), a main memory 3704 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM)), a static memory 3706 (e.g., flash memory, static random access memory (SRAM)), and a data storage device 3708, which communicate with each other via a bus 3710.

[0733] The processing device 3702 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device 3702 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing device 3702 may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processing device 3702 is configured to execute instructions for performing any of the operations and steps discussed herein.

[0734] The computer system 3700 may further include a network interface device 3712. The computer system 3700 also may include a video display 3714 (e.g., a liquid crystal display (LCD), a light-emitting diode (LED), an organic light-emitting diode (OLED or Organic LED), or a cathode ray tube (CRT)). The video display 3714 can represent the master display 3136 or any other suitable display. The computer system 3700 may include one or more input devices 3716 (e.g., a keyboard, a mouse, the goniometer, the wristband, the imaging device 3116, or any other suitable input). The computer system 3700 may include one or more output devices (e.g., a speaker 3718). In one illustrative example, the video display 3714, the input device(s) 3716, and/or the speaker 3718 may be combined into a single component or device (e.g., an LCD touch screen). [0735] The data storage device 3708 may include a computer-readable medium 3720 on which the instructions 3722 (e.g., implementing the control system, the patient portal 3114, the clinical portal 3134, and/or any functions performed by any device and/or component depicted in the FIGS and described herein) embodying any one or more of the methodologies or functions described herein are stored. The instructions 3722 may also reside, completely or at least partially, within the main memory 3704 and/or within the processing device 3702 during execution thereof by the computer system 3700. As such, the main memory 3704 and the processing device 3702 also constitute computer-readable media. The instructions 3722 may further be transmitted or received over a network via the network interface device 3712.

[0736] While the computer-readable storage medium 3720 is shown in the illustrative examples to be a single medium, the term “computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer-readable storage medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure. The term “computer-readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media.

[0737] In one exemplary embodiment, the computer system 3700 includes the input device 3716 (e.g., the master console 3124 comprising the master device 3126) and the control system comprising the processing devices 3702 (e.g., the master processing device) operatively coupled to the input device 3716 and the treatment device 3106. The system 3700 may comprise one or more memory devices (e.g., main memory 3704, data storage device 3708, etc.) operatively coupled to the processing device 3702. The one or more memory devices can be configured to store instructions 3722. The processing device 3702 can be configured to execute the instructions 3722 to receive the slave sensor data from the one or more slave sensors 3108, to use a manipulation of the master device 3126 to generate a manipulation instruction, to transmit the manipulation instruction, and to use the manipulation instruction to cause the slave pressure system 3110 to activate. The instructions can be executed in real-time or near real-time.

[0738] The processing device 3702 can be further configured to use the slave sensor data to transmit an augmented image 3400 to the video display (e.g., the master display 3136). The healthcare provider may view the augmented image 3400 and/or virtually touch the augmented image using the video display 3714. In other words, the augmented image 3400 may comprise a representation of the treatment device 3106 and one or more body parts of the patient. The representation may be displayed in 2D, 3D, or any other suitable dimension. As the healthcare provider conducts the remote examination during a telemedicine session, the augmented image 3400 may change to reflect the manipulations of the treatment device 3106 and/or of any movement of the patient’s one or more body parts.

[0739] The augmented image 3400 can comprise one or more pressure indicators, temperature indicators, any other suitable indicator, or combination thereof. Each pressure indicator can represent a measured level of force (i.e., based on the slave force measurements). Each temperature indicator can represent a measured level of temperature (i.e., based on the slave temperature measurements). For example, the pressure indicators and/or the temperature indicators may be different colors, each color correlating with one of the measured levels of force and temperature, respectively. The indicators may be displayed as a map. The map may be a gradient map displaying the pressure indicators and/or temperature indicators. The map may be overlaid over the augmented image. The map may be transmitted to the clinical portal, the master display, the patient portal, any other suitable display, or combination thereof.

[0740] The processing device 3702 can be further configured to use the slave sensor data (e.g., the slave force measurements) to provide a corresponding level of measured force to the master device 3126. In other words, while using the master device 3126, the healthcare provider can essentially feel the measured levels of force exerted by the patient’s one or more body parts during the remote examination.

[0741] As the healthcare provider is virtually examining the patient, the processing device 3702 can use the master sensor data to generate and transmit the manipulation instruction (e.g., a measured level of force) to manipulate the treatment device 3106. In other words, as the healthcare provider applies more force pressure) to the master device 3126, the master sensors 3128 can detect the measured level of force and instruct the treatment device 3106 to apply a correlated measured level of force. In some embodiments, the measured level of force can be based on a proximity of the master device 3126 to the representation. In other words, as the healthcare provider manipulates the master device 3126 closer to the representation and/or within the representation of the treatment device 3126 and/or the patient’s one or more body parts, the master sensors 3128 can detect that the measured force has increased. In some embodiments, the input device 3716 can comprise a pressure gradient. Using the pressure gradient, the processing device 3702 can be configured to cause the slave pressure system 3110 to apply one or more measured levels of force to one or more sections 3210 of the treatment device 3106.

[0742] In another exemplary embodiment, the computer system 3700 may include the input device 3716 (e.g., the treatment device 3106) and the control system comprising the processing device 3702 (e.g., the slave processing device) operatively coupled to the input device 3716 and the master device 3126. The system 3700 may comprise one or more memory devices (e.g., main memory 3704, data storage device 3708, etc.) operatively coupled to the processing device 3702. The one or more memory devices can be configured to store instructions 3722. The processing device 3702 can be configured to execute the instructions 3722 to receive the slave sensor data from the one or more slave sensors 3108, to transmit the slave sensor data, to receive the manipulation instruction, and to use the manipulation instruction to activate the slave pressure system 3110. The instructions can be executed in real-time or near real-time.

[0743] In yet another embodiment, the computer system 3700 may include one or more input devices 3716 (e.g., the master console 3124 comprising the master device 3126, the treatment device 3106, etc.) and the control system comprising one or more processing devices 3702 (e.g., the master processing device, the slave processing device) operatively coupled to the input devices 3716. For example, the master processing device may be operatively coupled to the master console 3124 and the slave processing device may be operatively coupled to the treatment device 3106. The system 3700 may comprise one or more memory devices (e.g., master memory coupled to the master processing device, slave memory coupled to the slave processing device, etc.) operatively coupled to the one or more processing devices 3702. The one or more memory devices can be configured to store instructions 3722 (e.g., master instructions, slave instructions, etc.). The one or more processing devices 3702 (e.g., the master processing device) can be configured to execute the master instructions 3722 to receive the slave sensor data from the slave processing device, use a manipulation of the master device 3126 to generate a manipulation instruction, and transmit the manipulation instruction to the slave processing device. The one or more processing devices 3702 (e.g., the slave processing device) canbe configured to execute the slave instructions 3722 to receive the slave sensor data from the one or more slave sensors, to transmit the slave sensor data to the master processing device, to receive the manipulation instruction from the master processing device, and to use the manipulation instruction to activate the slave pressure system. The instructions can be executed in real-time or near real-time.

[0744] FIG. 31 is not intended to be limiting: the system 3700 may include more or fewer components than those illustrated in FIG. 31.

[0745] Any of the systems and methods described in this disclosure may be used in connection with rehabilitation. Unless expressly stated otherwise, is to be understood that rehabilitation includes prehabilitation (also referred to as "pre-habilitation" or "prehab"). Prehabilitation may be used as a preventative procedure or as a pre-surgical or pre-treatment procedure. Prehabilitation may include any action performed by or on a patient (or directed to be performed by or on a patient, including, without limitation, remotely or distally through telemedicine) to, without limitation, prevent or reduce a likelihood of injury (e.g., prior to the occurrence of the injury); improve recovery time subsequent to surgery; improve strength subsequent to surgery; or any of the foregoing with respect to any non-surgical clinical treatment plan to be undertaken for the purpose of ameliorating or mitigating injury, dysfunction, or other negative consequence of surgical or non-surgical treatment on any external or internal part of a patient's body. For example, a mastectomy may require prehabilitation to strengthen muscles or muscle groups affected directly or indirectly by the mastectomy. As a further non-limiting example, the removal of an intestinal tumor, the repair of a hernia, open-heart surgery or other procedures performed on internal organs or structures, whether to repair those organs or structures, to excise them or parts of them, to treat them, etc., can require cutting through and harming numerous muscles and muscle groups in or about, without limitation, the abdomen, the ribs and/or the throracic cavity. Prehabilitation can improve a patient's speed of recovery, measure of quality of life, level of pain, etc. in all of the foregoing procedures. In one embodiment of prehabilitation, a pre-surgical procedure or a pre-non-surgical-treatment may include one or more sets of exercises for a patient to perform prior to such procedure or treatment. The patient may prepare an area of his or her body for the surgical procedure by performing the one or more sets of exercises, thereby strengthening muscle groups, improving existing and/or establishing new muscle memory, enhancing mobility, improving blood flow, and/or the like.

[0746] In some embodiments, the systems and methods described herein may use artificial intelligence and/or machine learning to generate a prehabilitation treatment plan for a user. Additionally, or alternatively, the systems and methods described herein may use artificial intelligence and/or machine learning to recommend an optimal exercise machine configuration for a user. For example, a data model may be trained on historical data such that the data model may be provided with input data relating to the user and may generate output data indicative of a recommended exercise machine configuration for a specific user. Additionally, or alternatively, the systems and methods described herein may use machine learning and/or artificial intelligence to generate other types of recommendations relating to prehabilitation, such as recommended reading material to educate the patient, a recommended health professional specialist to contact, and/or the like.

[0747] Consistent with the above disclosure, the examples of systems and methods enumerated in the following clauses are specifically contemplated and are intended as a non-limiting set of examples.

[0748] Clause 1.2. A computer-implemented system, comprising: a treatment device comprising one or more slave sensors and a slave pressure system, the treatment device configured to be manipulated while a patient performs a treatment plan; a master console comprising a master device; a user interface comprising an output device configured to present telemedicine information associated with a telemedicine session; and a control system comprising one or more processing devices operatively coupled to the master console and the treatment device, wherein the one or more processing devices are configured to: receive slave sensor data from the one or more slave sensors; use a manipulation of the master device to generate a manipulation instruction; transmit the manipulation instruction; and during the telemedicine session, use the manipulation instruction to cause the slave pressure system to activate.

[0749] Clause 2.2. The computer-implemented system of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the master device comprises a master pressure system; and wherein, using the slave force measurements, the one or more processing devices are further configured to activate the master pressure system.

[0750] Clause 3.2. The computer-implemented system of any clause herein, wherein the master device comprises a pressure gradient; and wherein, using the pressure gradient, the one or more processing devices are configured to cause the slave pressure system to apply one or more measured levels of force to one or more sections of the treatment device.

[0751] Clause 4.2. The computer-implemented system of any clause herein, wherein the treatment device comprises at least one of a physical therapy device, a brace, a mat, and a wrap.

[0752] Clause 5.2. A system for a remote examination of a patient, comprising: a master console comprising a master device; a treatment device comprising one or more slave sensors and a slave pressure system; and a control system comprising one or more processing devices operatively coupled to the master console and the treatment device, wherein the one or more processing devices are configured to: receive slave sensor data from the one or more slave sensors; use a manipulation of the master device to generate a manipulation instruction; transmit the manipulation instruction; and use the manipulation instruction to cause the slave pressure system to activate.

[0753] Clause 6.2. The system of any clause herein, wherein the master device comprises master sensors for detecting master sensor data correlating with the manipulation; and wherein the manipulation instruction is based on the master sensor data.

[0754] Clause 7.2. The system of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the master device comprises a master pressure system; and wherein, using the slave force measurements, the one or more processing devices are further configured to activate the master pressure system.

[0755] Clause 8.2. The system of any clause herein, further comprising: a second master device comprising a second master pressure system; wherein the slave sensor data comprises slave force measurements; and wherein, using the slave force measurements, the one or more processing devices are further configured to activate the second master pressure system.

[0756] Clause 9.2. The system of any clause herein, wherein the one or more processing devices are further configured to: use the slave sensor data to transmit an augmented image to a master display.

[0757] Clause 10.2. The system of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the augmented image comprises one or more pressure indicators; and wherein the one or more pressure indicators are based on the slave force measurements.

[0758] Clause 11.2. The system of any clause herein, wherein the slave sensor data comprises slave temperature measurements; wherein the augmented image comprises one or more temperature indicators; and wherein the one or more temperature indicators are based on the slave temperature measurements. [0759] Clause 12.2. The system of any clause herein, wherein the master device comprises a pressure gradient; and wherein, using the pressure gradient, the one or more processing devices are configured to cause the slave pressure system to apply one or more measured levels of force to one or more sections of the treatment device.

[0760] Clause 13.2. The system of any clause herein, wherein the augmented image comprises a representation of at least one of the treatment device and a body part of the patient, and wherein the representation is in 2D or 3D.

[0761] Clause 14.2. The system of any clause herein, wherein the manipulation instruction comprises a measured level of force; and wherein the measured level of force is based on a proximity of the master device to the representation. [0762] Clause 15.2. The system of any clause herein, wherein the one or more processing devices are further configured to: transmit the manipulation instruction in real-time or near real-time; and cause the slave pressure system to activate in real-time or near real-time.

[0763] Clause 16.2. The system of any clause herein, wherein the master device comprises at least one of a glove device, a joystick, and a model of the treatment device.

[0764] Clause 17.2. The system of any clause herein, wherein the treatment device comprises at least one of a physical therapy device, a brace, a mat, and a wrap. [0765] Clause 18.2. The system of any clause herein, further comprising one or more memory devices operatively coupled to the one or more processing devices, wherein the one or more memory devices stores instructions, and wherein the one or more processing devices are configured to execute the instructions.

[0766] Clause 19.2. A method for operating a system for remote examination of a patient, comprising: receiving slave sensor data from one or more slave sensors; based on a manipulation of a master device, generating a manipulation instruction; transmitting the manipulation instruction; and based on the manipulation instruction, causing a slave pressure system to activate.

[0767] Clause 20.2. The method of any clause herein, wherein the master device comprises master sensors for detecting master sensor data correlating with the manipulation; and wherein the manipulation instruction is based on the master sensor data.

[0768] Clause 21.2. The method of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the master device comprises a master pressure system; and wherein, based on the slave force measurements, activating the master pressure system.

[0769] Clause 22.2. The method of any clause herein, further comprising: a second master device comprising a second master pressure system; wherein the slave sensor data comprises slave force measurements; and wherein, using the slave force measurements, the one or more processing devices are further configured to activate the second master pressure system.

[0770] Clause 23.2. The method of any clause herein, further comprising: use the slave sensor data to transmitting an augmented image.

[0771] Clause 24.2. The method of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the augmented image comprises one or more pressure indicators; and wherein the one or more pressure indicators are based on the slave force measurements.

[0772] Clause 25.2. The method of any clause herein, wherein the slave sensor data comprises slave temperature measurements; wherein the augmented image comprises one or more temperature indicators; and wherein the one or more temperature indicators are based on the slave temperature measurements. [0773] Clause 26.2. The method of any clause herein, wherein the master device comprises a pressure gradient; and wherein, using the pressure gradient, causing the slave pressure system to apply one or more measured levels of force to one or more sections of the treatment device.

[0774] Clause 27.2. The method of any clause herein, wherein the augmented image comprises a representation of at least one of the treatment device and a body part of the patient, and wherein the representation is in 2D or 3D.

[0775] Clause 28.2. The method of any clause herein, wherein the manipulation instruction comprises a measured level of force; and wherein the measured level of force is based on a proximity of the master device to the representation. [0776] Clause 29.2. The method of any clause herein, further comprising: transmitting the manipulation instruction in real-time or near real-time; and causing the slave pressure system to activate in real-time or near real-time.

[0777] Clause 30.2. The method of any clause herein, wherein the master device comprises at least one of a glove device, a joystick, and a model of the treatment device.

[0778] Clause 31.2. The method of any clause herein, wherein the treatment device comprises at least one of a physical therapy device, a brace, a mat, and a wrap.

[0779] Clause 32.2. A tangible, non-transitoiy computer-readable storage medium storing instructions that, when executed, cause a processing device to: receive slave sensor data from one or more slave sensors; based on a manipulation of a master device, generate a manipulation instruction; transmit the manipulation instruction; and use the manipulation instruction to cause a slave pressure system to activate.

[0780] Clause 33.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the master device comprises master sensors for detecting master sensor data correlating with the manipulation; and wherein the manipulation instruction is based on the master sensor data.

[0781] Clause 34.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the master device comprises a master pressure system; and wherein, based on the slave force measurements, activate the master pressure system.

[0782] Clause 35.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, further comprising: a second master device comprising a second master pressure system; wherein the slave sensor data comprises slave force measurements; and wherein, using the slave force measurements, the one or more processing devices are further configured to activate the second master pressure system.

[0783] Clause 36.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the instructions further cause the processing device to: use the slave sensor data to transmit an augmented image.

[0784] Clause 37.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the augmented image comprises one or more pressure indicators; and wherein the one or more pressure indicators are based on the slave force measurements.

[0785] Clause 38.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the slave sensor data comprises slave temperature measurements; wherein the augmented image comprises one or more temperature indicators; and wherein the one or more temperature indicators are based on the slave temperature measurements. [0786] Clause 39.2. The tangible, non-transitor computer-readable storage medium of any clause herein, wherein the master device comprises a pressure gradient; and wherein, using the pressure gradient, cause the slave pressure system to apply one or more measured levels of force to one or more sections of the treatment device.

[0787] Clause 40.2. The tangible, non-transitory computer-readable storage medium of any clause herein, wherein the augmented image comprises a representation of at least one of the treatment device and a body part of the patient, and wherein the representation is in 2D or 3D.

[0788] Clause 41.2. The tangible, non-transitory computer-readable storage medium of any clause herein, wherein the manipulation instruction comprises a measured level of force; and wherein the measured level of force is based on a proximity of the master device to the representation. [0789] Clause 42.2. The tangible, non-transitory computer-readable storage medium of any clause herein, wherein the instructions further cause the processing device to: transmit the manipulation instruction in real-time or near real-time; and cause the slave pressure system to activate in real-time or near real-time.

[0790] Clause 43.2. The tangible, non-transitory computer-readable storage medium of any clause herein, wherein the master device comprises at least one of a glove device, a joystick, and a model of the treatment device.

[0791] Clause 44.2. The tangible, non-transitory computer-readable storage medium of any clause herein, wherein the treatment device comprises at least one of a physical therapy device, a brace, a mat, and a wrap. [0792] Clause 45.2. A system for a remote examination of a patient, comprising: a master console comprising a master device; a treatment device comprising one or more slave sensors and a slave pressure system; and a control system comprising one or more processing devices operatively coupled to the master console and the treatment device, wherein the one or more processing devices are configured to: receive slave sensor data from the one or more slave sensors; transmit the slave sensor data; receive a manipulation instruction; and use the manipulation instruction to activate the slave pressure system.

[0793] Clause 46.2. The system of any clause herein, wherein the manipulation instruction is based on a manipulation of the master device.

[0794] Clause 47.2. The system of any clause herein, wherein the master device comprises master sensors for detecting master sensor data correlating with the manipulation; and wherein the manipulation instruction is based on the master sensor data.

[0795] Clause 48.2. The system of any clause herein, further comprising: a second master device comprising a second master pressure system; wherein the slave sensor data comprises slave force measurements; and wherein, using the slave force measurements, the one or more processing devices are further configured to activate the second master pressure system. [0796] Clause 49.2. The system of any clause herein, wherein the one or more processing devices are further configured to: use the slave sensor data to transmit an augmented image to the master console.

[0797] Clause 50.2. The system of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the master device comprises a master pressure system; and wherein, using the slave force measurements, the one or more processing devices are further configured to cause the master pressure system to activate.

[0798] Clause 51.2. The system of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the augmented image comprises one or more pressure indicators; and wherein the one or more pressure indicators are based on the slave force measurements.

[0799] Clause 52.2. The system of any clause herein, wherein the slave sensor data comprises slave temperature measurements; wherein the augmented image comprises one or more temperature indicators; and wherein the one or more temperature indicators are based on the slave temperature measurements. [0800] Clause 53.2. The system of any clause herein, wherein the master device comprises a pressure gradient; and wherein, using the pressure gradient, activating the slave pressure system comprises applying one or more measured levels of force to one or more sections of the treatment device.

[0801] Clause 54.2. The system of any clause herein, wherein the augmented image comprises a representation of at least one of the treatment device and a body part of the patient, and wherein the representation is in 2D or 3D.

[0802] Clause 55.2. The system of any clause herein, wherein the manipulation instruction comprises a measured level of force; and wherein the measured level of force is based on a proximity of the master device to the representation. [0803] Clause 56.2. The system of any clause herein, wherein the one or more processing devices are further configured to: receive the manipulation instruction in real-time or near real-time; and activate the slave pressure system in real-time or near real-time.

[0804] Clause 57.2. The system of any clause herein, wherein the master device comprises at least one of a glove device, a joystick, and a model of the treatment device.

[0805] Clause 58.2. The system of any clause herein, wherein the treatment device comprises at least one of a physical therapy device, a brace, a mat, and a wrap.

[0806] Clause 59.2. The system of any clause herein, further comprising one or more memory devices operatively coupled to the one or more processing devices, wherein the one or more memory devices stores instructions, and wherein the one or more processing devices are configured to execute the instructions.

[0807] Clause 60.2. A method for operating a system for remote examination of a patient, comprising: receiving slave sensor data from one or more slave sensors; transmitting the slave sensor data; receiving a manipulation instruction; and based on the manipulation instruction, activating a slave pressure system.

[0808] Clause 61.2. The method of any clause herein, wherein the manipulation instruction is based on a manipulation of a master device.

[0809] Clause 62.2. The method of any clause herein, wherein the master device comprises master sensors for detecting master sensor data correlating with the manipulation; and wherein the manipulation instruction is based on the master sensor data.

[0810] Clause 63.2. The method of any clause herein, further comprising: a second master device comprising a second master pressure system; wherein the slave sensor data comprises slave force measurements; and wherein, using the slave force measurements, the one or more processing devices are further configured to activate the second master pressure system.

[0811] Clause 64.2. The method of any clause herein, further comprising: use the slave sensor data to transmitting an augmented image to the master console.

[0812] Clause 65.2. The method of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the master device comprises a master pressure system; and wherein, based on the slave force measurements, causing the master pressure system to activate. [0813] Clause 66.2. The method of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the augmented image comprises one or more pressure indicators; and wherein the one or more pressure indicators are based on the slave force measurements.

[0814] Clause 67.2. The method of any clause herein, wherein the slave sensor data comprises slave temperature measurements; wherein the augmented image comprises one or more temperature indicators; and wherein the one or more temperature indicators are based on the slave temperature measurements. [0815] Clause 68.2. The method of any clause herein, wherein the master device comprises a pressure gradient; and wherein, using the pressure gradient, activating the slave pressure system comprises applying one or more measured levels of force to one or more sections of the treatment device.

[0816] Clause 69.2. The method of any clause herein, wherein the augmented image comprises a representation of at least one of the treatment device and a body part of the patient, and wherein the representation is in 2D or 3D.

[0817] Clause 70.2. The method of any clause herein, wherein the manipulation instruction comprises a measured level of force; and wherein the measured level of force is based on a proximity of the master device to the representation. [0818] Clause 71.2. The method of any clause herein, further comprising: receiving the manipulation instruction in real-time or near real-time; and activating the slave pressure system in real-time or near real-time.

[0819] Clause 72.2. The method of any clause herein, wherein the master device comprises at least one of a glove device, a joystick, and a model of the treatment device.

[0820] Clause 73.2. The method of any clause herein, wherein the treatment device comprises at least one of a physical therapy device, a brace, a mat, and a wrap.

[0821] Clause 74.2. A tangible, non-transitoiy computer-readable storage medium storing instructions that, when executed, cause a processing device to: receive slave sensor data from one or more slave sensors; transmit the slave sensor data; receive a manipulation instruction; and use the manipulation instruction to activate a slave pressure system.

[0822] Clause 75.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the manipulation instruction is based on a manipulation of a master device.

[0823] Clause 76.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the master device comprises master sensors for detecting master sensor data correlating with the manipulation; and wherein the manipulation instruction is based on the master sensor data.

[0824] Clause 77.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, further comprising: a second master device comprising a second master pressure system; wherein the slave sensor data comprises slave force measurements; and wherein, using the slave force measurements, the one or more processing devices are further configured to activate the second master pressure system.

[0825] Clause 78.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the instructions further cause the processing device to: use the slave sensor data to transmit an augmented image to the master console.

[0826] Clause 79.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the master device comprises a master pressure system; and wherein, based on the slave force measurements, cause the master pressure system to activate.

[0827] Clause 80.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the augmented image comprises one or more pressure indicators; and wherein the one or more pressure indicators are based on the slave force measurements.

[0828] Clause 81.2. The method of any clause herein, wherein the slave sensor data comprises slave temperature measurements; wherein the augmented image comprises one or more temperature indicators; and wherein the one or more temperature indicators are based on the slave temperature measurements. [0829] Clause 82.2. The method of any clause herein, wherein the master device comprises a pressure gradient; and wherein, using the pressure gradient, activating the slave pressure system comprises applying one or more measured levels of force to one or more sections of the treatment device.

[0830] Clause 83.2. The method of any clause herein, wherein the augmented image comprises a representation of at least one of the treatment device and a body part of the patient, and wherein the representation is in 2D or 3D.

[0831] Clause 84.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the manipulation instruction comprises a measured level of force; and wherein the measured level of force is based on a proximity of the master device to the representation. [0832] Clause 85.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the instructions further cause the processing device to: receive the manipulation instruction in real-time or near real-time; and activate the slave pressure system in real-time or near real-time.

[0833] Clause 86.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the master device comprises at least one of a glove device, a joystick, and a model of the treatment device.

[0834] Clause 87.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the treatment device comprises at least one of a physical therapy device, a brace, a mat, and a wrap. [0835] Clause 88.2. A system for a remote examination of a patient, comprising: a master console comprising a master device; a treatment device comprising one or more slave sensors and a slave pressure system; and a control system comprising a master processing device and a slave processing device, wherein the master processing device is operatively coupled to the master console and the slave processing device is operatively coupled to the treatment device; wherein the master processing device is configured to: receive slave sensor data from the slave processing device; use a manipulation of the master device to generate a manipulation instruction; and transmit the manipulation instruction to the slave processing device; and wherein the slave processing device is configured to: receive the slave sensor data from the one or more slave sensors; transmit the slave sensor data to the master processing device; receive the manipulation instruction from the master processing device; and use the manipulation instruction to activate the slave pressure system.

[0836] Clause 89.2. The system of any clause herein, wherein the master device comprises master sensors for detecting master sensor data correlating with the manipulation; and wherein the manipulation instruction is based on the master sensor data.

[0837] Clause 90.2. The system of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the master device comprises a master pressure system; and wherein, using the slave force measurements, the master processing device is further configured to activate the master pressure system.

[0838] Clause 91.2. The system of any clause herein, further comprising: a second master device comprising a second master pressure system; wherein the slave sensor data comprises slave force measurements; and wherein, using the slave force measurements, the master processing device is further configured to activate the second master pressure system.

[0839] Clause 92.2. The system of any clause herein, wherein the master processing device is further configured to: use the slave sensor data to transmit an augmented image to a master display.

[0840] Clause 93.2. The system of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the augmented image comprises one or more pressure indicators; and wherein the one or more pressure indicators are based on the slave force measurements.

[0841] Clause 94.2. The system of any clause herein, wherein the slave sensor data comprises slave temperature measurements; wherein the augmented image comprises one or more temperature indicators; and wherein the one or more temperature indicators are based on the slave temperature measurements. [0842] Clause 95.2. The system of any clause herein, wherein the master device comprises a pressure gradient; and wherein, using the pressure gradient, activating the slave pressure system comprises applying one or more measured levels of force to one or more sections of the treatment device.

[0843] Clause 96.2. The system of any clause herein, wherein the augmented image comprises a representation of at least one of the treatment device and a body part of the patient, and wherein the representation is in 2D or 3D.

[0844] Clause 97.2. The system of any clause herein, wherein the manipulation instruction comprises a measured level of force; and wherein the measured level of force is based on a proximity of the master device to the representation. [0845] Clause 98.2. The system of any clause herein, wherein the manipulation instruction is transmitted in real-time or near real-time; and wherein the slave pressure system is activated in real-time or near real-time.

[0846] Clause 99.2. The system of any clause herein, wherein the master device comprises at least one of a glove device, a joystick, and a model of the treatment device.

[0847] Clause 100.2. The system of any clause herein, wherein the treatment device comprises at least one of a physical therapy device, a brace, a mat, and a wrap.

[0848] Clause 101.2. The system of any clause herein, further comprising: a master memory device operatively coupled to the master processing device, wherein the master memory device stores master instructions, and wherein the master processing device is configured to execute the master instructions; and a slave memory device operatively coupled to the slave processing device, wherein the slave memory device stores slave instructions, and wherein the slave processing device is configured to execute the slave ins tractions.

[0849] Clause 102.2. A method for operating a remote examination of a patient, comprising: causing a master processing device to: receive slave sensor data from the slave processing device; use a manipulation of a master device to generate a manipulation instruction; and transmit the manipulation instruction to the slave processing device; and causing a slave processing device to: receive the slave sensor data from the one or more slave sensors; transmit the slave sensor data to the master processing device; receive the manipulation instruction from the master processing device; and use the manipulation instruction to activate the slave pressure system.

[0850] Clause 103.2. The method of any clause herein, wherein the master device comprises master sensors for detecting master sensor data correlating with the manipulation; and wherein the manipulation instruction is based on the master sensor data.

[0851] Clause 104.2. The method of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the master device comprises a master pressure system; and causing the master processing device, based on the slave force measurements, to activate the master pressure system.

[0852] Clause 105.2. The method of any clause herein, further comprising: a second master device comprising a second master pressure system; wherein the slave sensor data comprises slave force measurements; and wherein, using the slave force measurements, the master processing device is further configured to activate the second master pressure system.

[0853] Clause 106.2. The method of any clause herein, further causing the master processing device to: use the slave sensor data to transmit an augmented image to a master display.

[0854] Clause 107.2. The method of any clause herein, wherein the slave sensor data comprises slave force measurements; wherein the augmented image comprises one or more pressure indicators; and wherein the one or more pressure indicators are based on the slave force measurements.

[0855] Clause 108.2. The method of any clause herein, wherein the slave sensor data comprises slave temperature measurements; wherein the augmented image comprises one or more temperature indicators; and wherein the one or more temperature indicators are based on the slave temperature measurements. [0856] Clause 109.2. The method of any clause herein, wherein the master device comprises a pressure gradient; and wherein, using the pressure gradient, activating the slave pressure system comprises applying one or more measured levels of force to one or more sections of the treatment device.

[0857] Clause 110.2. The method of any clause herein, wherein the augmented image comprises a representation of at least one of the treatment device and a body part of the patient, and wherein the representation is in 2D or 3D.

[0858] Clause 111.2. The method of any clause herein, wherein the manipulation instruction comprises a measured level of force; and wherein the measured level of force is based on a proximity of the master device to the representation. [0859] Clause 112.2. The method of any clause herein, wherein the manipulation instruction is transmitted in real-time or near real-time; and wherein the slave pressure system is activated in real-time or near real-time.

SYSTEM AND METHOD FOR USING ARTIFICIAL INTELLIGENCE IN TELEMEDICINE-ENABLED HARDWARE TO OPTIMIZE REHABILITATIVE ROUTINES CAPABLE OF ENABLING REMOTE REHABILITATIVE COMPLIANCE

[0860] Determining a treatment plan for a patient having certain characteristics (e.g., vital-sign or other measurements; performance; demographic; psycho graphic; geographic; diagnostic; measurement- or test-based; medically historic; behavioral historic; cognitive; etiologic; cohort-associative; differentially diagnostic; surgical, physically therapeutic, microbiome related, pharmacologic and other treatment(s) recommended; arterial blood gas and/or oxygenation levels or percentages; glucose levels; blood oxygen levels; insulin levels; psycho graphics; etc.) may be a technically challenging problem. For example, a multitude of information may be considered when determining a treatment plan, which may result in inefficiencies and inaccuracies in the treatment plan selection process. In a rehabilitative setting, some of the multitude of information considered may include characteristics of the patient such as personal information, performance information, and measurement information. The personal information may include, e.g., demographic, psychographic or other information, such as an age, a weight, a gender, a height, a body mass index, a medical condition, a familial medication history, an injury, a medical procedure, a medication prescribed, or some combination thereof. The performance information may include, e.g., an elapsed time of using a treatment apparatus, an amount of force exerted on a portion of the treatment apparatus, a range of motion achieved on the treatment apparatus, a movement speed of a portion of the treatment apparatus, a duration of use of the treatment apparatus, an indication of a plurality of pain levels using the treatment apparatus, or some combination thereof. The measurement information may include, e.g., a vital sign, a respiration rate, a heartrate, a temperature, a blood pressure, a glucose level, arterial blood gas and/or oxygenation levels or percentages, or other biomarker, or some combination thereof. It may be desirable to process and analyze the characteristics of a multitude of patients, the treatment plans performed for those patients, and the results of the treatment plans for those patients. [0861] Further, another technical problem may involve distally treating, via a computing apparatus during a telemedicine session, a patient from a location different than a location at which the patient is located. An additional technical problem is controlling or enabling, from the different location, the control of a treatment apparatus used by the patient at the patient’s location. Oftentimes, when a patient undergoes rehabilitative surgery (e.g., knee surgery), a healthcare provider may prescribe a treatment apparatus to the patient to use to perform a treatment protocol at their residence or at any mobile location or temporary domicile. A healthcare provider may refer to a doctor, physician assistant, nurse, chiropractor, dentist, physical therapist, acupuncturist, physical trainer, or the like. A healthcare provider may refer to any person with a credential, license, degree, or the like in the field of medicine, physical therapy, rehabilitation, or the like.

[0862] When the healthcare provider is located in a different location from the patient and the treatment apparatus, it may be technically challenging for the healthcare provider to monitor the patient’s actual progress (as opposed to relying on the patient’s word about their progress) in using the treatment apparatus, modify the treatment plan according to the patient’s progress, adapt the treatment apparatus to the personal characteristics of the patient as the patient performs the treatment plan, and the like.

[0863] Additionally, or alternatively, a computer-implemented system may be used in connection with a treatment apparatus to treat the patient, for example, during a telemedicine session. For example, the treatment apparatus can be configured to be manipulated by a user while the user is performing a treatment plan. The system may include a patient interface that includes an output device configured to present telemedicine information associated with the telemedicine session. During the telemedicine session, the processing device can be configured to receive treatment data pertaining to the user. The treatment data may include one or more characteristics of the user. The processing device may be configured to determine, via one or more trained machine learning models, at least one respective measure of benefit which one or more exercise regimens provide the user. Determining the respective measure of benefit may be based on the treatment data. The processing device may be configured to determine, via the one or more trained machine learning models, one or more probabilities of the user complying with the one or more exercise regimens. The processing device may be configured to transmit the treatment plan, for example, to a computing device. The treatment plan can be generated based on the one or more probabilities and the respective measure of benefit which the one or more exercise regimens provide the user.

[0864] Accordingly, systems and methods, such as those described herein, that receive treatment data pertaining to the user of the treatment apparatus during telemedicine session, may be desirable.

[0865] In some embodiments, the systems and methods described herein may be configured to use a treatment apparatus configured to be manipulated by an individual while performing a treatment plan. The individual may include a user, patient, or other a person using the treatment apparatus to perform various exercises for prehabilitation, rehabilitation, stretch training, and the like. The systems and methods described herein may be configured to use and/or provide a patient interface comprising an output device configured to present telemedicine information associated with a telemedicine session.

[0866] In some embodiments, during an adaptive telemedicine session, the systems and methods described herein may be configured to use artificial intelligence and/or machine learning to assign patients to cohorts and to dynamically control a treatment apparatus based on the assignment. The term “adaptive telemedicine” may refer to a telemedicine session dynamically adapted based on one or more factors, criteria, parameters, characteristics, or the like. The one or more factors, criteria, parameters, characteristics, or the like may pertain to the user (e.g., heartrate, blood pressure, perspiration rate, pain level, or the like), the treatment apparatus (e.g., pressure, range of motion, speed of motor, etc.), details of the treatment plan, and so forth.

[0867] In some embodiments, numerous patients may be prescribed numerous treatment apparatuses because the numerous patients are recovering from the same medical procedure and/or suffering from the same injury. The numerous treatment apparatusus may be provided to the numerous patients. The treatment apparatuses may be used by the patients to perform treatment plans in their residences, at gyms, at rehabilitative centers, at hospitals, or at any suitable locations, including permanent or temporary domiciles.

[0868] In some embodiments, the treatment apparatuses may be communicatively coupled to a server. Characteristics of the patients, including the treatment data, may be collected before, during, and/or after the patients perform the treatment plans. For example, any or each of the personal information, the performance information, and the measurement information may be collected before, during, and/or after a patient performs the treatment plans. The results (e.g., improved performance or decreased performance) of performing each exercise may be collected from the treatment apparatus throughout the treatment plan and after the treatment plan is performed. The parameters, settings, configurations, etc. (e.g., position of pedal, amount of resistance, etc.) of the treatment apparatus may be collected before, during, and/or after the treatment plan is performed. [0869] Each characteristic of the patient, each result, and each parameter, setting, configuration, etc. may be timestamped and may be correlated with a particular step or set of steps in the treatment plan. Such a technique may enable the determination of which steps in the treatment plan lead to desired results (e.g., improved muscle strength, range of motion, etc.) and which steps lead to diminishing returns (e.g., continuing to exercise after 3 minutes actually delays or harms recovery).

[0870] Data may be collected from the treatment apparatuses and/or any suitable computing device (e.g., computing devices where personal information is entered, such as the interface of the computing device described herein, a clinician interface, patient interface, or the like) over time as the patients use the treatment apparatuses to perform the various treatment plans. The data that may be collected may include the characteristics of the patients, the treatment plans performed by the patients, and the results of the treatment plans. Further, the data may include characteristics of the treatment apparatus. The characteristics of the treatment apparatus may include a make (e.g., identity of entity that designed, manufactured, etc. the treatment apparatus) of the treatment apparatus, a model (e.g., model number or other identifier of the model) of the treatment apparatus, a year (e.g., year the treatment apparatus was manufactured) of the treatment apparatus, operational parameters (e.g., engine temperature during operation, a respective status of each of one or more sensors included in or associated with the treatment apparatus, vibration measurements of the treatment apparatus in operation, measurements of static and/or dynamic forces exerted internally or externally on the treatment apparatus, etc.) of the treatment apparatus, settings (e.g., range of motion setting, speed setting, required pedal force setting, etc.) of the treatment apparatus, and the like. The data collected from the treatment apparatuses, computing devices, characteristics of the user, characteristics of the treatment apparatus, and the like may be collectively referred to as “treatment data” herein.

[0871] In some embodiments, the data may be processed to group certain people into cohorts. The people may be grouped by people having certain or selected similar characteristics, treatment plans, and results of performing the treatment plans. For example, athletic people having no medical conditions who perform a treatment plan (e.g., use the treatment apparatus for 30 minutes a day 5 times a week for 3 weeks) and who fully recover may be grouped into a first cohort. Older people who are classified obese and who perform a treatment plan (e.g., use the treatment plan for 10 minutes a day 3 times a week for 4 weeks) and who improve their range of motion by 75 percent may be grouped into a second cohort.

[0872] In some embodiments, an artificial intelligence engine may include one or more machine learning models that are trained using the cohorts. In some embodiments, the artificial intelligence engine may be used to identify trends and/or patterns and to define new cohorts based on achieving desired results from the treatment plans and machine learning models associated therewith may be trained to identify such trends and/or patterns and to recommend and rank the desirability of the new cohorts. For example, the one or more machine learning models may be trained to receive an input of characteristics of a new patient and to output a treatment plan for the patient that results in a desired result. The machine learning models may match a pattern between the characteristics of the new patient and at least one patient of the patients included in a particular cohort. When a pattern is matched, the machine learning models may assign the new patient to the particular cohort and select the treatment plan associated with the at least one patient. The artificial intelligence engine may be configured to control, distally and based on the treatment plan, the treatment apparatus while the new patient uses the treatment apparatus to perform the treatment plan.

[0873] As may be appreciated, the characteristics of the new patient (e.g., a new user) may change as the new patient uses the treatment apparatus to perform the treatment plan. For example, the performance of the patient may improve quicker than expected for people in the cohort to which the new patient is currently assigned. Accordingly, the machine learning models may be trained to dynamically reassign, based on the changed characteristics, the new patient to a different cohort that includes people having characteristics similar to the now-changed characteristics as the new patient. For example, a clinically obese patient may lose weight and no longer meet the weight criterion for the initial cohort, result in the patient’s being reassigned to a different cohort with a different weight criterion.

[0874] A different treatment plan may be selected for the new patient, and the treatment apparatus may be controlled, distally (e.g., which may be referred to as remotely) and based on the different treatment plan, the treatment apparatus while the new patient uses the treatment apparatus to perform the treatment plan. Such techniques may provide the technical solution of distally controlling a treatment apparatus.

[0875] Further, the systems and methods described herein may lead to faster recovery times and/or better results for the patients because the treatment plan that most accurately fits their characteristics is selected and implemented, in real-time, at any given moment. “Real-time” may also refer to near real-time, which may be less than 10 seconds or any reasonably proximate difference between two different times. As described herein, the term “results” may refer to medical results or medical outcomes. Results and outcomes may refer to responses to medical actions. The term “medical action(s)” may refer to any suitable action performed by the healthcare provider, and such action or actions may include diagnoses, prescription of treatment plans, prescription of treatment apparatusus, and the making, composing and/or executing of appointments, telemedicine sessions, prescription of medicines, telephone calls, emails, text messages, and the like.

[0876] Depending on what result is desired, the artificial intelligence engine may be trained to output several treatment plans. For example, one result may include recovering to a threshold level (e.g., 75% range of motion) in a fastest amount of time, while another result may include fully recovering (e.g., 100% range of motion) regardless of the amount of time. The data obtained from the patients and sorted into cohorts may indicate that a first treatment plan provides the first result for people with characteristics similar to the patient’ s, and that a second treatment plan provides the second result for people with characteristics similar to the patient. [0877] Further, the artificial intelligence engine may be trained to output treatment plans that are not optimal i.e., sub-optimal, nonstandard, or otherwise excluded (all referred to, without limitation, as “excluded treatment plans”) for the patient. For example, if a patient has high blood pressure, a particular exercise may not be approved or suitable for the patient as it may put the patient at unnecessary risk or even induce a hypertensive crisis and, accordingly, that exercise may be flagged in the excluded treatment plan for the patient. In some embodiments, the artificial intelligence engine may monitor the treatment data received while the patient (e.g., the user) with, for example, high blood pressure, uses the treatment apparatus to perform an appropriate treatment plan and may modify the appropriate treatment plan to include features of an excluded treatment plan that may provide beneficial results for the patient if the treatment data indicates the patient is handling the appropriate treatment plan without aggravating, for example, the high blood pressure condition of the patient. In some embodiments, the artificial intelligence engine may modify the treatment plan if the monitored data shows the plan to be inappropriate or counterproductive for the user.

[0878] In some embodiments, the treatment plans and/or excluded treatment plans may be presented, during a telemedicine or telehealth session, to a healthcare provider. The healthcare provider may select a particular treatment plan for the patient to cause that treatment plan to be transmitted to the patient and/or to control, based on the treatment plan, the treatment apparatus. In some embodiments, to facilitate telehealth or telemedicine applications, including remote diagnoses, determination of treatment plans and rehabilitative and/or pharmacologic prescriptions, the artificial intelligence engine may receive and/or operate distally from the patient and the treatment apparatus.

[0879] In such cases, the recommended treatment plans and/or excluded treatment plans may be presented simultaneously with a video of the patient in real-time or near real-time during a telemedicine or telehealth session on a user interface of a computing apparatus of a healthcare provider. The video may also be accompanied by audio, text and other multimedia information and/or other sensorial or perceptive (e.g., tactile, gustatory, haptic, pressure-sensing-based or electromagnetic (e.g., neurostimulation). Real-time may refer to less than or equal to 2 seconds. Near real-time may refer to any interaction of a sufficiently short time to enable two individuals to engage in a dialogue via such user interface, and will generally be less than 10 seconds (or any suitably proximate difference between two different times) but greater than 2 seconds. Presenting the treatment plans generated by the artificial intelligence engine concurrently with a presentation of the patient video may provide an enhanced user interface because the healthcare provider may continue to visually and/or otherwise communicate with the patient while also reviewing the treatment plans on the same user interface. The enhanced user interface may improve the healthcare provider’s experience using the computing device and may encourage the healthcare provider to reuse the user interface. Such a technique may also reduce computing resources (e.g., processing, memory, network) because the healthcare provider does not have to switch to another user interface screen to enter a query for a treatment plan to recommend based on the characteristics of the patient. The artificial intelligence engine may be configured to provide, dynamically on the fly, the treatment plans and excluded treatment plans.

[0880] In some embodiments, the treatment plan may be modified by a healthcare provider. For example, certain procedures may be added, modified or removed. In the telehealth scenario, there are certain procedures that may not be performed due to the distal nature of a healthcare provider using a computing device in a different physical location than a patient.

[0881] A technical problem may relate to the information pertaining to the patient’s medical condition being received in disparate formats. For example, a server may receive the information pertaining to a medical condition of the patient from one or more sources (e.g., from an electronic medical record (EMR) system, application programming interface (API), or any suitable system that has information pertaining to the medical condition of the patient). That is, some sources used by various healthcare provider entities may be installed on their local computing devices and, additionally and/or alternatively, may use proprietary formats. Accordingly, some embodiments of the present disclosure may use an API to obtain, via interfaces exposed by APIs used by the sources, the formats used by the sources. In some embodiments, when information is received from the sources, the API may map and convert the format used by the sources to a standardized (i.e., canonical) format, language and/or encoding (“format” as used herein will be inclusive of all of these terms) used by the artificial intelligence engine. Further, the information converted to the standardized format used by the artificial intelligence engine may be stored in a database accessed by the artificial intelligence engine when the artificial intelligence engine is performing any of the techniques disclosed herein. Using the information converted to a standardized format may enable a more accurate determination of the procedures to perform for the patient. [0882] The various embodiments disclosed herein may provide a technical solution to the technical problem pertaining to the patient’s medical condition information being received in disparate formats. For example, a server may receive the information pertaining to a medical condition of the patient from one or more sources (e.g., from an electronic medical record (EMR) system, application programming interface (API), or any suitable system that has information pertaining to the medical condition of the patient). The information may be converted from the format used by the sources to the standardized format used by the artificial intelligence engine. Further, the information converted to the standardized format used by the artificial intelligence engine may be stored in a database accessed by the artificial intelligence engine when performing any of the techniques disclosed herein. The standardized information may enable generating optimal treatment plans, where the generating is based on treatment plans associated with the standardized information. The optimal treatment plans may be provided in a standardized format that can be processed by various applications (e.g., telehealth) executing on various computing devices of healthcare providers and/or patients.

[0883] A technical problem may include a challenge of generating treatment plans for users, such treatment plans comprising exercises that balance a measure of benefit which the exercise regimens provide to the user and the probability the user complies with the exercises (or the distinct probabilities the user complies with each of the one or more exercises). By selecting exercises having higher compliance probabilities for the user, more efficient treatment plans may be generated, and these may enable less frequent use of the treatment apparatus and therefore extend the lifetime or time between recommended maintenance of or needed repairs to the treatment apparatus. For example, if the user consistently quits a certain exercise but yet attempts to perform the exercise multiple times thereafter, the treatment apparatus may be used more times, and therefore suffer more “wear-and-tear” than if the user fully complies with the exercise regimen the first time. In some embodiments, a technical solution may include using trained machine learning models to generate treatment plans based on the measure of benefit exercise regimens provide users and the probabilities of the users associated with complying with the exercise regimens, such inclusion thereby leading to more time-efficient, cost-efficient, and maintenance -efficient use of the treatment apparatus.

[0884] In some embodiments, the treatment apparatus may be adaptive and/or personalized because its properties, configurations, and positions may be adapted to the needs of a particular patient. For example, the pedals may be dynamically adjusted on the fly (e.g., via a telemedicine session or based on programmed configurations in response to certain measurements being detected) to increase or decrease a range of motion to comply with a treatment plan designed for the user. In some embodiments, a healthcare provider may adapt, remotely during a telemedicine session, the treatment apparatus to the needs of the patient by causing a control instruction to be transmitted from a server to treatment apparatus. Such adaptive nature may improve the results of recovery for a patient, furthering the goals of personalized medicine, and enabling personalization of the treatment plan on a per-individual basis.

[0885] FIG. 36 shows a block diagram of a computer-implemented system 4010, hereinafter called “the system” for managing a treatment plan. Managing the treatment plan may include using an artificial intelligence engine to recommend treatment plans and/or provide excluded treatment plans that should not be recommended to a patient.

[0886] The system 4010 also includes a server 4030 configured to store and to provide data related to managing the treatment plan. The server 4030 may include one or more computers and may take the form of a distributed and/or virtualized computer or computers. The server 4030 also includes a first communication interface 4032 configured to communicate with the clinician interface 4020 via a first network 4034. In some embodiments, the first network 4034 may include wired and/or wireless network connections such as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC), cellular data network, etc. The server 4030 includes a first processor 4036 and a first machine -readable storage memory 4038, which may be called a “memory” for short, holding first instructions 4040 for performing the various actions of the server 4030 for execution by the first processor 4036. The server 4030 is configured to store data regarding the treatment plan. For example, the memory 4038 includes a system data store 4042 configured to hold system data, such as data pertaining to treatment plans for treating one or more patients.

[0887] The system data store 4042 may be configured to store optimal treatment plans generated based on one or more probabilities of users associated with complying with the exercise regimens, and the measure of benefit with which one or more exercise regimens provide the user. The system data store 4042 may hold data pertaining to one or more exercises (e.g., a type of exercise, which body part the exercise affects, a duration of the exercise, which treatment apparatus to use to perform the exercise, repetitions of the exercise to perform, etc.). When any of the techniques described herein are being performed, or prior to or thereafter such performance, any of the data stored in the system data store 4042 may be accessed by an artificial intelligence engine 4011.

[0888] The server 4030 may also be configured to store data regarding performance by a patient in following a treatment plan. For example, the memory 4038 includes a patient data store 4044 configured to hold patient data, such as data pertaining to the one or more patients, including data representing each patient’s performance within the treatment plan. The patient data store 4044 may hold treatment data pertaining to users over time, such that historical treatment data is accumulated in the patient data store 4044. The patient data store 4044 may hold data pertaining to measures of benefit one or more exercises provide to users, probabilities of the users complying with the exercise regimens, and the like. The exercise regimens may include any suitable number of exercises (e.g., shoulder raises, squats, cardiovascular exercises, sit-ups, curls, etc.) to be performed by the user. When any of the techniques described herein are being performed, or prior to or thereafter such performance, any of the data stored in the patient data store 4044 may be accessed by an artificial intelligence engine 4011.

[0889] In addition, the determination or identification of: the characteristics (e.g., personal, performance, measurement, etc.) of the users, the treatment plans followed by the users, the measure of benefits which exercise regimens provide to the users, the probabilities of the users associated with complying with exercise regimens, the level of compliance with the treatment plans (e.g., the user completed 4 out of 5 exercises in the treatment plans, the user completed 80% of an exercise in the treatment plan, etc.), and the results of the treatment plans may use correlations and other statistical or probabilistic measures to enable the partitioning of or to partition the treatment plans into different patient cohort-equivalent databases in the patient data store 4044. For example, the data for a first cohort of first patients having a first determined measure of benefit provided by exercise regimens, a first determined probability of the user associated with complying with exercise regimens, a first similar injury, a first similar medical condition, a first similar medical procedure performed, a first treatment plan followed by the first patient, and/or a first result of the treatment plan, may be stored in a first patient database. The data for a second cohort of second patients having a second determined measure of benefit provided by exercise regimens, a second determined probability of the user associated with complying with exercise regimens, a second similar injury, a second similar medical condition, a second similar medical procedure performed, a second treatment plan followed by the second patient, and/or a second result of the treatment plan may be stored in a second patient database. Any single characteristic, any combination of characteristics, or any measures calculation therefrom or thereupon may be used to separate the patients into cohorts. In some embodiments, the different cohorts of patients may be stored in different partitions or volumes of the same database. There is no specific limit to the number of different cohorts of patients allowed, other than as limited by mathematical combinatoric and/or partition theory.

[0890] This measure of exercise benefit data, user compliance probability data, characteristic data, treatment plan data, and results data may be obtained from numerous treatment apparatuses and/or computing devices over time and stored in the database 44. The measure of exercise benefit data, user compliance probability data, characteristic data, treatment plan data, and results data may be correlated in the patient-cohort databases in the patient data store 4044. The characteristics of the users may include personal information, performance information, and/or measurement information.

[0891] In addition to the historical treatment data, measure of exercise benefit data, and/or user compliance probability data about other users stored in the patient cohort-equivalent databases, real-time or near-real-time information based on the current patient’s treatment data, measure of exercise benefit data, and/or user compliance probability data about a current patient being treated may be stored in an appropriate patient cohort- equivalent database. The treatment data, measure of exercise benefit data, and/or user compliance probability data of the patient may be determined to match or be similar to the treatment data, measure of exercise benefit data, and/or user compliance probability data of another person in a particular cohort (e.g., a first cohort “A”, a second cohort “B” or a third cohort “C”, etc.) and the patient may be assigned to the selected or associated cohort. [0892] In some embodiments, the server 4030 may execute the artificial intelligence (AI) engine 4011 that uses one or more machine learning models 4013 to perform at least one of the embodiments disclosed herein. The server 4030 may include a training engine 409 capable of generating the one or more machine learning models 4013. The machine learning models 4013 may be trained to assign users to certain cohorts based on their treatment data, generate treatment plans using real-time and historical data correlations involving patient cohort- equivalents, and control a treatment apparatus 4070, among other things. The machine learning models 4013 may be trained to generate, based on one or more probabilities of the user complying with one or more exercise regimens and/or a respective measure of benefit one or more exercise regimens provide the user, a treatment plan at least a subset of the one or more exercises for the user to perform. The one or more machine learning models 4013 may be generated by the training engine 4009 and may be implemented in computer instructions executable by one or more processing devices of the training engine 409 and/or the servers 4030. To generate the one or more machine learning models 4013, the training engine 4009 may train the one or more machine learning models 4013. The one or more machine learning models 4013 may be used by the artificial intelligence engine 4011.

[0893] The training engine 4009 may be a rackmount server, a router computer, a personal computer, a portable digital assistant, a smartphone, a laptop computer, a tablet computer, a netbook, a desktop computer, an Internet of Things (IoT) device, any other desired computing device, or any combination of the above. The training engine 9 may be cloud-based or a real-time software platform, and it may include privacy software or protocols, and/or security software or protocols.

[0894] To train the one or more machine learning models 4013, the training engine 4009 may use a training data set of a corpus of information (e.g., treatment data, measures of benefits of exercises provide to users, probabilities of users complying with the one or more exercise regimens, etc.) pertaining to users who performed treatment plans using the treatment apparatus 4070, the details (e.g., treatment protocol including exercises, amount of time to perform the exercises, instructions for the patient to follow, how often to perform the exercises, a schedule of exercises, parameters/configurations/settings of the treatment apparatus 4070 throughout each step of the treatment plan, etc.) of the treatment plans performed by the users using the treatment apparatus 4070, and/or the results of the treatment plans performed by the users, etc.

[0895] The one or more machine learning models 4013 may be trained to match patterns of treatment data of a user with treatment data of other users assigned to a particular cohort. The term “match” may refer to an exact match, a correlative match, a substantial match, a probabilistic match, etc. The one or more machine learning models 4013 may be trained to receive the treatment data of a patient as input, map the treatment data to the treatment data of users assigned to a cohort, and determine a respective measure of benefit one or more exercise regimens provide to the user based on the measures of benefit the exercises provided to the users assigned to the cohort. The one or more machine learning models 4013 may be trained to receive the treatment data of a patient as input, map the treatment data to treatment data of users assigned to a cohort, and determine one or more probabilities of the user associated with complying with the one or more exercise regimens based on the probabilities of the users in the cohort associated with complying with the one or more exercise regimens. The one or more machine learning models 4013 may also be trained to receive various input (e.g., the respective measure of benefit which one or more exercise regimens provide the user; the one or more probabilities of the user complying with the one or more exercise regimens; an amount, quality or other measure of sleep associated with the user; information pertaining to a diet of the user, information pertaining to an eating schedule of the user; information pertaining to an age of the user, information pertaining to a sex of the user; information pertaining to a gender of the user; an indication of a mental state of the user; information pertaining to a genetic condition of the user; information pertaining to a disease state of the user; an indication of an energy level of the user; or some combination thereof), and to output a generated treatment plan for the patient.

[0896] The one or more machine learning models 4013 may be trained to match patterns of a first set of parameters (e.g., treatment data, measures of benefits of exercises provided to users, probabilities of user compliance associated with the exercises, etc.) with a second set of parameters associated with an optimal treatment plan. The one or more machine learning models 4013 may be trained to receive the first set of parameters as input, map the characteristics to the second set of parameters associated with the optimal treatment plan, and select the optimal treatment plan. The one or more machine learning models 4013 may also be trained to control, based on the treatment plan, the treatment apparatus 4070.

[0897] Using training data that includes training inputs and corresponding target outputs, the one or more machine learning models 4013 may refer to model artifacts created by the training engine 4009. The training engine 4009 may find patterns in the training data wherein such patterns map the training input to the target output, and generate the machine learning models 4013 that capture these patterns. In some embodiments, the artificial intelligence engine 4011, the database 4033, and/or the training engine 4009 may reside on another component (e.g., assistant interface 4094, clinician interface 4020, etc.) depicted in FIG. 36.

[0898] The one or more machine learning models 4013 may comprise, e.g., a single level of linear or non linear operations (e.g., a support vector machine [SVM]) or the machine learning models 4013 may be a deep network, i.e., a machine learning model comprising multiple levels of non-linear operations. Examples of deep networks are neural networks including generative adversarial networks, convolutional neural networks, recurrent neural networks with one or more hidden layers, and fully connected neural networks (e.g., each neuron may transmit its output signal to the input of the remaining neurons, as well as to itself). For example, the machine learning model may include numerous layers and/or hidden layers that perform calculations (e.g., dot products) using various neurons.

[0899] Further, in some embodiments, based on subsequent data (e.g., treatment data, measures of exercise benefit data, probabilities of user compliance data, treatment plan result data, etc.) received, the machine learning models 4013 may be continuously or continually updated. For example, the machine learning models 4013 may include one or more hidden layers, weights, nodes, parameters, and the like. As the subsequent data is received, the machine learning models 4013 may be updated such that the one or more hidden layers, weights, nodes, parameters, and the like are updated to match or be computable from patterns found in the subsequent data. Accordingly, the machine learning models 4013 may be re-trained on the fly as subsequent data is received, and therefore, the machine learning models 4013 may continue to learn.

[0900] The system 4010 also includes a patient interface 4050 configured to communicate information to a patient and to receive feedback from the patient. Specifically, the patient interface includes an input device 4052 and an output device 4054, which may be collectively called a patient user interface 4052, 4054. The input device 4052 may include one or more devices, such as a keyboard, a mouse, a touch screen input, a gesture sensor, and/or a microphone and processor configured for voice recognition. The output device 4054 may take one or more different forms including, for example, a computer monitor or display screen on a tablet, smartphone, or a smart watch. The output device 4054 may include other hardware and/or software components such as a projector, virtual reality capability, augmented reality capability, etc. The output device 4054 may incorporate various different visual, audio, or other presentation technologies. For example, the output device 4054 may include a non-visual display, such as an audio signal, which may include spoken language and/or other sounds such as tones, chimes, and/or melodies, which may signal different conditions and/or directions and/or other sensorial or perceptive (e.g., tactile, gustatory, haptic, pressure-sensing-based or electromagnetic (e.g., neurostimulation) communication devices. The output device 54 may comprise one or more different display screens presenting various data and/or interfaces or controls for use by the patient. The output device 54 may include graphics, which may be presented by a web-based interface and/or by a computer program or application (App.). In some embodiments, the patient interface 50 may include functionality provided by or similar to existing voice-based assistants such as Siri by Apple, Alexa by Amazon, Google Assistant, or Bixby by Samsung.

[0901] In some embodiments, the output device 4054 may present a user interface that may present a recommended treatment plan, excluded treatment plan, or the like to the patient. The user interface may include one or more graphical elements that enable the user to select which treatment plan to perform. Responsive to receiving a selection of a graphical element (e.g., “Start” button) associated with a treatment plan via the input device 4054, the patient interface 4050 may communicate a control signal to the controller 4072 of the treatment apparatus, wherein the control signal causes the treatment apparatus 4070 to begin execution of the selected treatment plan. As described below, the control signal may control, based on the selected treatment plan, the treatment apparatus 4070 by causing actuation of the actuator 4078 (e.g., cause a motor to drive rotation of pedals of the treatment apparatus at a certain speed), causing measurements to be obtained via the sensor 4076, or the like. The patient interface 4050 may communicate, via a local communication interface 4068, the control signal to the treatment apparatus 4070.

[0902] As shown in FIG. 36, the patient interface 4050 includes a second communication interface 4056, which may also be called a remote communication interface configured to communicate with the server 4030 and/or the clinician interface 4020 via a second network 4058. In some embodiments, the second network 4058 may include a local area network (LAN), such as an Ethernet network. In some embodiments, the second network 4058 may include the Internet, and communications between the patient interface 4050 and the server 4030 and/or the clinician interface 4020 may be secured via encryption, such as, for example, by using a virtual private network (VPN). In some embodiments, the second network 4058 may include wired and/or wireless network connections such as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC), cellular data network, etc. In some embodiments, the second network 58 may be the same as and/or operationally coupled to the first network 4034.

[0903] The patient interface 4050 includes a second processor 4060 and a second machine -readable storage memory 4062 holding second instructions 4064 for execution by the second processor 4060 for performing various actions of patient interface 4050. The second machine-readable storage memory 4062 also includes a local data store 4066 configured to hold data, such as data pertaining to a treatment plan and/or patient data, such as data representing a patient’s performance within a treatment plan. The patient interface 4050 also includes a local communication interface 4068 configured to communicate with various devices for use by the patient in the vicinity of the patient interface 4050. The local communication interface 4068 may include wired and/or wireless communications. In some embodiments, the local communication interface 4068 may include a local wireless network such as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC), cellular data network, etc.

[0904] The system 4010 also includes a treatment apparatus 4070 configured to be manipulated by the patient and/or to manipulate a body part of the patient for performing activities according to the treatment plan. In some embodiments, the treatment apparatus 4070 may take the form of an exercise and rehabilitation apparatus configured to perform and/or to aid in the performance of a rehabilitation regimen, which may be an orthopedic rehabilitation regimen, and the treatment includes rehabilitation of a body part of the patient, such as a joint or a bone or a muscle group. The treatment apparatus 4070 may be any suitable medical, rehabilitative, therapeutic, etc. apparatus configured to be controlled distally via another computing device to treat a patient and/or exercise the patient. The treatment apparatus 4070 may be an electromechanical machine including one or more weights, an electromechanical bicycle, an electromechanical spin-wheel, a smart-mirror, a treadmill, or the like. The body part may include, for example, a spine, a hand, a foot, a knee, or a shoulder. The body part may include a part of a joint, a bone, or a muscle group, such as one or more vertebrae, a tendon, or a ligament. As shown in FIG. 36, the treatment apparatus 4070 includes a controller 4072, which may include one or more processors, computer memory, and/or other components. The treatment apparatus 4070 also includes a fourth communication interface 4074 configured to communicate with the patient interface 4050 via the local communication interface 4068. The treatment apparatus 4070 also includes one or more internal sensors 4076 and an actuator 4078, such as a motor. The actuator 4078 may be used, for example, for moving the patient’s body part and/or for resisting forces by the patient.

[0905] The internal sensors 4076 may measure one or more operating characteristics of the treatment apparatus 4070 such as, for example, a force, a position, a speed, a velocity, and/or an acceleration. In some embodiments, the internal sensors 4076 may include a position sensor configured to measure at least one of a linear motion or an angular motion of a body part of the patient. For example, an internal sensor 4076 in the form of a position sensor may measure a distance that the patient is able to move a part of the treatment apparatus 4070, where such distance may correspond to a range of motion that the patient’s body part is able to achieve. In some embodiments, the internal sensors 4076 may include a force sensor configured to measure a force applied by the patient. For example, an internal sensor 4076 in the form of a force sensor may measure a force or weight the patient is able to apply, using a particular body part, to the treatment apparatus 4070.

[0906] The system 4010 shown in FIG. 36 also includes an ambulation sensor 4082, which communicates with the server 4030 via the local communication interface 4068 of the patient interface 4050. The ambulation sensor 4082 may track and store a number of steps taken by the patient. In some embodiments, the ambulation sensor 4082 may take the form of a wristband, wristwatch, or smart watch. In some embodiments, the ambulation sensor 4082 may be integrated within a phone, such as a smartphone.

[0907] The system 4010 shown in FIG. 36 also includes a goniometer 4084, which communicates with the server 4030 via the local communication interface 4068 of the patient interface 4050. The goniometer 4084 measures an angle of the patient’s body part. For example, the goniometer 4084 may measure the angle of flex of a patient’s knee or elbow or shoulder.

[0908] The system 4010 shown in FIG. 36 also includes a pressure sensor 4086, which communicates with the server 4030 via the local communication interface 68 of the patient interface 4050. The pressure sensor 4086 measures an amount of pressure or weight applied by a body part of the patient. For example, pressure sensor 4086 may measure an amount of force applied by a patient’s foot when pedaling a stationary bike.

[0909] The system 4010 shown in FIG. 36 also includes a supervisory interface 4090 which may be similar or identical to the clinician interface 4020. In some embodiments, the supervisory interface 4090 may have enhanced functionality beyond what is provided on the clinician interface 4020. The supervisory interface 4090 may be configured for use by a person having responsibility for the treatment plan, such as an orthopedic surgeon.

[0910] The system 4010 shown in FIG. 36 also includes a reporting interface 4092 which may be similar or identical to the clinician interface 4020. In some embodiments, the reporting interface 4092 may have less functionality from what is provided on the clinician interface 4020. For example, the reporting interface 4092 may not have the ability to modify a treatment plan. Such a reporting interface 4092 may be used, for example, by a biller to determine the use of the system 4010 for billing purposes. In another example, the reporting interface 4092 may not have the ability to display patient identifiable information, presenting only pseudonymized data and/or anonymized data for certain data fields concerning a data subject and/or for certain data fields concerning a quasi-identifier of the data subject. Such a reporting interface 4092 may be used, for example, by a researcher to determine various effects of a treatment plan on different patients.

[0911] The system 4010 includes an assistant interface 4094 for an assistant, such as a doctor, a nurse, a physical therapist, or a technician, to remotely communicate with the patient interface 4050 and/or the treatment apparatus 4070. Such remote communications may enable the assistant to provide assistance or guidance to a patient using the system 4010. More specifically, the assistant interface 4094 is configured to communicate a telemedicine signal 4096, 4097, 4098a, 4098b, 4099a, 4099b with the patient interface 4050 via a network connection such as, for example, via the first network 4034 and/or the second network 4058. The telemedicine signal 4096, 4097, 4098a, 4098b, 4099a, 4099b comprises one of an audio signal 4096, an audiovisual signal 4097, an interface control signal 4098a for controlling a function of the patient interface 4050, an interface monitor signal 4098b for monitoring a status of the patient interface 4050, an apparatus control signal 4099a for changing an operating parameter of the treatment apparatus 4070, and/or an apparatus monitor signal 4099b for monitoring a status of the treatment apparatus 4070. In some embodiments, each of the control signals 4098a, 4099a may be unidirectional, conveying commands from the assistant interface 4094 to the patient interface 4050. In some embodiments, in response to successfully receiving a control signal 4098a, 4099a and/or to communicate successful and/or unsuccessful implementation of the requested control action, an acknowledgement message may be sent from the patient interface 4050 to the assistant interface 4094. In some embodiments, each of the monitor signals 4098b, 4099b may be unidirectional, status-information commands from the patient interface 4050 to the assistant interface 4094. In some embodiments, an acknowledgement message may be sent from the assistant interface 4094 to the patient interface 4050 in response to successfully receiving one of the monitor signals 4098b, 4099b.

[0912] In some embodiments, the patient interface 4050 may be configured as a pass-through for the apparatus control signals 4099a and the apparatus monitor signals 4099b between the treatment apparatus 70 and one or more other devices, such as the assistant interface 4094 and/or the server 4030. For example, the patient interface 4050 may be configured to transmit an apparatus control signal 4099a to the treatment apparatus 4070 in response to an apparatus control signal 4099a within the telemedicine signal 4096, 4097, 4098a, 4098b, 4099a, 4099b from the assistant interface 4094. In some embodiments, the assistant interface 4094 transmits the apparatus control signal 4099a (e.g., control instruction that causes an operating parameter of the treatment apparatus 4070 to change) to the treatment apparatus 4070 via any suitable network disclosed herein.

[0913] In some embodiments, the assistant interface 4094 may be presented on a shared physical device as the clinician interface 4020. For example, the clinician interface 4020 may include one or more screens that implement the assistant interface 4094. Alternatively or additionally, the clinician interface 4020 may include additional hardware components, such as a video camera, a speaker, and/or a microphone, to implement aspects of the assistant interface 4094.

[0914] In some embodiments, one or more portions of the telemedicine signal 4096, 4097, 4098a, 4098b, 4099a, 4099b may be generated from a prerecorded source (e.g., an audio recording, a video recording, or an animation) for presentation by the output device 4054 of the patient interface 4050. For example, a tutorial video may be streamed from the server 4030 and presented upon the patient interface 4050. Content from the prerecorded source may be requested by the patient via the patient interface 4050. Alternatively, via a control on the assistant interface 4094, the assistant may cause content from the prerecorded source to be played on the patient interface 4050.

[0915] The assistant interface 4094 includes an assistant input device 4022 and an assistant display 4024, which may be collectively called an assistant user interface 4022, 4024. The assistant input device 4022 may include one or more of a telephone, a keyboard, a mouse, a trackpad, or a touch screen, for example. Alternatively or additionally, the assistant input device 4022 may include one or more microphones. In some embodiments, the one or more microphones may take the form of a telephone handset, headset, or wide-area microphone or microphones configured for the assistant to speak to a patient via the patient interface 4050. In some embodiments, assistant input device 4022 may be configured to provide voice-based functionalities, with hardware and/or software configured to interpret spoken instructions by the assistant by using the one or more microphones. The assistant input device 4022 may include functionality provided by or similar to existing voice- based assistants such as Siii by Apple, Alexaby Amazon, Google Assistant, or Bixby by Samsung. The assistant input device 4022 may include other hardware and/or software components. The assistant input device 4022 may include one or more general purpose devices and/or special-purpose devices.

[0916] The assistant display 4024 may take one or more different forms including, for example, a computer monitor or display screen on a tablet, a smartphone, or a smart watch. The assistant display 4024 may include other hardware and/or software components such as projectors, virtual reality capabilities, or augmented reality capabilities, etc. The assistant display 4024 may incorporate various different visual, audio, or other presentation technologies. For example, the assistant display 4024 may include a non-visual display, such as an audio signal, which may include spoken language and/or other sounds such as tones, chimes, melodies, and/or compositions, which may signal different conditions and/or directions. The assistant display 4024 may comprise one or more different display screens presenting various data and/or interfaces or controls for use by the assistant. The assistant display 4024 may include graphics, which may be presented by a web-based interface and/or by a computer program or application (App.).

[0917] In some embodiments, the system 4010 may provide computer translation of language from the assistant interface 4094 to the patient interface 4050 and/or vice-versa. The computer translation of language may include computer translation of spoken language and/or computer translation of text. Additionally or alternatively, the system 4010 may provide voice recognition and/or spoken pronunciation of text. For example, the system 4010 may convert spoken words to printed text and/or the system 4010 may audibly speak language from printed text. The system 4010 may be configured to recognize spoken words by any or all of the patient, the clinician, and/or the healthcare provider. In some embodiments, the system 4010 may be configured to recognize and react to spoken requests or commands by the patient. For example, in response to a verbal command by the patient (which may be given in any one of several different languages), the system 4010 may automatically initiate a telemedicine session.

[0918] In some embodiments, the server 4030 may generate aspects of the assistant display 4024 for presentation by the assistant interface 4094. For example, the server 4030 may include a web server configured to generate the display screens for presentation upon the assistant display 4024. For example, the artificial intelligence engine 4011 may generate recommended treatment plans and/or excluded treatment plans for patients and generate the display screens including those recommended treatment plans and/or external treatment plans for presentation on the assistant display 4024 of the assistant interface 4094. In some embodiments, the assistant display 4024 may be configured to present a virtualized desktop hosted by the server 4030. In some embodiments, the server 4030 may be configured to communicate with the assistant interface 4094 via the first network 4034. In some embodiments, the first network 4034 may include a local area network (LAN), such as an Ethernet network.

[0919] In some embodiments, the first network 4034 may include the Internet, and communications between the server 4030 and the assistant interface 4094 may be secured via privacy enhancing technologies, such as, for example, by using encryption over a virtual private network (VPN). Alternatively or additionally, the server 4030 may be configured to communicate with the assistant interface 4094 via one or more networks independent of the first network 4034 and/or other communication means, such as a direct wired or wireless communication channel. In some embodiments, the patient interface 4050 and the treatment apparatus 4070 may each operate from a patient location geographically separate from a location of the assistant interface 4094. For example, the patient interface 4050 and the treatment apparatus 4070 may be used as part of an in-home rehabilitation system, which may be aided remotely by using the assistant interface 4094 at a centralized location, such as a clinic or a call center.

[0920] In some embodiments, the assistant interface 4094 may be one of several different terminals (e.g., computing devices) that may be grouped together, for example, in one or more call centers or at one or more clinicians’ offices. In some embodiments, a plurality of assistant interfaces 4094 may be distributed geographically. In some embodiments, a person may work as an assistant remotely from any conventional office infrastructure. Such remote work may be performed, for example, where the assistant interface 4094 takes the form of a computer and/or telephone. This remote work functionality may allow for work-from-home arrangements that may include part time and/or flexible work hours for an assistant.

[0921] FIGS. 37-38 show an embodiment of a treatment apparatus 4070. More specifically, FIG. 37 shows a treatment apparatus 4070 in the form of a stationary cycling machine 4100, which may be called a stationary bike, for short. The stationary cycling machine 4100 includes a set of pedals 4102 each attached to a pedal arm 4104 for rotation about an axle 4106. In some embodiments, and as shown in FIG. 37, the pedals 4102 are movable on the pedal arms 4104 in order to adjust a range of motion used by the patient in pedaling. For example, the pedals being located inwardly toward the axle 4106 corresponds to a smaller range of motion than when the pedals are located outwardly away from the axle 4106. A pressure sensor 4086 is attached to or embedded within one of the pedals 4102 for measuring an amount of force applied by the patient on the pedal 4102. The pressure sensor 4086 may communicate wirelessly to the treatment apparatus 4070 and/or to the patient interface 4050. [0922] FIG. 39 shows a person (a patient) using the treatment apparatus of FIG. 37, and showing sensors and various data parameters connected to a patient interface 4050. The example patient interface 4050 is a tablet computer or smartphone, or a phablet, such as an iPad, an iPhone, an Android device, or a Surface tablet, which is held manually by the patient. In some other embodiments, the patient interface 4050 may be embedded within or attached to the treatment apparatus 4070. FIG. 39 shows the patient wearing the ambulation sensor 4082 on his wrist, with a note showing “STEPS TODAY 1355”, indicating that the ambulation sensor 4082 has recorded and transmitted that step count to the patient interface 4050. FIG. 39 also shows the patient wearing the goniometer 84 on his right knee, with a note showing “KNEE ANGLE 72°”, indicating that the goniometer 4084 is measuring and transmitting that knee angle to the patient interface 4050. FIG. 39 also shows a right side of one of the pedals 4102 with a pressure sensor 4086 showing “FORCE 12.5 lbs.,” indicating that the right pedal pressure sensor 4086 is measuring and transmitting that force measurement to the patient interface 4050. FIG. 39 also shows a left side of one of the pedals 4102 with a pressure sensor 4086 showing “FORCE 27 lbs.”, indicating that the left pedal pressure sensor 4086 is measuring and transmitting that force measurement to the patient interface 4050. FIG. 39 also shows other patient data, such as an indicator of “SESSION TIME 0:04: 13”, indicating that the patient has been using the treatment apparatus 4070 for 4 minutes and 13 seconds. This session time may be determined by the patient interface 4050 based on information received from the treatment apparatus 4070. FIG. 39 also shows an indicator showing “PAIN LEVEL 3”. Such a pain level may be obtained from the patent in response to a solicitation, such as a question, presented upon the patient interface 4050. [0923] FIG. 40 is an example embodiment of an overview display 4120 of the assistant interface 4094. Specifically, the overview display 4120 presents several different controls and interfaces for the assistant to remotely assist a patient with using the patient interface 4050 and/or the treatment apparatus 4070. This remote assistance functionality may also be called telemedicine or telehealth.

[0924] Specifically, the overview display 4120 includes a patient profile display 4130 presenting biographical information regarding a patient using the treatment apparatus 4070. The patient profile display 4130 may take the form of a portion or region of the overview display 4120, as shown in FIG. 40, although the patient profile display 4130 may take other forms, such as a separate screen or a popup window. In some embodiments, the patient profile display 4130 may include a limited subset of the patient’s biographical information. More specifically, the data presented upon the patient profile display 4130 may depend upon the assistant’s need for that information. For example, a healthcare provider that is assisting the patient with a medical issue may be provided with medical history information regarding the patient, whereas a technician troubleshooting an issue with the treatment apparatus 4070 may be provided with a much more limited set of information regarding the patient. The technician, for example, may be given only the patient’s name. The patient profile display 4130 may include pseudonymized data and/or anonymized data or use any privacy enhancing technology to prevent confidential patient data from being communicated in a way that could violate patient confidentiality requirements. Such privacy enhancing technologies may enable compliance with laws, regulations, or other rules of governance such as, but not limited to, the Health Insurance Portability and Accountability Act (HIPAA), or the General Data Protection Regulation (GDPR), wherein the patient may be deemed a “data subject”.

[0925] In some embodiments, the patient profile display 4130 may present information regarding the treatment plan for the patient to follow in using the treatment apparatus 4070. Such treatment plan information may be limited to an assistant who is a healthcare provider, such as a doctor or physical therapist. For example, a healthcare provider assisting the patient with an issue regarding the treatment regimen may be provided with treatment plan information, whereas a technician troubleshooting an issue with the treatment apparatus 4070 may not be provided with any information regarding the patient’s treatment plan.

[0926] In some embodiments, one or more recommended treatment plans and/or excluded treatment plans may be presented in the patient profile display 4130 to the assistant. The one or more recommended treatment plans and/or excluded treatment plans may be generated by the artificial intelligence engine 4011 of the server 4030 and received from the server 4030 in real-time during, inter alia, a telemedicine or telehealth session. An example of presenting the one or more recommended treatment plans and/or excluded treatment plans is described below with reference to FIG. 42.

[0927] The example overview display 4120 shown in FIG. 40 also includes a patient status display 4134 presenting status information regarding a patient using the treatment apparatus. The patient status display 4134 may take the form of a portion or region of the overview display 4120, as shown in FIG. 40, although the patient status display 4134 may take other forms, such as a separate screen or a popup window. The patient status display 4134 includes sensor data 4136 from one ormore of the external sensors 4082, 4084, 4086, and/orfrom one or more internal sensors 4076 of the treatment apparatus 4070. In some embodiments, the patient status display 4134 may include sensor data from one or more sensors of one or more wearable devices worn by the patient while using the treatment device 4070. The one or more wearable devices may include a watch, a bracelet, a necklace, a chest strap, and the like. The one or more wearable devices may be configured to monitor a heartrate, a temperature, a blood pressure, one or more vital signs, and the like of the patient while the patient is using the treatment device 4070. In some embodiments, the patient status display 4134 may present other data 4138 regarding the patient, such as last reported pain level, or progress within a treatment plan.

[0928] User access controls may be used to limit access, including what data is available to be viewed and/or modified, on any or all of the user interfaces 4020, 4050, 4090, 4092, 4094 of the system 4010. In some embodiments, user access controls may be employed to control what information is available to any given person using the system 410. For example, data presented on the assistant interface 494 may be controlled by user access controls, with permissions set depending on the assistant/user’s need for and/or qualifications to view that information.

[0929] The example overview display 4120 shown in FIG. 40 also includes a help data display 4140 presenting information for the assistant to use in assisting the patient. The help data display 4140 may take the form of a portion or region of the overview display 4120, as shown in FIG. 40. The help data display 4140 may take other forms, such as a separate screen or a popup window. The help data display 4140 may include, for example, presenting answers to frequently asked questions regarding use of the patient interface 4050 and/or the treatment apparatus 4070. The help data display 4140 may also include research data or best practices. In some embodiments, the help data display 4140 may present scripts for answers or explanations in response to patient questions. In some embodiments, the help data display 4140 may present flow charts or walk-throughs for the assistant to use in determining a root cause and/or solution to a patient’ s problem. In some embodiments, the assistant interface 4094 may present two or more help data displays 4140, which may be the same or different, for simultaneous presentation of help data for use by the assistant for example, a first help data display may be used to present a troubleshooting flowchart to determine the source of a patient’s problem, and a second help data display may present script information for the assistant to read to the patient, such information to preferably include directions for the patient to perform some action, which may help to narrow down or solve the problem. In some embodiments, based upon inputs to the troubleshooting flowchart in the first help data display, the second help data display may automatically populate with script information.

[0930] The example overview display 4120 showninFIG. 40 also includes apatient interface control 4150 presenting information regarding the patient interface 4050, and/or to modify one or more settings of the patient interface 4050. The patient interface control 4150 may take the form of a portion or region of the overview display 4120, as shown in FIG. 40. The patient interface control 4150 may take other forms, such as a separate screen or a popup window. The patient interface control 4150 may present information communicated to the assistant interface 494 via one or more of the interface monitor signals 498b. As shown in FIG. 40, the patient interface control 4150 includes a display feed 4152 of the display presented by the patient interface 4050. In some embodiments, the display feed 4152 may include a live copy of the display screen currently being presented to the patient by the patient interface 4050. In other words, the display feed 4152 may present an image of what is presented on a display screen of the patient interface 4050. In some embodiments, the display feed 4152 may include abbreviated information regarding the display screen currently being presented by the patient interface 4050, such as a screen name or a screen number. The patient interface control 4150 may include a patient interface setting control 4154 for the assistant to adjust or to control one or more settings or aspects of the patient interface 4050. In some embodiments, the patient interface setting control 4154 may cause the assistant interface 4094 to generate and/or to transmit an interface control signal 4098 for controlling a function or a setting of the patient interface 4050.

[0931] In some embodiments, the patient interface setting control 4154 may include collaborative browsing or co-browsing capability for the assistant to remotely view and/or control the patient interface 4050. For example, the patient interface setting control 4154 may enable the assistant to remotely enter text to one or more text entry fields on the patient interface 4050 and/or to remotely control a cursor on the patient interface 4050 using a mouse or touchscreen of the assistant interface 4094.

[0932] In some embodiments, using the patient interface 4050, the patient interface setting control 4154 may allow the assistant to change a setting that cannot be changed by the patient. For example, the patient interface 4050 may be precluded from accessing a language setting to prevent a patient from inadvertently switching, on the patient interface 4050, the language used for the displays, whereas the patient interface setting control 4154 may enable the assistant to change the language setting of the patient interface 4050. In another example, the patient interface 4050 may not be able to change a font size setting to a smaller size in order to prevent a patient from inadvertently switching the font size used for the displays on the patient interface 4050 such that the display would become illegible to the patient, whereas the patient interface setting control 4154 may provide for the assistant to change the font size setting of the patient interface 4050.

[0933] The example overview display 4120 shown in FIG. 40 also includes an interface communications display 4156 showing the status of communications between the patient interface 4050 and one or more other devices 4070, 4082, 4084, such as the treatment apparatus 4070, the ambulation sensor 4082, and/or the goniometer 4084. The interface communications display 4156 may take the form of a portion or region of the overview display 4120, as shown in FIG. 40. The interface communications display 4156 may take other forms, such as a separate screen or a popup window. The interface communications display 4156 may include controls for the assistant to remotely modify communications with one or more of the other devices 4070, 4082, 4084. For example, the assistant may remotely command the patient interface 4050 to reset communications with one of the other devices 4070, 4082, 4084, or to establish communications with a new one of the other devices 4070, 4082, 4084. This functionality may be used, for example, where the patient has a problem with one of the other devices 4070, 4082, 4084, or where the patient receives a new or a replacement one of the other devices 4070, 4082, 4084.

[0934] The example overview display 4120 shown in FIG. 40 also includes an apparatus control 4160 for the assistant to view and/or to control information regarding the treatment apparatus 4070. The apparatus control 4160 may take the form of a portion or region of the overview display 4120, as shown in FIG. 40. The apparatus control 4160 may take other forms, such as a separate screen or a popup window. The apparatus control 4160 may include an apparatus status display 4162 with information regarding the current status of the apparatus. The apparatus status display 4162 may present information communicated to the assistant interface 4094 via one or more of the apparatus monitor signals 4099b. The apparatus status display 4162 may indicate whether the treatment apparatus 4070 is currently communicating with the patient interface 4050. The apparatus status display 4162 may present other current and/or historical information regarding the status of the treatment apparatus 4070.

[0935] The apparatus control 4160 may include an apparatus setting control 4164 for the assistant to adjust or control one or more aspects of the treatment apparatus 4070. The apparatus setting control 4164 may cause the assistant interface 4094 to generate and/or to transmit an apparatus control signal 4099a for changing an operating parameter of the treatment apparatus 4070, (e.g., a pedal radius setting, a resistance setting, a target RPM, other suitable characteristics of the treatment device 4070, or a combination thereof).

[0936] The apparatus setting control 4164 may include a mode button 4166 and a position control 4168, which may be used in conjunction for the assistant to place an actuator 4078 of the treatment apparatus 4070 in a manual mode, after which a setting, such as a position or a speed of the actuator 4078, can be changed using the position control 4168. The mode button 4166 may provide for a setting, such as a position, to be toggled between automatic and manual modes. In some embodiments, one or more settings may be adjustable at any time, and without having an associated auto/manual mode. In some embodiments, the assistant may change an operating parameter of the treatment apparatus 4070, such as a pedal radius setting, while the patient is actively using the treatment apparatus 4070. Such “on the fly” adjustment may or may not be available to the patient using the patient interface 4050. In some embodiments, the apparatus setting control 4164 may allow the assistant to change a setting that cannot be changed by the patient using the patient interface 4050. For example, the patient interface 4050 may be precluded from changing a preconfigured setting, such as a height or a tilt setting of the treatment apparatus 4070, whereas the apparatus setting control 4164 may provide forthe assistant to change the height or tilt setting of the treatment apparatus 4070.

[0937] The example overview display 4120 shown in FIG. 40 also includes a patient communications control 4170 for controlling an audio or an audiovisual communications session with the patient interface 4050. The communications session with the patient interface 4050 may comprise a live feed from the assistant interface 4094 for presentation by the output device of the patient interface 4050. The live feed may take the form of an audio feed and/or a video feed. In some embodiments, the patient interface 4050 may be configured to provide two-way audio or audiovisual communications with a person using the assistant interface 4094. Specifically, the communications session with the patient interface 4050 may include bidirectional (two-way) video or audiovisual feeds, with each of the patient interface 4050 and the assistant interface 4094 presenting video of the other one. In some embodiments, the patient interface 4050 may present video from the assistant interface 4094, while the assistant interface 4094 presents only audio or the assistant interface 4094 presents no live audio or visual signal from the patient interface 4050. In some embodiments, the assistant interface 94 may present video from the patient interface 4050, while the patient interface 4050 presents only audio or the patient interface 50 presents no live audio or visual signal from the assistant interface 4094.

[0938] In some embodiments, the audio or an audiovisual communications session with the patient interface 4050 may take place, at least in part, while the patient is performing the rehabilitation regimen upon the body part. The patient communications control 4170 may take the form of a portion or region of the overview display 4120, as shown in FIG. 40. The patient communications control 4170 may take other forms, such as a separate screen or a popup window. The audio and/or audiovisual communications may be processed and/or directed by the assistant interface 4094 and/or by another device or devices, such as a telephone system, or a videoconferencing system used by the assistant while the assistant uses the assistant interface 4094. Alternatively or additionally, the audio and/or audiovisual communications may include communications with a third party. For example, the system 4010 may enable the assistant to initiate a 3-way conversation regarding use of a particular piece of hardware or software, with the patient and a subject matter expert, such as a medical professional or a specialist. The example patient communications control 4170 shown in FIG. 40 includes call controls 4172 for the assistant to use in managing various aspects of the audio or audiovisual communications with the patient. The call controls 4172 include a disconnect button 4174 for the assistant to end the audio or audiovisual communications session. The call controls 4172 also include a mute button 4176 to temporarily silence an audio or audiovisual signal from the assistant interface 4094. In some embodiments, the call controls 4172 may include other features, such as a hold button (not shown). The call controls 4172 also include one or more record/playback controls 4178, such as record, play, and pause buttons to control, with the patient interface 4050, recording and/or playback of audio and/or video from the teleconference session (e.g., which may be referred to herein as the virtual conference room) . The call controls 4172 also include a video feed display 4180 for presenting still and/or video images from the patient interface 4050, and a self -video display 4182 showing the current image of the assistant using the assistant interface. The self-video display 4182 may be presented as a picture-in-picture format, within a section of the video feed display 4180, as shown in FIG. 40. Alternatively or additionally, the self-video display 4182 may be presented separately and/or independently from the video feed display 4180.

[0939] The example overview display 4120 shown in FIG. 40 also includes a third party communications control 4190 for use in conducting audio and/or audiovisual communications with a third party. The third party communications control 4190 may take the form of a portion or region of the overview display 4120, as shown in FIG. 40. The third party communications control 4190 may take other forms, such as a display on a separate screen or a popup window. The third party communications control 4190 may include one or more controls, such as a contact list and/or buttons or controls to contact a third party regarding use of a particular piece of hardware or software, e.g., a subject matter expert, such as a medical professional or a specialist. The third party communications control 4190 may include conference calling capability for the third party to simultaneously communicate with both the assistant via the assistant interface 4094, and with the patient via the patient interface 4050. For example, the system 4010 may provide for the assistant to initiate a 3-way conversation with the patient and the third party.

[0940] FIG. 41 shows an example block diagram of training a machine learning model 4013 to output, based on data 4600 pertaining to the patient, a treatment plan 4602 for the patient according to the present disclosure. Data pertaining to other patients may be received by the server 4030. The other patients may have used various treatment apparatuses to perform treatment plans. The data may include characteristics of the other patients, the details of the treatment plans performed by the other patients, and/or the results of performing the treatment plans (e.g., a percent of recovery of a portion of the patients’ bodies, an amount of recovery of a portion of the patients’ bodies, an amount of increase or decrease in muscle strength of a portion of patients’ bodies, an amount of increase or decrease in range of motion of a portion of patients’ bodies, etc.).

[0941] As depicted, the data has been assigned to different cohorts. Cohort A includes data for patients having similar first characteristics, first treatment plans, and first results. Cohort B includes data for patients having similar second characteristics, second treatment plans, and second results. For example, cohort A may include first characteristics of patients in their twenties without any medical conditions who underwent surgery for a broken limb; their treatment plans may include a certain treatment protocol (e.g., use the treatment apparatus 4070 for 30 minutes 5 times a week for 3 weeks, wherein values for the properties, configurations, and/or settings of the treatment apparatus 4070 are set to X (where X is a numerical value) for the first two weeks and to Y (where Y is a numerical value) for the last week).

[0942] Cohort A and cohort B may be included in a training dataset used to train the machine learning model 13. The machine learning model 4013 may be trained to match a pattern between characteristics for each cohort and output the treatment plan that provides the result. Accordingly, when the data 4600 for a new patient is input into the trained machine learning model 4013, the trained machine learning model 4013 may match the characteristics included in the data 4600 with characteristics in either cohort A or cohort B and output the appropriate treatment plan 4602. In some embodiments, the machine learning model 4013 may be trained to output one or more excluded treatment plans that should not be performed by the new patient.

[0943] FIG. 42 shows an embodiment of an overview display 4120 of the assistant interface 4094 presenting recommended treatment plans and excluded treatment plans in real-time during a telemedicine session according to the present disclosure. As depicted, the overview display 4120 only includes sections for the patient profile 4130 and the video feed display 4180, including the self-video display 4182. Any suitable configuration of controls and interfaces of the overview display 4120 described with reference to FIG. 40 may be presented in addition to or instead of the patient profile 4130, the video feed display 4180, and the self-video display 4182.

[0944] The healthcare provider using the assistant interface 4094 (e.g., computing device) during the telemedicine session may be presented in the self-video 4182 in a portion of the overview display 4120 (e.g., user interface presented on a display screen 4024 of the assistant interface 4094) that also presents a video from the patient in the video feed display 4180. Further, the video feed display 4180 may also include a graphical user interface (GUI) object 4700 (e.g., a button) that enables the healthcare provider to share on the patient interface 4050, in real-time or near real-time during the telemedicine session, the recommended treatment plans and/or the excluded treatment plans with the patient. The healthcare provider may select the GUI object 4700 to share the recommended treatment plans and/or the excluded treatment plans. As depicted, another portion of the overview display 4120 includes the patient profile display 4130.

[0945] In FIG 42, the patient profile display 4130 is presenting two example recommended treatment plans 4708 and one example excluded treatment plan 4710. As described herein, the treatment plans may be recommended based on the one or more probabilities and the respective measure of benefit the one or more exercises provide the user. The trained machine learning models 4013 may (i) use treatment data pertaining to a user to determine a respective measure of benefit which one or more exercise regimens provide the user, (ii) determine one or more probabilities of the user associated with complying with the one or more exercise regimens, and (iii) generate, using the one or more probabilities and the respective measure of benefit the one or more exercises provide to the user, the treatment plan. In some embodiments, the one or more trained machine learning models 13 may generate treatment plans including exercises associated with a certain threshold (e.g., any suitable percentage metric, value, percentage, number, indicator, probability, etc., which may be configurable) associated with the user complying with the one or more exercise regimens to enable achieving a higher user compliance with the treatment plan. In some embodiments, the one or more trained machine learning models 4013 may generate treatment plans including exercises associated with a certain threshold (e.g., any suitable percentage metric, value, percentage, number, indicator, probability, etc., which may be configurable) associated with one or more measures of benefit the exercises provide to the user to enable achieving the benefits (e.g., strength, flexibility, range of motion, etc.) at a faster rate, at a greater proportion, etc. In some embodiments, when both the measures of benefit and the probability of compliance are considered by the trained machine learning models 4013, each of the measures of benefit and the probability of compliance may be associated with a different weight, such different weight causing one to be more influential than the other. Such techniques may enable configuring which parameter (e.g., probability of compliance or measures of benefit) is more desirable to consider more heavily during generation of the treatment plan.

[0946] For example, as depicted, the patient profile display 4130 presents “The following treatment plans are recommended for the patient based on one or more probabilities of the user complying with one or more exercise regimens and the respective measure of benefit the one or more exercises provide the user.” Then, the patient profile display 4130 presents a first recommended treatment plan.

[0947] As depicted, treatment plan “1” indicates “Patient X should use treatment apparatus for 30 minutes a day for 4 days to achieve an increased range of motion of Y%. The exercises include a first exercise of pedaling the treatment apparatus for 30 minutes at a range of motion of Z% at 5 miles per hour, a second exercise of pedaling the treatment apparatus for 30 minutes at a range of motion of Y% at 10 miles per hour, etc. The first and second exercise satisfy a threshold compliance probability and/or a threshold measure of benefit which the exercise regimens provide to the user.” Accordingly, the treatment plan generated includes a first and second exercise, etc. that increase the range of motion of Y%. Further, in some embodiments, the exercises are indicated as satisfying a threshold compliance probability and/or a threshold measure of benefit which the exercise regimens provide to the user. Each of the exercises may specify any suitable parameter of the exercise and/or treatment apparatus 4070 (e.g., duration of exercise, speed of motor of the treatment apparatus 4070, range of motion setting of pedals, etc.). This specific example and all such examples elsewhere herein are not intended to limit in any way the generated treatment plan from recommending any suitable number and/or type of exercise.

[0948] Recommended treatment plan “2” may specify, based on a desired benefit, an indication of a probability of compliance, or some combination thereof, and different exercises for the user to perform.

[0949] As depicted, the patient profile display 4130 may also present the excluded treatment plans 4710. These types of treatment plans are shown to the assistant using the assistant interface 4094 to alert the assistant not to recommend certain portions of a treatment plan to the patient. For example, the excluded treatment plan could specify the following: “Patient X should not use treatment apparatus for longer than 30 minutes a day due to a heart condition.” Specifically, the excluded treatment plan points out a limitation of a treatment protocol where, due to a heart condition, Patient X should not exercise for more than 30 minutes a day. The excluded treatment plans may be based on treatment data (e.g., characteristics of the user, characteristics of the treatment apparatus 4070, or the like).

[0950] The assistant may select the treatment plan for the patient on the overview display 4120. For example, the assistant may use an input peripheral (e.g., mouse, touchscreen, microphone, keyboard, etc.) to select from the treatment plans 4708 for the patient.

[0951] In any event, the assistant may select the treatment plan for the patient to follow to achieve a desired result. The selected treatment plan may be transmitted to the patient interface 4050 for presentation. The patient may view the selected treatment plan on the patient interface 4050. In some embodiments, the assistant and the patient may discuss during the telemedicine session the details (e.g., treatment protocol using treatment apparatus 4070, diet regimen, medication regimen, etc.) in real-time or in near real-time. In some embodiments, as discussed further with reference to method 41000 of FIG. 45 below, the server 4030 may control, based on the selected treatment plan and during the telemedicine session, the treatment apparatus 4070 as the user uses the treatment apparatus 4070.

[0952] FIG. 43 shows an example embodiment of a method 4300 for optimizing a treatment plan for a user to increase a probability of the user complying with the treatment plan according to the present disclosure. The method 4300 is performed by processing logic that may include hardware (circuitry, dedicated logic, etc.), software (such as is ran on a general-purpose computer system or a dedicated machine), or a combination of both. The method 4300 and/or each of its individual functions, routines, other methods, scripts, subroutines, or operations may be performed by one or more processors of a computing device (e.g., any component of FIGURE 36, such as server 4030 executing the artificial intelligence engine 4011). In certain implementations, the method 4800 may be performed by a single processing thread. Alternatively, the method 4800 may be performed by two or more processing threads, each thread implementing one or more individual functions or routines; or other methods, scripts, subroutines, or operations of the methods.

[0953] For simplicity of explanation, the method 4800 is depicted and described as a series of operations. However, operations in accordance with this disclosure can occur in various orders and/or concurrently, and/or with other operations not presented and described herein. For example, the operations depicted in the method 800 may occur in combination with any other operation of any other method disclosed herein. Furthermore, not all illustrated operations may be required to implement the method 4800 in accordance with the disclosed subject matter. In addition, those skilled in the art will understand and appreciate that the method 4800 could alternatively be represented as a series of interrelated states via a state diagram, a directed graph, a deterministic finite state automaton, a non-deterministic finite state automaton, a Markov diagram, or event diagrams. [0954] At 4802, the processing device may receive treatment data pertaining to a user (e.g., patient, volunteer, trainee, assistant, healthcare provider, instructor, etc.). The treatment data may include one or more characteristics (e.g., vital-sign or other measurements; performance; demographic; psychographic; geographic; diagnostic; measurement- or test-based; medically historic; etiologic; cohort-associative; differentially diagnostic; surgical, physically therapeutic, pharmacologic and other treatment(s) recommended; arterial blood gas and/or oxygenation levels or percentages; psychographics; etc.) of the user. The treatment data may include one or more characteristics of the treatment apparatus 4070. In some embodiments, the one or more characteristics of the treatment apparatus 4070 may include a make (e.g., identity of entity that designed, manufactured, etc. the treatment apparatus 4070) of the treatment apparatus 4070, a model (e.g., model number or other identifier of the model) of the treatment apparatus 4070, a year (e.g., year of manufacturing) of the treatment apparatus 4070, operational parameters (e.g., motor temperature during operation; status of each sensor included in or associated with the treatment apparatus 4070; the patient, or the environment; vibration measurements of the treatment apparatus 4070 in operation; measurements of static and/or dynamic forces exerted on the treatment apparatus 4070; etc.) of the treatment apparatus 4070, settings (e.g., range of motion setting; speed setting; required pedal force setting; etc.) of the treatment apparatus 4070, and the like. In some embodiments, the characteristics of the user and/or the characteristics of the treatment apparatus 4070 may be tracked over time to obtain historical data pertaining to the characteristics of the user and/or the treatment apparatus 4070. The foregoing embodiments shall also be deemed to include the use of any optional internal components or of any external components attachable to, but separate from the treatment apparatus itself. “Attachable” as used herein shall be physically, electronically, mechanically, virtually or in an augmented reality manner.

[0955] In some embodiments, when generating a treatment plan, the characteristics of the user and/or treatment apparatus 4070 may be used. For example, certain exercises may be selected or excluded based on the characteristics of the user and/or treatment apparatus 4070. For example, if the user has a heart condition, high intensity exercises may be excluded in a treatment plan. In another example, a characteristic of the treatment apparatus 4070 may indicate the motor shudders, stalls or otherwise runs improperly at a certain number of revolutions per minute. In order to extend the lifetime of the treatment apparatus 4070, the treatment plan may exclude exercises that include operating the motor at that certain revolutions per minute or at a prescribed manufacturing tolerance within those certain revolutions per minute.

[0956] At 4804, the processing device may determine, via one or more trained machine learning models 4013, a respective measure of benefit with which one or more exercises provide the user. In some embodiments, based on the treatment data, the processing device may execute the one or more trained machine learning models 4013 to determine the respective measures of benefit. For example, the treatment data may include the characteristics of the user (e.g., heartrate, vital-sign, medical condition, injury, surgery, etc.), and the one or more trained machine learning models may receive the treatment data and output the respective measure of benefit with which one or more exercises provide the user. For example, if the user has a heart condition, a high intensity exercise may provide a negative benefit to the user, and thus, the trained machine learning model may output a negative measure of benefit for the high intensity exercise for the user. In another example, an exercise including pedaling at a certain range of motion may have a positive benefit for a user recovering from a certain surgery, and thus, the trained machine learning model may output a positive measure of benefit for the exercise regimen for the user.

[0957] At 4806, the processing device may determine, via the one or more trained machine learning models 4013, one or more probabilities associated with the user complying with the one or more exercise regimens. In some embodiments, the relationship between the one or more probabilities associated with the user complying with the one or more exercise regimens may be one to one, one to many, many to one, or many to many. The one or more probabilities of compliance may refer to a metric (e.g., value, percentage, number, indicator, probability, etc.) associated with a probability the user will comply with an exercise regimen. In some embodiments, the processing device may execute the one or more trained machine learning models 4013 to determine the one or more probabilities based on (i) historical data pertaining to the user, another user, or both, (ii) received feedback from the user, another user, or both, (iii) received feedback from a treatment apparatus used by the user, or (iv) some combination thereof.

[0958] For example, historical data pertaining to the user may indicate a history of the user previously performing one or more of the exercises. In some instances, at a first time, the user may perform a first exercise to completion. At a second time, the user may terminate a second exercise prior to completion. Feedback data from the user and/or the treatment apparatus 4070 may be obtained before, during, and after each exercise performed by the user. The trained machine learning model may use any combination of data (e.g., (i) historical data pertaining to the user, another user, or both, (ii) received feedback from the user, another user, or both, (iii) received feedback from a treatment apparatus used by the user) described above to learn a user compliance profile for each of the one or more exercises. The term “user compliance profile” may refer to a collection of histories of the user complying with the one or more exercise regimens. In some embodiments, the trained machine learning model may use the user compliance profile, among other data (e.g., characteristics of the treatment apparatus 4070), to determine the one or more probabilities of the user complying with the one or more exercise regimens.

[0959] At 4808, the processing device may transmit a treatment plan to a computing device. The computing device may be any suitable interface described herein. For example, the treatment plan may be transmitted to the assistant interface 4094 for presentation to a healthcare provider, and/or to the patient interface 4050 for presentation to the patient. The treatment plan may be generated based on the one or more probabilities and the respective measure of benefit the one or more exercises may provide to the user. In some embodiments, as described further below with reference to the method 41000 of FIG. 45, while the user uses the treatment apparatus 4070, the processing device may control, based on the treatment plan, the treatment apparatus 4070. [0960] In some embodiments, the processing device may generate, using at least a subset of the one or more exercises, the treatment plan for the user to perform, wherein such performance uses the treatment apparatus 4070. The processing device may execute the one or more trained machine learning models 4013 to generate the treatment plan based on the respective measure of the benefit the one or more exercises provide to the user, the one or more probabilities associated with the user complying with each of the one or more exercise regimens, or some combination thereof. For example, the one or more trained machine learning models 4013 may receive the respective measure of the benefit the one or more exercises provide to the user, the one or more probabilities of the user associated with complying with each of the one or more exercise regimens, or some combination thereof as input and output the treatment plan.

[0961] In some embodiments, during generation of the treatment plan, the processing device may more heavily or less heavily weight the probability of the user complying than the respective measure of benefit the one or more exercise regimens provide to the user. During generation of the treatment plan, such a technique may enable one of the factors (e.g., the probability of the user complying or the respective measure of benefit the one or more exercise regimens provide to the user) to become more important than the other factor. For example, if desirable to select exercises that the user is more likely to comply with in a treatment plan, then the one or more probabilities of the user associated with complying with each of the one or more exercise regimens may receive a higher weight than one or more measures of exercise benefit factors. In another example, if desirable to obtain certain benefits provided by exercises, then the measure of benefit an exercise regimen provides to a user may receive a higher weight than the user compliance probability factor. The weight may be any suitable value, number, modifier, percentage, probability, etc.

[0962] In some embodiments, the processing device may generate the treatment plan using a non- parametric model, a parametric model, or a combination of both a non-parametric model and a parametric model. In statistics, a parametric model or finite-dimensional model refers to probability distributions that have a finite number of parameters. Non-parametric models include model structures not specified a priori but instead determined from data. In some embodiments, the processing device may generate the treatment plan using a probability density function, a Bayesian prediction model, a Markovian prediction model, or any other suitable mathematically-based prediction model. A Bayesian prediction model is used in statistical inference where Bayes’ theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayes’ theorem may describe the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, as additional data (e.g., user compliance data for certain exercises, characteristics of users, characteristics of treatment apparatuses, and the like) are obtained, the probabilities of compliance for users for performing exercise regimens may be continuously updated. The trained machine learning models 4013 may use the Bayesian prediction model and, in preferred embodiments, continuously, constantly or frequently be re-trained with additional data obtained by the artificial intelligence engine 4011 to update the probabilities of compliance, and/or the respective measure of benefit one or more exercises may provide to a user.

[0963] In some embodiments, the processing device may generate the treatment plan based on a set of factors. In some embodiments, the set of factors may include an amount, quality or other quality of sleep associated with the user, information pertaining to a diet of the user, information pertaining to an eating schedule of the user, information pertaining to an age of the user, information pertaining to a sex of the user, information pertaining to a gender of the user, an indication of a mental state of the user, information pertaining to a genetic condition of the user, information pertaining to a disease state of the user, an indication of an energy level of the user, or some combination thereof. For example, the set of factors may be included in the training data used to train and/or re-train the one or more machine learning models 4013. For example, the set of factors may be labeled as corresponding to treatment data indicative of certain measures of benefit one or more exercises provide to the user, probabilities of the user complying with the one or more exercise regimens, or both. [0964] FIG. 44 shows an example embodiment of a method 4900 for generating a treatment plan based on a desired benefit, a desired pain level, an indication of a probability associated with complying with the particular exercise regimen, or some combination thereof, according to some embodiments. Method 4900 includes operations performed by processors of a computing device (e.g., any component of FIG. 36, such as server 4030 executing the artificial intelligence engine 4011). In some embodiments, one or more operations of the method 4900 are implemented in computer instructions stored on a memory device and executed by a processing device. The method 4900 may be performed in the same or a similar manner as described above in regard to method 4800. The operations of the method 4900 may be performed in some combination with any of the operations of any of the methods described herein.

[0965] At 4902, the processing device may receive user input pertaining to a desired benefit, a desired pain level, an indication of a probability associated with complying with a particular exercise regimen, or some combination thereof. The user input may be received from the patient interface 4050. That is, in some embodiments, the patient interface 4050 may present a display including various graphical elements that enable the user to enter a desired benefit of performing an exercise, a desired pain level (e.g., on a scale ranging from 1 - 10, 1 being the lowest pain level and 10 being the highest pain level), an indication of a probability associated with complying with the particular exercise regimen, or some combination thereof. For example, the user may indicate he or she would not comply with certain exercises (e.g., one-arm push-ups) included in an exercise regimen due to a lack of ability to perform the exercise and/or a lack of desire to perform the exercise. The patient interface 4050 may transmit the user input to the processing device (e.g., of the server 4030, assistant interface 4094, or any suitable interface described herein).

[0966] At 4904, the processing device may generate, using at least a subset of the one or more exercises, the treatment plan for the user to perform wherein the performance uses the treatment apparatus 4070. The processing device may generate the treatment plan based on the user input including the desired benefit, the desired pain level, the indication of the probability associated with complying with the particular exercise regimen, or some combination thereof. For example, if the user selected a desired benefit of improved range of motion of flexion and extension of their knee, then the one or more trained machine learning models 4013 may identify, based on treatment data pertaining to the user, exercises that provide the desired benefit. Those identified exercises may be further filtered based on the probabilities of user compliance with the exercise regimens. Accordingly, the one or more machine learning models 4013 may be interconnected, such that the output of one or more trained machine learning models that perform function(s) (e.g., determine measures of benefit exercises provide to user) may be provided as input to one or more other trained machine learning models that perform other functions(s) (e.g., determine probabilities of the user complying with the one or more exercise regimens, generate the treatment plan based on the measures of benefit and/or the probabilities of the user complying, etc.).

[0967] FIG. 45 shows an example embodiment of a method 41000 for controlling, based on a treatment plan, a treatment apparatus 4070 while a user uses the treatment apparatus 4070, according to some embodiments. Method 41000 includes operations performed by processors of a computing device (e.g., any component of FIG. 36, such as server 4030 executing the artificial intelligence engine 4011). In some embodiments, one or more operations of the method 41000 are implemented in computer instructions stored on a memory device and executed by a processing device. The method 41000 may be performed in the same or a similar manner as described above in regard to method 4800. The operations of the method 41000 may be performed in some combination with any of the operations of any of the methods described herein.

[0968] At 41002, the processing device may transmit, during a telemedicine or telehealth session, a recommendation pertaining to a treatment plan to a computing device (e.g., patient interface 4050, assistant interface 4094, or any suitable interface described herein). The recommendation may be presented on a display screen of the computing device in real-time (e.g., less than 2 seconds) in a portion of the display screen while another portion of the display screen presents video of a user (e.g., patient, healthcare provider, or any suitable user). The recommendation may also be presented on a display screen of the computing device in near time (e.g., preferably more than or equal to 2 seconds and less than or equal to 10 seconds) or with a suitable time delay necessary for the user of the display screen to be able to observe the display screen.

[0969] At 41004, the processing device may receive, from the computing device, a selection of the treatment plan. The user (e.g., patient, healthcare provider, assistant, etc.) may use any suitable input peripheral (e.g., mouse, keyboard, microphone, touchpad, etc.) to select the recommended treatment plan. The computing device may transmit the selection to the processing device of the server 4030, which is configured to receive the selection. There may any suitable number of treatment plans presented on the display screen. Each of the treatment plans recommended may provide different results and the healthcare provider may consult, during the telemedicine session, with the user, to discuss which result the user desires. In some embodiments, the recommended treatment plans may only be presented on the computing device of the healthcare provider and not on the computing device of the user (patient interface 4050). In some embodiments, the healthcare provider may choose an option presented on the assistant interface 4094. The option may cause the treatment plans to be transmitted to the patient interface 4050 for presentation. In this way, during the telemedicine session, the healthcare provider and the user may view the treatment plans at the same time in real-time or in near real-time, which may provide for an enhanced user experience for the patient and/or healthcare provider using the computing device.

[0970] After the selection of the treatment plan is received at the server 4030, at 41006, while the user uses the treatment apparatus 4070, the processing device may control, based on the selected treatment plan, the treatment apparatus 4070. In some embodiments, controlling the treatment apparatus 4070 may include the server 4030 generating and transmitting control instructions to the treatment apparatus 4070. In some embodiments, controlling the treatment apparatus 4070 may include the server 4030 generating and transmitting control instructions to the patient interface 4050, and the patient interface 4050 may transmit the control instructions to the treatment apparatus 4070. The control instructions may cause an operating parameter (e.g., speed, orientation, required force, range of motion of pedals, etc.) to be dynamically changed according to the treatment plan (e.g., a range of motion may be changed to a certain setting based on the user achieving a certain range of motion for a certain period of time). The operating parameter may be dynamically changed while the patient uses the treatment apparatus 4070 to perform an exercise. In some embodiments, during a telemedicine session between the patient interface 4050 and the assistant interface 4094, the operating parameter may be dynamically changed in real-time or near real-time.

[0971] FIG. 46 shows an example computer system 41100 which can perform any one or more of the methods described herein, in accordance with one or more aspects of the present disclosure. In one example, computer system 41100 may include a computing device and correspond to the assistance interface 4094, reporting interface 4092, supervisory interface 4090, clinician interface 4020, server 4030 (including the AI engine 4011), patient interface 4050, ambulatory sensor 4082, goniometer 4084, treatment apparatus 4070, pressure sensor 4086, or any suitable component of FIG. 36. The computer system 41100 may be capable of executing instructions implementing the one or more machine learning models 4013 of the artificial intelligence engine 4011 of FIG. 36. The computer system may be connected (e.g., networked) to other computer systems in a LAN, an intranet, an extranet, or the Internet, including via the cloud or a peer-to-peer network. The computer system may operate in the capacity of a server in a client-server network environment. The computer system may be a personal computer (PC), a tablet computer, a wearable (e.g., wristband), a set-top box (STB), a personal Digital Assistant (PDA), a mobile phone, a camera, a video camera, an Internet of Things (IoT) device, or any device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that device. Further, while only a single computer system is illustrated, the term “computer” shall also be taken to include any collection of computers that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methods discussed herein.

[0972] The computer system 41100 includes a processing device 41102, a main memory 41104 (e.g., read only memory (ROM), flash memory, solid state drives (SSDs), dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM)), a static memory 41106 (e.g., flash memory, solid state drives (SSDs), static random access memory (SRAM)), and a data storage device 41108, which communicate with each other via a bus 41110.

[0973] Processing device 41102 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device 41102 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing device 1102 may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a system on a chip, a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processing device 41102 is configured to execute instructions for performing any of the operations and steps discussed herein.

[0974] The computer system 41100 may further include a network interface device 41112. The computer system 41100 also may include a video display 41114 (e.g., a liquid crystal display (LCD), a light-emitting diode (LED), an organic light-emitting diode (OLED), a quantum LED, a cathode ray tube (CRT), a shadow mask CRT, an aperture grille CRT, a monochrome CRT), one or more input devices 41116 (e.g., a keyboard and/or a mouse or a gaming-like control), and one or more speakers 41118 (e.g., a speaker). In one illustrative example, the video display 41114 and the input device(s) 41116 may be combined into a single component or device (e.g., an LCD touch screen).

[0975] The data storage device 41116 may include a computer-readable medium 41120 on which the instructions 41122 embodying any one or more of the methods, operations, or functions described herein is stored. The instructions 41122 may also reside, completely or at least partially, within the main memory 41104 and/or within the processing device 41102 during execution thereof by the computer system 41100. As such, the main memory 41104 and the processing device 41102 also constitute computer-readable media. The instructions 41122 may further be transmitted or received over a network via the network interface device 41112. [0976] While the computer-readable storage medium 41120 is shown in the illustrative examples to be a single medium, the term “computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer-readable storage medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure. The term “computer-readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media.

[0977] Clause 1.3. A computer-implemented system, comprising:

[0978] a treatment apparatus configured to be manipulated by a user while performing a treatment plan; [0979] a patient interface comprising an output device configured to present telemedicine information associated with a telemedicine session; and [0980] a processing device configured to:

[0981] receive treatment data pertaining to the user during the telemedicine session, wherein the treatment data comprises one or more characteristics of the user;

[0982] determine, via one or more trained machine learning models, at least one respective measure of benefit one or more exercise regimens provide the user, wherein the determining the respective measure of benefit is based on the treatment data;

[0983] determine, via the one or more trained machine learning models, one or more probabilities of the user complying with the one or more exercise regimens; and

[0984] transmit the treatment plan to a computing device, wherein the treatment plan is generated based on the one or more probabilities and the respective measure of benefit the one or more exercise regimens provide the user.

[0985] Clause 2.3. The computer-implemented system of any clause herein, wherein the measure of benefit may be positive or negative.

[0986] Clause 3.3. The computer-implemented system of any clause herein, wherein the processing device is further configured to control, based on the treatment plan, the treatment apparatus while the user uses the treatment apparatus.

[0987] Clause 4.3. The computer-implemented system of any clause herein, wherein the determining the one or more probabilities is based on:

[0988] (i) historical data pertaining to the user, another user, or both,

[0989] (ii) received feedback from the user, the another user, or both,

[0990] (iii) received feedback from the treatment apparatus used by the user, or [0991] (iv) some combination thereof.

[0992] Clause 5.3. The computer-implemented system of any clause herein, wherein the processing device is further configured to:

[0993] receive user input pertaining to a desired benefit, a desired pain level, an indication of a probability of complying with a particular exercise regimen, or some combination thereof; and [0994] generate, using at least a subset of the one or more exercises, the treatment plan for the user to perform using the treatment apparatus, wherein the generating is further performed based on the desired benefit, the desired pain level, the indication of the probability of complying with the particular exercise regimen, or some combination thereof.

[0995] Clause 6.3. The computer-implemented system of any clause herein, wherein the processing device is further configured to generate, using at least a subset of the one or more exercises, the treatment plan for the user to perform using the treatment apparatus, wherein the generating is performed based on the respective measure of benefit the one or more exercise regimens provide to the user, the one or more probabilities of the user complying with each of the one or more exercise regimens, or some combination thereof.

[0996] Clause 7.3. The computer-implemented system of any clause herein, wherein the treatment plan is generated using a non-parametric model, a parametric model, or a combination of both the non-parametric model and the parametric model.

[0997] Clause 8.3. The computer-implemented system of any clause herein, wherein the treatment plan is generated using a probability density function, a Bayesian prediction model, a Markovian prediction model, or any other mathematically -based prediction model.

[0998] Clause 9.3. The computer-implemented system of any clause herein, wherein the processing device is further configured to:

[0999] generate the treatment plan based on a plurality of factors comprising an amount of sleep associated with the user, information pertaining to a diet of the user, information pertaining to an eating schedule of the user, information pertaining to an age of the user, information pertaining to a sex of the user, information pertaining to a gender of the user, an indication of a mental state of the user, information pertaining to a genetic condition of the user, information pertaining to a disease state of the user, information pertaining to a microbiome from one or more locations on or in the user, an indication of an energy level of the user, or some combination thereof.

[1000] Clause 10.3. The computer-implemented system of any clause herein, wherein the treatment data further comprises one or more characteristics of the treatment apparatus.

[1001] Clause 11.3. A computer-implemented method for optimizing a treatment plan for a user to perform using a treatment apparatus, the computer-implemented method comprising:

[1002] receiving treatment data pertaining to the user, wherein the treatment data comprises one or more characteristics of the user;

[1003] determining, via one or more trained machine learning models, a respective measure of benefit one or more exercise regimens provide the user, wherein the determining the respective measure of benefit is based on the treatment data;

[1004] determining, via the one or more trained machine learning models, one or more probabilities of the user complying with the one or more exercise regimens; and

[1005] transmitting the treatment plan to a computing device, wherein the treatment plan is generated based on the one or more probabilities and the respective measure of benefit the one or more exercise regimens provide the user. [1006] Clause 12.3. The computer-implemented method of any clause herein, wherein the measure of benefit may be positive or negative.

[1007] Clause 13.3. The computer-implemented method of any clause herein, further comprising controlling, based on the treatment plan, the treatment apparatus while the user uses the treatment apparatus. [1008] Clause 14.3. The computer-implemented method of any clause herein, wherein the determining the one or more probabilities is based on:

[1009] (i) historical data pertaining to the user, another user, or both,

[1010] (ii) received feedback from the user, the another user, or both,

[1011] (iii) received feedback from the treatment apparatus used by the user, or [1012] (iv) some combination thereof.

[1013] Clause 15.3. The computer-implemented method of any clause herein, further comprising:

[1014] receiving user input pertaining to a desired benefit, a desired pain level, an indication of a probability of complying with a particular exercise regimen, or some combination thereof; and [1015] generating, using at least a subset of the one or more exercises, the treatment plan for the user to perform using the treatment apparatus, wherein the generating is further performed based on the desired benefit, the desired pain level, the indication of the probability of complying with the particular exercise regimen, or some combination thereof.

[1016] Clause 16.3. The computer-implemented method of any clause herein, further comprising generating, using at least a subset of the one or more exercises, the treatment plan for the user to perform using the treatment apparatus, wherein the generating is performed based on the respective measure of benefit the one or more exercise regimens provide to the user, the one or more probabilities of the user complying with each of the one or more exercise regimens, or some combination thereof.

[1017] Clause 17.3. The computer-implemented method of any clause herein, wherein, during generation of the treatment plan, the probability of the user complying is weighted more heavily or less heavily than the respective measure of benefit the one or more exercise regimens provide the user.

[1018] Clause 18.3. The computer-implemented method of any clause herein, wherein the treatment plan is generated using a non-parametric model, a parametric model, or a combination of both the non-parametric model and the parametric model.

[1019] Clause 19.3. The computer-implemented method of any clause herein, wherein the treatment plan is generated using a probability density function, a Bayesian prediction model, a Markovian prediction model, or any other mathematically -based prediction model.

[1020] Clause 20.3. The computer-implemented method of any clause herein, further comprising:

[1021] generating the treatment plan based on a plurality of factors comprising an amount of sleep associated with the user, information pertaining to a diet of the user, information pertaining to an eating schedule of the user, information pertaining to an age of the user, information pertaining to a sex of the user, information pertaining to a gender of the user, an indication of a mental state of the user, information pertaining to a genetic condition of the user, information pertaining to a disease state of the user, an indication of an energy level of the user, or some combination thereof. [1022] Clause 21. The computer-implemented method of any clause herein, wherein the treatment data further comprises one or more characteristics of the treatment apparatus.

SYSTEMS AND METHODS FOR USING MACHINE LEARNING TO CONTROL AN

ELECTROMECHANICAL DEVICE USED FOR PREHABILITATION, REHABILITATION, AND/OR

EXERCISE

[1023] Improvement is desired in the field of devices used for prehabilitation and exercise. People may injure, sprain, or tear a body part and consult a healthcare professional to diagnose the injury. As used herein, and without limiting the foregoing, a “healthcare professional” may be a human being, a robot, a virtual assistant, a virtual assistant in a virtual and/or augmented reality, or an artificially intelligent entity, including a software program, integrated software and hardware, or hardware alone. If, for example, the healthcare professional is a human being, the healthcare professional may be any person with a credential, license, degree, or the like in the field of medicine, physical therapy, prehabilitation, rehabilitation, exercise, strength training, endurance training, and/or the like. A healthcare professional may refer to a doctor, physician assistant, nurse, chiropractor, dentist, physical therapist, physiotherapist, kinesiologist, acupuncturist, physical train, coach, personal trainer, neurologist, cardiologist, or the like. A healthcare professional may refer to any person with a credential, license, degree, or the like in the field of medicine, physical therapy, rehabilitation, or the like.

[1024] In some situations, a healthcare professional, such as a physician, may prescribe a treatment plan to a patient. A treatment plan, as used herein, may refer to a plan for a patient who is receiving treatment relating to a past, present, or future illness, condition, or ailment; an exercise plan, strength training plan, or endurance- increasing plan for an individual trying to improve his or her fitness; and/or any other plan capable of affecting the health of the patient. A treatment plan may, for example, include a prehabilitation plan for an individual who is to undergo surgery or who may have to undergo surgery at a later time period, a rehabilitation plan for a patient who has undergone surgery or who has a particular illness, condition, or ailment, and/or the like.

[1025] In some instances, the physician may prescribe a treatment plan that includes operating one or more electrical, mechanical, optic, electro-optical and/or electromechanical devices (e.g., pedaling devices for arms or legs) for a period of time to exercise the affected area in an attempt to improve one or more characteristics of the affected body part and to attempt to regain as much normal operability of that affected body part as possible. In other instances, the person with the affected body part may determine to operate a device without consulting a physician. In either scenario, the devices that are operated lack effective monitoring of progress of the affected area and control over the electromechanical device 104 during operation by the user. Conventional devices lack components that enable operating the electromechanical device in various modes that are designed to enhance the rate and effectiveness of prehabilitation, rehabilitation, and/or the like.

[1026] Further, conventional systems lack monitoring devices that aid in determining one or more properties of the user (e.g., range of motion of the affected area, heartrate of the user, etc.) and enable adjusting components based on the determined properties. When the user is supposed to be adhering to a treatment plan, conventional systems may not provide real-time results of sessions to the healthcare professionals. That is, typically the healthcare professionals have to rely on the patient’ s word as to whether the patient adhering to the treatment plan. [1027] Additionally, computer-implemented treatment systems do not provide a mechanism for a healthcare professional and/or patient to closely monitor patient progress in real-time. Consequently, the user may over-exert himself or herself while exercising, may exercise using improper form, exercise using a sub- optimal range of motion, and/or exercise in any other manner that risks adversely affecting a health indicator of the user (e.g., by reinjuring a body part that was previously operated on or injured) and/or increasing the cost of the user’s patient recovery or improvement process without an attendant benefit in improvement to the underlying condition. These risks are especially apparent in both rehabilitation and prehabilitation.

[1028] Furthermore, computer-implemented treatment systems are unable to generate optimal prehabilitation for patients. For example, if a patient undergoing prehabilitation has previously tom an ACL, the patient may have a reduced or limited range of motion (ROM) of one or more body parts affected by the tom ACL. The tom ACL may have also affected strength and/or endurance of the patient. Consequently, an optimal prehabilitation plan should improve the patient’s ROM, strength, and/or endurance (e.g., stamina, etc.) to reduce the likelihood that the patient experiences a recurring injury. Additionally, an optimal prehabilitation plan for a patient may vary based on a degree to which a surgery for the tom ACL was successful, based on the patient’s medical history, based on the patient’s demographic information, based on the patient’s ability to accurately cany out a prehabilitation plan, and/or the like. Computer-implemented treatment systems that use electromechanical devices are unable to generate optimal treatment plans that account for these variances. [1029] Moreover, in the context of patient rehabilitation, a computer-implemented treatment system will have the benefit of generating a rehabilitation plan based in part on data relating to an injury that has already occurred. However, in order to generate an optimal prehabilitation plan for a patient undergoing prehabilitation, a prehabilitation system should be configured to determine characteristics of the optimal prehabilitation plan before any injury occurs (wherein the prehabilitation system is unable to rely on data relating to an injury that has already occurred when generating the optimal prehabilitation plan).

[1030] Another technical problem may relate to information pertaining to the patient’s medical condition being received in disparate formats. For example, a server may receive the information pertaining to a medical condition of the patient from one or more sources (e.g., from an electronic medical record (EMR) system, application programming interface (API), or any suitable system that has information pertaining to a medical condition of the patient). That is, some sources that are used by various healthcare professional entities may be installed on their local computing devices and may use proprietary formats. Accordingly, some embodiments of the present disclosure may use an API to obtain, via interfaces exposed by APIs used by the sources, the formats used by the sources. In some embodiments, when information is received from the sources, the API may map and convert the format used by the sources to a standardized format used by the artificial intelligence engine. Further, the information converted to the standardized format used by the artificial intelligence engine may be stored in a database accessed by the artificial intelligence engine when performing any of the techniques disclosed herein. Using the information converted to a standardized format may enable more accurately determining the procedures to perform for the patient.

[1031] To that end, the standardized information may enable generating prehabilitation plans having a particular format that canbe processed by various applications (e.g., telehealth). For example, applications, such as telehealth applications, may be executing on various computing devices of healthcare professionals and/or patients. The applications (e.g., standalone or web-based) may be provided by a server and may be configured to process data according to a format in which the treatment plans are implemented. Accordingly, the disclosed embodiments may provide a technical solution by (i) receiving, from various sources (e.g., EMR systems), information in non-standardized and/or different formats; (ii) standardizing the information; and (iii) generating, based on the standardized information, prehabilitation plans having standardized formats that are capable of being processed by applications (e.g., telehealth application) executing on computing devices of healthcare professional and/or patients.

[1032] Still further, another technical problem may involve distally treating, via a computing device during a telemedicine or telehealth session, a patient from a location different than a location at which the patient is located. An additional technical problem is controlling or enabling the control of, from the different location, an electromechanical device used by the patient at the location at which the patient is located. Oftentimes, when a patient undergoes rehabilitative surgery (e.g., knee surgery), a physical therapist or other healthcare professional may prescribe an electromechanical device to the patient to use to perform a treatment protocol at their residence or any mobile location or temporary domicile.

[1033] Since the healthcare professional is located in a location different from the patient and the electromechanical device, it may be technically challenging for the healthcare professional to monitor the patient’s actual progress (as opposed to relying on the patient’s word about their progress) in using the electromechanical device, to modify the treatment plan according to the patient’s progress, to adapt the electromechanical device to the personal characteristics of the patient as the patient performs the treatment plan, and the like.

[1034] Accordingly, aspects of the present disclosure generally relate to a prehabilitation system for a prehabilitation and exercise electromechanical device (referred to herein as “electromechanical device”). The electromechanical device may include an electric motor configured to drive one or more radially-adjustable couplings to rotationally move pedals coupled to the radially-adjustable couplings. The electromechanical device may be operated by a user engaging the pedals with their hands or their feet and rotating the pedals to exercise and/or rehabilitate a desired body part. The electromechanical device and the prehabilitation system may be included as part of a larger prehabilitation system. The prehabilitation system may also include monitoring devices (e.g., goniometer, wristband, force sensors in the pedals, etc.) that provide valuable information about the user to the prehabilitation system. As such, the monitoring devices may be in direct or indirect communication with the prehabilitation system.

[1035] The monitoring devices may include a goniometer that is configured to measure range of motion (e.g., angles of extension and/or bend) of a body part to which the goniometer is attached. The measured range of motion may be presented to the user and/or a physician via a user portal and/or a clinical portal. Also the prehabilitation system may use the measured range of motion to determine whether to adjust positions of the pedals on the radially-adjustable couplings and/or to adjust the mode types (e.g., passive, active-assisted, resistive, active) and/or durations to operate the electromechanical device during a treatment plan. The monitoring devices may also include a wristband configured to track the steps of the user over a time period (e.g., day, week, etc.) and/or measure vital signs of the user (e.g., heartrate, blood pressure, oxygen level). The monitoring devices may also include force sensors disposed in the pedals that are configured to measure the force exerted by the user on the pedals. [1036] The prehabilitation system may enable operating the electromechanical device in a variety of modes, such as a passive mode, an active-assisted mode, a resistive mode, and/or an active mode. The prehabilitation system may use the information received from the measuring devices to adjust parameters (e.g., reduce resistance provided by electric motor, increase resistance provided by the electric motor, increase/decrease speed of the electric motor, adjust position of pedals on radially-adjustable couplings, etc.) while operating the electromechanical device in the various modes. The prehabilitation system may receive the information from the monitoring devices, aggregate the information, make determinations using the information, and/or transmit the information to a cloud-based computing system for storage. The cloud-based computing system may maintain the information that is related to each user.

[1037] A clinician and/or a machine learning model may generate a treatment plan, such as a prehabilitation plan for a user, to improve and/or strengthen a part of the user’ s body using at least the electromechanical device. A treatment plan may include a set of pedaling sessions using the electromechanical device, a set of joint extension sessions, a set of flex sessions, a set of walking sessions, a set of heartrates per pedaling session and/or walking session, and the like. Additionally, or alternatively, the treatment plan may include a medical procedure to perform on the patient, a treatment protocol for the patient using the electromechanical device, a diet regimen for the patient, a medical regiment for the patient, a sleep regiment for the patient, and/or the like.

[1038] Each pedaling session may specify that a user is to operate the electromechanical device in a combination of one or more modes, including: passive, active-passive, active, and resistive. The pedaling session may specify that the user is to wear the wristband and the goniometer during the pedaling session. Further, each pedaling session may include a set amount of time that the electromechanical device is to operate in each mode, a target heartrate for the user during each mode in the pedaling session, target forces that the user is to exert on the pedals during each mode in the pedaling session, target ranges of motion the body parts are to attain during the pedaling session, positions of the pedals on the radially-adjustable couplings, and the like.

[1039] Each joint extension session may specify a target angle of extension at the joint, and each set of joint flex sessions may specify a target angle of flex at the joint. Each walking session may specify a target number of steps the user should take over a set period of time (e.g., day, week etc.) and/or a target heartrate to achieve and/or maintain during the walking session.

[1040] The treatment plans may be stored in the cloud-based computing system and downloaded to the computing device of the user when the user is ready to begin the treatment plan. In some embodiments, the computing device that executes a clinical portal may transmit the treatment plan to the computing device that executes a user portal and the user may initiate the treatment plan when ready.

[1041] In addition, the disclosed prehabilitation system may enable a physician to monitor the progress of the user in real-time using the clinical portal. The clinical portal may present information pertaining to when the user is engaged in one or more sessions, statistics (e.g., speed, revolutions per minute, position of pedals, force on the pedals, vital signs, number of steps taken by user, range of motion, etc.) of the sessions, and the like. The clinical portal may also enable the physician to view before and after session images of the affected body part of the user to enable the physician to judge how well the treatment plan is working and/or to make adjustments to the treatment plan. The clinical portal may enable the physician to dynamically change a parameter (e.g., position of pedals, amount of resistance provided by electric motor, speed of the electric motor, duration of one of the modes, etc.) of the treatment plan in real-time based on information received from the prehabilitation system.

[1042] Furthermore, the disclosed prehabilitation system may generate a prehabilitation plan by using a machine learning model to process received user data and treatment data. The prehabilitation plan may include an exercise session to be performed on an electromechanical device. The disclosed prehabilitation system may select a device configuration for the electromechanical device, where the device configuration corresponds to the prehabilitation plan. The disclosed prehabilitation system may provide the device configuration to the electromechanical device such that the device configuration may be implemented on the electromechanical device.

[1043] The disclosed techniques provide numerous benefits over conventional systems. For example, the prehabilitation system provides granular control over the components of the electromechanical device to enhance the efficiency and effectiveness of prehabilitation of the user. The prehabilitation system enables operating the electromechanical device in any suitable combination of the modes described herein by controlling the electric motor, for example. To provide a specific example, the prehabilitation system uses information received from the monitoring devices to adjust parameters of components of the electromechanical device in real-time during a pedaling session. By adjusting parameters of components of the electromechanical device in real-time, the prehabilitation system enables the user to exercise efficiently and effectively while reducing chances of injury. For example, if a user is applying too much pressure to a pedal, a resistance of the pedal may be adjusted to reduce the risk of injury during the pedaling session. This adjustment conserves resources (e.g., power resources, processing resources, network resources, and/or the like) of the electromechanical device and related computing or other devices. For example, resources are conserved relative to a prior system that produces inferior performance, results, etc., such as a prior system that is unable to adjust parameters of one or more components of an exercise device while a user is using the exercise device to exercise. Additional benefits of this disclosure include enabling a computing device operated by a physician to monitor the progress of a user participating in a prehabilitation plan in real-time (e.g., during a telemedicine or telehealth session) and/or to control operation of the electromechanical device during a pedaling session.

[1044] Furthermore, some embodiments described herein use machine learning to generate a prehabilitation plan that is optimal for the user. For example, the prehabilitation system may use machine learning to generate a prehabilitation plan that includes an exercise session, where the exercise session may be performed by the user when a device configuration is implemented on the electromechanical device. The device configuration allows the exercise session to be performed using an optimal Range of Motion (ROM), performed at an optimal strength, and/or performed at an optimal endurance. By using machine learning to generate an optimal treatment plan for the user, the prehabilitation system conserves resources of the electromechanical device (and/or related devices) relative to a prior system that does not utilize machine learning to generate an optimal prehabilitation plan for a user. For example, the prehabilitation system conserves resources that a prior system may expend generating, transmitting, and/or displaying data relating to an inferior treatment plan. An inferior treatment plan, as used herein, may refer to a treatment plan not optimized for the user, a treatment plan not generated using machine learning, a treatment plan more likely to injure or re-injure a user (relative to an optimal treatment plan generated for the user), a treatment plan requiring more time for the user to recover from an exercise session performed using an exercise device (relative to the optimal treatment plan), and/or the like. Further, by using machine learning to generate an optimal prehabilitation plan that accounts for a number of factors that influence optimality (e.g., user demographic, medical history, surgery results relating to past injuries and/or conditions, and/or the like), the prehabilitation system reduces a likelihood of injury or re-injury, improves or strengthens one or more body parts of the user at risk for injury or re-injury, improves the overall health of the user, and/or the like.

[1045] FIGs. 47 through 80, discussed below, and the various embodiments used to describe the principles of this disclosure are by way of illustration only and should not be construed in any way to limit the scope of the disclosure.

[1046] FIG. 47 generally illustrates a block diagram of an embodiment of a computer-implemented system 5100 for managing a prehabilitation plan architecture according to principles of the present disclosure. In some embodiments, the computer-implemented system 5100 may include a computing device 5102 communicatively coupled to an electromechanical device 5104, a goniometer 5106, a wristband 5108, and/or pedals 5110 of the electromechanical device 5104. Each of the computing device 5102, the electromechanical device 5104, the goniometer 5106, the wristband 5108, and the pedals 5110 may include one or more processing devices, memory devices, and network interface cards. The network interface cards may enable communication via a wireless protocol for transmitting data over short distances, such as Bluetooth, ZigBee, etc. In some embodiments, the computing device 5102 is communicatively coupled to the electromechanical device 5104, goniometer 5106, the wristband 5108, and/or the pedals 5110 via Bluetooth.

[1047] Additionally, the network interface cards may enable communicating data over long distances, and in one example, the computing device 5102 may communicate with a network 5112. Network 5112 may be a public network (e.g., connected to the Internet via wired (Ethernet) or wireless (WiFi)), a private network (e.g., a local area network (LAN) or wide area network (WAN)), or a combination thereof. The computing device 5102 may be communicatively coupled with a computing device 5114 and a cloud-based computing system 5116.

[1048] The computing device 5102 may be any suitable computing device, such as a laptop, tablet, smartphone, or computer. The computing device 5102 may include a display that is capable of presenting a user interface, such as a user portal 5118. The user portal 5118 may be implemented in computer instructions stored on the one or more memory devices of the computing device 5102 and executable by the one or more processing devices of the computing device 5102. The user portal 5118 may present various screens to a user that enable the user to view a treatment plan, initiate a pedaling session of the treatment plan, control parameters of the electromechanical device 5104, view progress of prehabilitation during the pedaling session, and so forth as described in more detail below. The computing device 5102 may also include instructions stored on the one or more memory devices that, when executed by the one or more processing devices of the computing device 5102, perform operations to control the electromechanical device 5104.

[1049] The computing device 5114 may execute a clinical portal 5126. The clinical portal 5126 may be implemented in computer instructions stored on the one or more memory devices of the computing device 5114 and executable by the one or more processing devices of the computing device 5114. The clinical portal 5126 may present various screens to a physician that enable the physician to create a treatment plan for a patient, view progress of the user throughout the treatment plan, view measured properties (e.g., angles of bend/extension, force exerted on pedals 5110, heartrate, steps taken, images of the affected body part) of the user during sessions of the treatment plan, view properties (e.g., modes completed, revolutions per minute, etc.) of the electromechanical device 5104 during sessions of the treatment plan. The treatment plan specific to a patient may be transmitted via the network 5112 to the cloud-based computing system 5116 for storage and/or to the computing device 5102 so the patient may begin the treatment plan.

[1050] The electromechanical device 5104 may be an adjustable pedaling device for exercising a target area of the body of a user (e.g., a body part, muscle, tendon, and/or the like). For example, a user may perform one or more exercise sessions on the electromechanical device 5104 to strengthen, increase flexibility, and/or improve endurance of the target area. The electromechanical device 5104 may include at least one or more motor controllers 5120, one or more electric motors 5122, and one or more radially -adjustable couplings 5124. Two pedals 5110 may be coupled to two radially -adjustable couplings 5124 via a left and right pedal assemblies that each include a respective stepper motor. The motor controller 5120 may be operatively coupled to the electric motor 5122 and configured to provide commands to the electric motor 5122 to control operation of the electric motor 5122. The motor controller 5120 may include any suitable microcontroller including a circuit board having one or more processing devices, one or more memory devices (e.g., read-only memory (ROM) and/or random access memory (RAM)), one or more network interface cards, and/or programmable input/output peripherals. The motor controller 5120 may provide control signals or commands to drive the electric motor 5122. The electric motor 5122 may be powered to drive one or more radially -adjustable couplings 5124 of the electromechanical device 5104 in a rotational manner. The electric motor 5122 may provide the driving force to rotate the radially-adjustable couplings 5124 at configurable speeds. The couplings 5124 are radially - adjustable in that a pedal 5110 attached to the coupling 5124 may be adjusted to a number of positions on the coupling 5125 in a radial fashion. Further, the electromechanical device 5104 may include current shunt to provide resistance to dissipate energy from the electric motor 5122. As such, the electric motor 5122 may be configured to provide resistance to rotation of the radially-adjustable couplings 5124.

[1051] The computing device 5102 may be communicatively connected to the electromechanical device 104 via the network interface card on the motor controller 5120. The computing device 5102 may transmit commands to the motor controller 5120 to control the electric motor 5122. The network interface card of the motor controller 5120 may receive the commands and transmit the commands to the electric motor 5122 to drive the electric motor 5122. In this way, the computing device 5102 is operatively coupled to the electric motor 5122.

[1052] The computing device 5102 and/or the motor controller 5120 may be referred to herein as a prehabilitation system or a control system. The user portal 5118 may be referred to as a user interface of the control system herein. The control system may control the electric motor 5122 to operate in a number of modes: passive, active-assisted, resistive, and active. The passive mode may refer to the electric motor 5122 independently driving the one or more radially-adjustable couplings 5124 rotationally coupled to the one or more pedals 5110. In the passive mode, the electric motor 5122 may be the only source of driving force on the radially-adjustable couplings 5124. That is, the user may engage the pedals 5110 with their hands or their feet and the electric motor 5122 may rotate the radially-adjustable couplings 5124 for the user. This may enable moving the affected body part and stretching the affected body part without the user exerting excessive force. [1053] The active-assisted mode may refer to the electric motor 5122 receiving measurements of revolutions per minute of the one or more radially-adjustable couplings 5124, and causing the electric motor 5122 to drive the one or more radially -adjustable couplings 5124 rotationally coupled to the one or more pedals 5110 when the measured revolutions per minute satisfy a threshold condition. The threshold condition may be configurable by the user and/or the physician. The electric motor 5122 may be powered off while the user provides the driving force to the radially -adjustable couplings 5124 as long as the revolutions per minute are above a revolutions per minute threshold and the threshold condition is not satisfied. When the revolutions per minute are less than the revolutions per minute threshold then the threshold condition is satisfied and the electric motor 5122 may be controlled to drive the radially -adjustable couplings 5124 to maintain the revolutions per minute threshold.

[1054] The resistive mode may refer to the electric motor 5122 providing resistance to rotation of the one or more radially -adjustable couplings 5124 coupled to the one or more pedals 5110. The resistive mode may increase the strength of the body part undergoing prehabilitation by causing the muscle to exert force to move the pedals against the resistance provided by the electric motor 5122.

[1055] The active mode may refer to the electric motor 5122 powering off to provide no driving force assistance to the radially -adjustable couplings 5124. Instead, in this mode, the user provides the sole driving force of the radially-adjustable couplings 5124 using their hands or feet, for example.

[1056] During one or more of the modes, each of the pedals 5110 may measure force exerted by a part of the body of the user on the pedal 5110. For example, the pedals 5110 may each contain any suitable sensor (e.g., strain gauge load cell, piezoelectric crystal, hydraulic load cell, etc.) for measuring force exerted on the pedal 5110. Further, the pedals 5110 may each contain any suitable sensor for detecting whether the body part of the user separates from contact with the pedals 5110. In some embodiments, the measured force may be used to detect whether the body part has separated from the pedals 5110. The force detected may be transmitted via the network interface card of the pedal 5110 to the control system (e.g., computing device 5102 and/or motor controller 5120). As described further below, the control system may modify a parameter of operating the electric motor 5122 based on the measured force. Further, the control system may perform one or more preventative actions (e.g., locking the electric motor 5122 to stop the radially-adjustable couplings 124 from moving, slowing down the electric motor 5122, presenting a notification to the user, etc.) when the body part is detected as separated from the pedals 5110, among other things.

[1057] The goniometer 5106 may be configured to measure angles of extension and/or bend of body parts and transmit the measured angles to the computing device 5102 and/or the computing device 5114. The goniometer 5106 may be included in an electronic device that includes the one or more processing devices, memory devices, and/or network interface cards. The goniometer 5106 may be disposed in a cavity of a mechanical brace. The cavity of the mechanical brace may be located near a center of the mechanical brace where the mechanical brace affords to bend and extend. The mechanical brace may be configured to secure to an upper body part (e.g., leg, arm, etc.) and a lower body part (e.g., leg, arm, etc.) to measure the angles of bend as the body parts are extended away from one another or retracted closer to one another.

[1058] The wristband 108 may include a 3-axis accelerometer to track motion in the X, Y, and Z directions, an altimeter for measuring altitude, and/or a gyroscope to measure orientation and rotation. The accelerometer, altimeter, and/or gyroscope may be operatively coupled to a processing device in the wristband 108 and may transmit data to the processing device. The processing device may cause a network interface card to transmit the data to the computing device 5102 and the computing device 5102 may use the data representing acceleration, frequency, duration, intensity, and patterns of movement to track steps taken by the user over certain time periods (e.g., days, weeks, etc.). The computing device 5102 may transmit the steps to the computing device 5114 executing a clinical portal 5126. Additionally, in some embodiments, the processing device of the wristband 5108 may determine the steps taken and transmit the steps to the computing device 5102. In some embodiments, the wristband 5108 may use photo plethysmography (PPG) to measure heartrate that detects an amount of red light or green light on the skin of the wrist. For example, blood may absorb green light so when the heart beats, the blood flow may absorb more green light, thereby enabling detecting heartrate. The heartrate may be sent to the computing device 5102 and/or the computing device 5114.

[1059] The computing device 5102 may present the steps taken by the user and/or the heartrate via respective graphical element on the user portal 5118, as discussed further below. The computing device 5102 may also use the steps taken and/or the heart rate to control a parameter of operating the electromechanical device 5104. For example, if the heartrate exceeds a target heartrate for a pedaling session, the computing device 5102 may control the electric motor 5122 to reduce resistance being applied to rotation of the radially -adjustable couplings 5124. In another example, if the steps taken are below a step threshold for a day, the treatment plan may increase the amount of time for one or more modes that the user in which the user is to operate the electromechanical device 5104 to ensure the affected body part is getting sufficient movement.

[1060] In some embodiments, the cloud-based computing system 5116 may include one or more servers 5128 that form a distributed computing architecture. Each of the servers 128 may include one or more processing devices, memory devices, data storage, and/or network interface cards. The servers 5128 may be in communication with one another via any suitable communication protocol. The servers 5128 may store profiles for each of the users that use the electromechanical device 5104. The profiles may include information about the users such as a treatment plan, the affected body part, any procedure the user had performed on the affected body part, health, age, race, measured data from the goniometer 5106, measured data from the wristband 108, measured data from the pedals 5110, user input received at the user portal 5118 during operation of any of the modes of the treatment plan, a level of discomfort the user experiences before and after any of the modes, before and after session images of the affected body part, and so forth.

[1061] In some embodiments the cloud-based computing system 5116 may include a training engine 5130 that is capable of generating one or more machine learning models 5132. The machine learning models 5132 may be trained to generate treatment plans for the patients in response to receiving various inputs (e.g., a procedure performed on the patient, an affected body part the procedure was performed on, other health characteristics (age, race, fitness level, etc.). The one or more machine learning models 5132 may be generated by the training engine 5130 and may be implemented in computer instructions that are executable by one or more processing device of the training engine 5130 and/or the servers 5128. To generate the one or more machine learning models 5132, the training engine 5130 may train the one or more machine learning models 5132. The training engine 5130 may use a base data set of patient characteristics, treatment plans followed by the patient, and results of the treatment plan followed by the patients. The results may include information indicating whether the treatment plan led to full recovery of the affected body part, partial recover of the affect body part, or lack of recovery of the affected body part. The training engine 5130 may be a rackmount server, a router computer, a personal computer, a portable digital assistant, a smartphone, a laptop computer, a tablet computer, a camera, a video camera, a netbook, a desktop computer, a media center, or any combination of the above. The one or more machine learning models 5132 may refer to model artifacts that are created by the training engine 5130 using training data that includes training inputs and corresponding target outputs. The training engine 5130 may find patterns in the training data that map the training input to the target output, and generate the machine learning models 5132 that capture these patterns. Although depicted separately from the computing device 5102, in some embodiments, the training engine 5130 and/or the machine learning models 5132 may reside on the computing device 5102 and/or the computing device 5114.

[1062] The machine learning models 5132 may include one or more of a neural network, such as an image classifier, recurrent neural network, convolutional network, generative adversarial network, a fully connected neural network, or some combination thereof, for example. In some embodiments, a machine learning model may be supported by a data structure such as a data model. For example, a data model may be a structural framework that is organized according to one or more schemata. A machine learning model may use the data model by applying one or more machine learning techniques to the data model to generate output values or to identify specific data points. In some embodiments, the machine learning models 5132 may be composed of a single level of linear or non-linear operations or may include multiple levels of non-linear operations. For example, the machine learning model 5132 may include numerous layers and/or hidden layers that perform calculations (e.g., dot products) using various neurons.

[1063] FIG. 48A illustrates a perspective view of an example of a device used for prehabilitation, such as the electromechanical device 5104, according to principles of the present disclosure. The electromechanical device 5104 is shown having pedal 5110 on opposite sides that are adjustably positionable relative to one another on respective radially-adjustable couplings 5124. The electromechanical device 5104 is configured as a small and portable unit so that it is easily transported to different locations at which prehabilitation or treatment is to be provided, such as at patients’ homes, alternative care facilities, or the like. The patient may sit in a chair proximate the electromechanical device 5104 to engage the device 5104 with their feet, for example.

[1064] The electromechanical device 5104 includes a rotary device such as radially-adjustable couplings 5124 or flywheel or the like rotatably mounted such as by a central hub to a frame 5016 or other support. The pedals 5110 are configured for interacting with a patient to be rehabilitated and may be configured for use with lower body extremities such as the feet, legs, or upper body extremities, such as the hands, arms, and the like. For example, the pedal 5110 may be a bicycle pedal of the type having a foot support rotatably mounted onto an axle with bearings. The axle may or may not have exposed end threads for engaging a mount on the radially- adjustable coupling 5124 to locate the pedal on the radially-adjustable coupling 5124. The radially-adjustable coupling 5124 may include an actuator configured to radially adjust the location of the pedal to various positions on the radially-adjustable coupling 5124.

[1065] The radially-adjustable coupling 5124 may be configured to have both pedals 5110 on opposite sides of a single coupling 5124. In some embodiments, as depicted, a pair of radially-adjustable couplings 5124 may be spaced apart from one another but interconnected to the electric motor 5122. In the depicted example, the computing device 5102 may be mounted on the frame 5200 and may be detachable and held by the user while the user operates the device 5104. The computing device 5102 may present the user portal 5118 and control the operation of the electric motor 5122, as described herein.

[1066] FIG. 48B generally illustrates a perspective view of another example of an exercise and prehabilitation device, such as the electromechanical device 5104 according to principles of the present disclosure. The electromechanical device 5104 takes the form of a traditional exercise/prehabilitation device which is more or less non-portable and remains in a fixed location, such as a prehabilitation clinic or medical practice. The electromechanical device 5104 in FIG. 48B may include similar features described in FIG. 48A except the electromechanical device 5104 in FIG. 48B includes a seat and is less portable.

[1067] FIG. 49 generally illustrates example operations of a method 5300 for controlling an electromechanical device 5104 for prehabilitation in various modes according to principles of the present disclosure. The method 5300 may be performed by processing logic that may include hardware (circuitry, dedicated logic, etc.), software, or a combination of both. The method 5300 and/or each of their individual functions, subroutines, or operations may be performed by one or more processors of a control system (e.g., computing device 5102 of FIG. 47) implementing the method 5300. The method 5300 may be implemented as computer instructions that, when executed by a processing device, execute the user portal 5118. In certain implementations, the method 5300 may be performed by a single processing thread. Alternatively, the method 5300 may be performed by two or more processing threads, each thread implementing one or more individual functions, routines, subroutines, or operations of the methods. Various operations of the method 5300 may be performed by one or more of the cloud-based computing system 5116, the motor controller 5120, the pedals 5110, the goniometer 5106, the wristband 5108, and/or the computing device 114 of FIG. 47.

[1068] As discussed above, an electromechanical device 5104 may include one or more pedals 5110 coupled to one or more radially -adjustable couplings 5124, an electric motor 5122 coupled to the one or more pedals 5110 via the one or more radially-adjustable couplings 5124, and the control system including one or more processing devices operatively coupled to the electric motor 5122. In some embodiments, the control system (e.g., computing device 5102 and/or motor controller 5120) may store instructions and one or more operations of the control system may be presented via the user portal 5118. In some embodiments the radially- adjustable couplings 5124 are configured for translating rotational motion of the electric motor 5122 to radial motion of the pedals 5110.

[1069] At block 5302, responsive to a first trigger condition occurring, the processing device may control the electric motor 5122 to operate in a passive mode by independently driving the one or more radially-adjustable couplings 5124 rotationally coupled to the one or more pedals 5110. “Independently drive” may refer to the electric motor 5122 driving the one or more radially-adjustable couplings 5124 without the aid of another driving source (e.g., the user). The first trigger condition may include an initiation of a pedaling session via the user interface of the control system, a period of time elapsing, a detected physical condition (e.g., heartrate, oxygen level, blood pressure, etc.) of a user operating the electromechanical device 5104, a request received from the user via the user interface, or a request received via a computing device communicatively coupled to the control system (e.g., a request received from the computing device 5114 executing the clinical portal 5126). The processing device may control the electric motor 5122 to independently drive the one or more radially-adjustable couplings 5124 rotationally coupled to the one or more pedals 5110 at a controlled speed specified in a treatment plan for a user operating the electromechanical device 5104 while operating in the passive mode.

[1070] In some embodiments, the electromechanical device 5104 may be configured such that the processor controls the electric motor 5122 to individually drive the radially-adjustable couplings 5124. For example, the processing device may control the electric motor 5122 to individually drive the left or right radially- adjustable coupling, while allowing the user to provide the force to drive the other radially-adjustable coupling. As another example, the processing device may control the electric motor 5122 to drive both the left and right radially -adjustable couplings but at different speeds. This granularity of control may be beneficial by controlling the speed at which a healing body part is moved (e.g., rotated, flexed, extended, etc.) to avoid tearing tendons or causing pain to the user.

[1071] At block 5304, responsive to a second trigger condition occurring, the processing device may control the electric motor 5122 to operate in an active-assisted mode by measuring (block 5306) revolutions per minute of the one or more radially -adjustable couplings 5124, and causing (block 5308) the electric motor 5122 to drive the one or more radially -adjustable couplings5 124 rotationally coupled to the one or more pedals 5110 when the measured revolutions per minute satisfy a threshold condition. The second trigger condition may include an initiation of a pedaling session via the user interface of the control system, a period of time elapsing, a detected physical condition (e.g., heartrate, oxygen level, blood pressure, etc.) of a user operating the electromechanical device 5104, a request received from the user via the user interface, or a request received via a computing device communicatively coupled to the control system (e.g., a request received from the computing device 5114 executing the clinical portal 5126). The threshold condition may be satisfied when the measured revolutions per minute are less than a minimum revolutions per minute. In such an instance, the electric motor 5122 may begin driving the one or more radially-adjustable couplings 5124 to increase the revolutions per minute of the radially-adjustable couplings 5124.

[1072] As with the passive mode, the processing device may control the electric motor 5122 to individually drive the one or more radially-adjustable couplings 5124 in the active-assisted mode. For example, if just a right knee is being rehabilitated, the revolutions per minute of the right radially-adjustable coupling 5124 may be measured and the processing device may control the electric motor 5122 to individually drive the right radially- adjustable coupling 5124 when the measured revolutions per minute is less than the minimum revolutions per minute. In some embodiments, there may be different minimum revolution per minutes set for the left radially- adjustable coupling and the right radially-adjustable coupling, and the processing device may control the electric motor 5122 to individually drive the left radially-adjustable coupling and the right radially-adjustable coupling as appropriate to maintain the different minimum revolutions per minute.

[1073] At block 5310, responsive to a third trigger condition occurring, the processing device may control the electric motor 5122 to operate in a resistive mode by providing resistance to rotation of the one or more radially-adjustable couplings 5124 coupled to the one or more pedals 5110. The third trigger condition may include an initiation of a pedaling session via the user interface of the control system, a period of time elapsing, a detected physical condition (e.g., heartrate, oxygen level, blood pressure, etc.) of a user operating the electromechanical device 5104, a request received from the user via the user interface, or a request received via a computing device communicatively coupled to the control system (e.g., a request received from the computing device 5114 executing the clinical portal 5126).

[1074] In some embodiments, responsive to a fourth trigger condition occurring, the processing device is further configured to control the electric motor 5122 to operate in an active mode by powering off to enable another source (e.g., the user) to drive the one or more radially-adjustable couplings 5124 via the one or more pedals 5110. In the active mode, another source may drive the one or more radially-adjustable couplings 5124 via the one or more pedals 5110 at any desired speed. [1075] In some embodiments, the processing device may control the electric motor 5122 to operate in each of the passive mode, the active-assisted mode, the resistive mode, and/or the active mode for a respective period of time during a pedaling session based on a treatment plan for a user operating the electromechanical device 5104. In some embodiments, the various modes and the respective periods of time may be selected by a clinician that sets up the treatment plan using the clinical portal 5126. In some embodiments, the various modes and the respective periods of time may be selected by a machine learning model trained to receive parameters (e.g., procedure performed on the user, body part on which the procedure was performed, health of the user) and to output a treatment plan to rehabilitate the affected body part, as described above.

[1076] In some embodiments, the processing device may modify one or more positions of the one or more pedals 5110 on the one or more radially -adjustable couplings 5124 to change one or more diameters of ranges of motion of the one or more pedals 5110 during any of the passive mode, active-assisted mode, the resistive mode, and/or the active mode throughout a pedaling session for a user operating the electromechanical device 5104. The processing device may be further configured to modify the position of one of the one or more pedals 5110 on one of the one or more radially -adjustable couplings 5124 to change the diameter of the range of motion of the one of the one or more pedals 5110 while maintaining another position of another of the one or more pedals 5110 on another of the one or more radially -adjustable couplings 5124 to maintain another diameter of another range of motion of the another pedal. In some embodiments, the processing device may cause both positions of the pedals 5110 to move to change the diameter of the range of motion for both pedals 5110. The amount of movement of the positions of the pedals 5110 may be individually controlled in order to provide different diameters of ranges of motions of the pedals 5110 as desired.

[1077] In some embodiments, the processing device may receive, from the goniometer 5106 worn by the user operating the electromechanical device 5104, at least one of an angle of extension of a joint of the user during a pedaling session or an angle of bend of the joint of the user during the pedaling session. In some instances, the joint may be a knee or an elbow. The goniometer 5106 may be measuring the angles of bend and/or extension of the joint and continuously or periodically transmitting the angle measurements that are received by the processing device. The processing device may modify the positions of the pedals 5110 on the radially -adjustable couplings 5124 to change the diameters of the ranges of motion of the pedals 5110 based on the at least one of the angle of extension of the joint of the user or the angle of bend of the joint of the user. [1078] In some embodiments, the processing device may receive, from the goniometer 5106 worn by the user, a set of angles of extension between an upper leg and a lower leg at a knee of the user as the user extends the lower leg away from the upper leg via the knee. In some embodiments, the goniometer 5106 may send the set of angles of extension between an upper arm, upper body, etc. and a lower arm, lower body, etc. The processing device may present, on a user interface of the control system, a graphical animation of the upper leg, the lower leg, and the knee of the user as the lower leg is extended away from the upper leg via the knee. The graphical animation may include the set of angles of extension as the set of angles of extension change during the extension. The processing device may store, in a data store of the control system, a lowest value of the set of angles of extension as an extension statistic for an extension session. A set of extension statistics may be stored for a set of extension sessions specified by the treatment plan. The processing device may present progress of the set of extension sessions throughout the treatment plan via a graphical element (e.g., line graph, bar chart, etc.) on the user interface presenting the set of extension statistics. [1079] In some embodiments, the processing device may receive, from the goniometer 5106 worn by the user, a set of angles of bend or flex between an upper leg and a lower leg at a knee of the user as the user retracts the lower leg closer to the upper leg via the knee. In some embodiments, the goniometer 5106 may send the set of angles of bend between an upper arm, upper body, etc. and a lower arm, lower body, etc. The processing device may present, on a user interface of the control system, a graphical animation of the upper leg, the lower leg, and the knee of the user as the lower leg is retracted closer to the upper leg via the knee. The graphical animation may include the set of angles of bend as the set of angles of bend change during the bending. The processing device may store, in a data store of the control system, a highest value of the set of angles of bend as a bend statistic for a bend session. A set of bend statistics may be stored for a set of bend sessions specified by the treatment plan. The processing device may present progress of the set of bend sessions throughout the treatment plan via a graphical element (e.g., line graph, bar chart, etc.) on the user interface presenting the set of bend statistics.

[1080] In some embodiments, the angles of extension and/or bend of the joint may be transmitted by the goniometer 5106 to the computing device 5114 executing the clinical portal 5126. A clinician may be operating the computing device 5114 executing the clinical portal 5126. The clinical portal 5126 may present a graphical animation of the upper leg extending away from the lower leg and/or the upper leg bending closer to the lower leg in real-time during a pedaling session, extension session, and/or a bend session of the user. In some embodiments, the clinician portal 5126 may provide notifications to the computing device 5102 to present via the user portal 5118. The notifications may indicate that the user has satisfied a target extension and/or bend angle. Other notifications may indicate that the user has extended or retracted a body part too far and should cease the extension and/or bend session. In some embodiments, the computing device 5114 executing the clinical portal 5126 may transmit a control signal to the control system to move a position of a pedal 5110 on the radially -adjustable coupling 5124 based on the angle of extension or angle of bend received from the goniometer 5106. That is, the clinician can increase a diameter of range of motion for a body part of the user in real-time based on the measured angles of extension and/or bend during a pedaling session. This may enable the clinician dynamically control the pedaling session to enhance the prehabilitation results of the pedaling session. [1081] In some embodiments, the processing device may receive, from a wearable device (e.g., wristband 108), an amount of steps taken by a user over a certain time period (e.g., day, week, etc.). The processing device may calculate whether the amount of steps satisfies a step threshold of a walking session of a treatment plan for the user. The processing device may present the amount of steps taken by the user on a user interface of the control system and may present an indication of whether the amount of steps satisfies the step threshold. [1082] The wristband 5108 may also measure one or more vital statistics of the user, such as a heartrate, oxygen level, blood pressure, and the like. The measurements of the vital statistics may be performed at any suitable time, such as during a pedaling session, walking session, extension session, and/or bend session. The wristband 5108 may transmit the one or more vital statistics to the control system. The processing device of the control system may use the vital statistics to determine whether to reduce resistance the electric motor 5122 is providing to lower one of the vital statistics (e.g., heartrate) when that vital statistic is above a threshold, to determine whether the user is in pain when one of the vital statistics is elevated beyond a threshold, to determine whether to provide a notification indicating the user should take a break or increase the intensity of the appropriate session, and so forth. [1083] In some embodiments, the processing device may receive a request to stop the one or more pedals 110 from moving. The request may be received by a user selecting a graphical icon representing “stop” on the user portal 5118 of the control system. The processing device may cause the electric motor 5122 to lock and stop the one or more pedals from moving over a configured period of time (e.g., instantly, over 1 second, 2 seconds, 3 seconds, 5 seconds, 10 seconds, etc.). One benefit of including an electric motor 5122 in the electromechanical device 104 is the ability to stop the movement of the pedals 5110 as soon as a user desires. [1084] In some embodiments, the processing device may receive, from one or more force sensors operatively coupled to the one or more pedals and the one or more processing devices, one or more measurements of force on the one or more pedals. The force sensors may be operatively coupled with the one or more processing devices via a wireless connection (e.g., Bluetooth) provided by wireless circuitry of the pedals. The processing device may determine whether the user has fallen from the electromechanical device 104 based on the one or more measurements of force. Responsive to determining that the user has fallen from the electromechanical device 104, the processing device may lock the electric motor 5122 to stop the one or more pedals 110 from moving.

[1085] Additionally or alternatively, the processing device may determine that feet or hands have separated from the pedals 5110 based on the one or more measurements of force. In response to determining that the feed or hands have separated from the pedals 5110, the processing device may lock the electric motor 5122 to stop the one or more pedals 5110 from moving. Also, the processing device may present a notification on a user interface of the control system that instructs the user to place their feet or hands in contact with the pedals 5110. [1086] In some embodiments, the processing device may receive, from the force sensors operatively coupled to the one or more pedals 5110, the measurements of force exerted by a user on the pedals 5110 during a pedaling session. The processing device may present the respective measurements of force on each of the pedals 5110 on a separate respective graphical scale on the user interface of the control system while the user pedals during the pedaling session. Various graphical indicators may be presented on the user interface to indicate when the force is below a threshold target range, within the threshold target range, and/or exceeds the threshold target range. Notifications may be presented to encourage the user to apply more force and/or less force to achieve the threshold target range of force. For example, the processing device is to present a first notification on the user interface when the one or more measurements of force satisfy a pressure threshold and present a second notification on the user interface when the one or more measurements do not satisfy the pressure threshold.

[1087] In addition, the processing device may provide an indicator to the user based on the one or more measurements of force. The indicator may include at least one of (1) providing haptic feedback in the pedals, handles, and/or seat of the electromechanical device 5104, (2) providing visual feedback on the user interface (e.g., an alert, a light, a sign, etc.), (3) providing audio feedback via an audio subsystem (e.g., speaker) of the electromechanical device 5104, or (4) illuminating a warning light of the electromechanical device 5104. [1088] In some embodiments, the processing device may receive, from an accelerometer of the control system, motor controller 5120, pedal 5110, or the like, a measurement of acceleration of movement of the electromechanical device 5104. The processing device may determine whether the electromechanical device 5104 has moved excessively relative to a vertical axis (e.g., fallen over) based on the measurement of acceleration. Responsive to determining that the electromechanical device 5104 has moved excessively relative to the vertical axis based on the measurement of acceleration, the processing device may lock the electric motor 5122 to stop the one or more pedals 5110 from moving.

[1089] After a pedaling session is complete, the processing device may lock the electric motor 5122 to prevent the one or more pedals from moving a certain amount of time after the completion of the pedaling session. This may enable healing of the body part being rehabilitated and prevent strain on that body part by excessive movement. Upon expiration of the certain amount of time, the processing device may unlock the electric motor 5122 to enable movement of the pedals 5110 again.

[1090] The user portal 5118 may provide an option to image the body part being rehabilitated. For example, the user may place the body part within an image capture section of the user portal 5118 and select an icon to capture an image of the body part. The images may be captured before and after a pedaling session, walking session, extension session, and/or bend session. These images may be sent to the cloud-based computing system to use as training data for the machine learning model to determine the effects of the session. Further, the images may be sent to the computing device 5114 executing the clinical portal 5126 to enable the clinician to view the results of the sessions and modify the treatment plan if desired and/or provide notifications (e.g., reduce resistance, increase resistance, extend the joint further or less, etc.) to the user if desired.

[1091] FIG. 50 generally illustrates example operations of a method 5400 for controlling an amount of resistance provided by an electromechanical device 5104 according to principles of the present disclosure. Method 5400 includes operations performed by processing devices of the control system (e.g., computing device 5102) of FIG. 36. In some embodiments, one or more operations of the method 5400 are implemented in computer instructions that, when executed by a processing device, execute the control system and/or the user portal 5118. Various operations of the method 5400 may be performed by one or more of the computing device 5114, the cloud-based computing system 5116, the motor controller 5120, the pedal 5110, the goniometer 5106, and/or the wristband 5108. The method 5400 may be performed in the same or a similar manner as described above in regards to method 5300.

[1092] At block 5402, the processing device may receive configuration information for a pedaling session. The configuration information may be received via selection by the user on the user portal 5118 executing on the computing device 5102, received from the computing device 5114 executing the clinical portal 5126, downloaded from the cloud-based computing system 5116, retrieved from a memory device of the computing device 5102 executing the user portal 5118, or some combination thereof. For example, the clinician may select the configuration information for a pedaling session of a patient using the clinical portal 5126 and upload the configuration information from the computing device 5114 to a server of the cloud-based computing system 116.

[1093] The configuration information for the pedaling session may specify one or more modes in which the electromechanical device 5104 is to operate, and configuration information specific to each of the modes, an amount of time to operate each mode, and the like. For example, for a passive mode, the configuration information may specify a position for the pedal to be in on the radially -adjustable couplings 5124 and a speed at which to control the electric motor 5122. For the resistive mode, the configuration information may specify an amount of resistive force the electric motor 5122 is to apply to rotation of radially -adjustable couplings 5124 during the pedaling session, a maximum pedal force that is desired for the user to exert on each pedal 5110 of the electromechanical device 5104 during the pedaling session, and/or a revolutions per minute threshold for the radially-adjustable couplings 5124. For the active-assisted mode, the configuration information may specify a minimum pedal force and a maximum pedal force that is desired for the user to exert on each pedal of the electromechanical device 5104, a speed to operate the electric motor 5122 at which to drive one or both of the radially-adjustable couplings 5124, and so forth.

[1094] In some embodiments, responsive to receiving the configuration information, the processing device may determine that a trigger condition has occurred. The trigger condition may include receiving a selection of a mode from a user, an amount of time elapsing, receiving a command from the computing device 5114 executing the clinical portal 5126, or the like. The processing device may control, based on the trigger condition occurring, the electric motor 5122 to operate in a resistive mode by providing a resistance to rotation of the pedals 5110 based on the trigger condition.

[1095] At block 5404, the processing device may set a resistance parameter and a maximum pedal force parameter based on the amount of resistive force and the maximum pedal force, respectively, included in the configuration information for the pedaling session. The resistance parameter and the maximum force parameter may be stored in a memory device of the computing device 5102 and used to control the electric motor 5122 during the pedaling session. For example, the processing device may transmit a control signal along with the resistance parameter and/or the maximum pedal force parameter to the motor controller 5120, and the motor controller 5120 may drive the electric motor 5122 using at least the resistance parameter during the pedaling session.

[1096] At block 5406, the processing device may measure force applied to pedals 5110 of the electromechanical device 5104 as a user operates (e.g., pedals) the electromechanical device 5104. The electric motor 5122 of the electromechanical device 104 may provide resistance during the pedaling session based on the resistance parameter. A force sensor disposed in each pedal 5110 and operatively coupled to the motor controller 5120 and/or the computing device 5102 executing the user portal 5118 may measure the force exerted on each pedal throughout the pedaling session. The force sensors may transmit the measured force to a processing device of the pedals 5110, which in turn causes a communication device to transmit the measured force to the processing device of the motor controller 5120 and/or the computing device 5102.

[1097] At block 5408, the processing device may determine whether the measured force exceeds the maximum pedal force parameter. The processing device may compare the measured force to the maximum pedal force parameter to make this determination.

[1098] At block 5410, responsive to determining that the measured force exceeds the maximum pedal force parameter, the processing device may reduce the resistance parameter so the electric motor 5122 applies less resistance during the pedaling session to maintain the revolutions per minute threshold specified in the configuration information. Reducing the resistance may enable the user to pedal faster, thereby increasing the revolutions per minute of the radially-adjustable couplings 5124. Maintaining the revolutions per minute threshold may ensure that the patient is exercising the affected body part as rigorously as desired during the mode. In response to determining that the measured force does not exceed the maximum pedal force parameter, the processing device may maintain the same maximum pedal force parameter specified by the configuration information during the pedaling session.

[1099] In some embodiments, the processing device may determine than a second trigger condition has occurred. The second trigger condition may include receiving a selection of a mode from a user via the user portal 5118, an amount of time elapsing, receiving a command from the computing device 5114 executing the clinical portal 5126, or the like. The processing device may control, based on the trigger condition occurring, the electric motor 5122 to operate in a passive mode by independently driving one or more radially -adjustable couplings 5124 coupled to the pedals 5110 in a rotational fashion. The electric motor 5122 may drive the one or more radially-adjustable couplings 124 at a speed specified in the configuration information without another driving source. Also, the electric motor 5122 may drive each of the one or more radially-adjustable couplings 5124 individually at different speeds.

[1100] In some embodiments, the processing device may determine that a third trigger condition has occurred. The third trigger condition may be similar to the other trigger conditions described herein. The processing device may control, based on the third trigger condition occurring, the electric motor 5122 to operate in an active-assisted mode by measuring revolutions per minute of the one or more radially-adjustable couplings 5124 coupled to the pedals 5110 and causing the electric motor 5122 to drive in a rotational fashion the one or more radially-adjustable couplings 5124 coupled to the pedals 5110 when the measured revolutions per minute satisfy a threshold condition.

[1101] In some embodiments, the processing device may receive, from the goniometer 5106 worn by the user operating the electromechanical device 5104, a set of angles of extension between an upper leg and a lower leg at a knee of the user. The set of angles are measured as the user extends the lower leg away from the upper leg via the knee. In some embodiments, the angles of extension may represent angles between extending a lower arm away from an upper arm at an elbow. Further, the processing device may receive, from the goniometer 5106, a set of angles of bend between the upper leg and the lower leg at the knee of the user. The set of angles of bend are measured as the user retracts the lower leg closer to the upper leg via the knee. In some embodiments, the angles of bend represent angles between bending a lower arm closer to an upper arm at an elbow.

[1102] The processing device may determine whether a range of motion threshold condition is satisfied based on the set of angles of extension and the set of angles of bend. Responsive to determining that the range of motion threshold condition is satisfied, the processing device may modify a position of one of the pedals 5110 on one of the radially-adjustable couplings 5124 to change a diameter of a range of motion of the one of the pedals 5110. Satisfying the range of motion threshold condition may indicate that the affected body part is strong enough or flexible enough to increase the range of motion allowed by the radially-adjustable couplings 5124. [1103] FIG. 51 generally illustrates example operations of a method 5500 for measuring angles of bend and/or extension of a lower leg relative to an upper leg using the goniometer 5106 according to principles of the present disclosure. In some embodiments, one or more operations of the method 5500 are implemented in computer instructions that are executed by the processing devices of the goniometer 5106. 106 of FIG. 36. The method 5500 may be performed in the same or a similar manner as described above in regards to method 5300. [1104] At block 5502, the processing device may receive a set of angles from the one or more goniometers

5106. The goniometer 5106 may measure angles of extension and/or bend between an upper body part (leg, arm, torso, neck, head, etc.) and a lower body part (leg, arm, torso, neck head, hand, feet, etc.) as the body parts are extended and/or bent during various sessions (e.g., pedaling session, walking session, extension session, bend session, etc.). The set of angles may be received while the user is pedaling one or more pedals 5110 of the electromechanical device 5104. [1105] At block 5504, the processing device may transmit, via one or more network interface cards, the set of angles to a computing device controlling the electromechanical device 5104. The electromechanical device 5104 may be operated by a user rehabilitating an affected body part. For example, the user may have recently had surgery to repair a second or third degree sprain of an anterior cruciate ligament (ACL). Accordingly, the goniometer 5106 may be seemed proximate to the knee around the upper and lower leg by the affected ACL. [1106] In some embodiments, transmitting the set of angles to the computing device 5102 controlling the electromechanical device 5104 may cause the computing device 5102 to adjust a position of one of one or more pedals 5110 on the radially -adjustable coupling 5124 based on the set of angles satisfying a range of motion threshold condition. The range of motion threshold condition may be set based on configuration information for a treatment plan received from the cloud-based computing system 5116 or the computing device 5114 executing the clinical portal 5126. The position of the pedal 5110 is adjusted to increase a diameter of a range of motion transited by an upper body part (e.g., leg), lower body part (e.g., leg), and a joint (e.g., knee) of the user as the user operates the pedal 5110. In some embodiments, the position of the pedal 5110 may be adjusted in real-time while the user is operating the electromechanical device 5104. In some embodiments, the user portal 5118 may present a notification to the user indicating that the position of the pedal 5110 should be modified, and the user may modify the position of the pedal 5110 and resume operating the electromechanical device 5104 with the modified pedal position.

[1107] In some embodiments, transmitting the set of angles to the computing device 5102 may cause the computing device 5102 executing the user portal 5118 to present the set of angles in a graphical animation of the lower body part and the upper body part moving in real-time during the extension or the bend. In some embodiments, the set of angles may be transmitted to the computing device 5114 executing the clinical portal 5126, and the clinical portal 5126 may present the set of angles in a graphical animation of the lower body part and the upper body part moving in real-time during the extension or the bend. In addition, the set of angles may be presented in one or more graphs or charts on the clinical portal 5126 and/or the user portal 5118 to depict progress of the extension or bend for the user.

[1108] FIGs. 52-58 generally illustrate various detailed views of the components of the prehabilitation system disclosed herein.

[1109] For example, FIG. 52 generally illustrates an exploded view of components of the electromechanical device 5104 according to principles of the present disclosure. The electromechanical device 5104 may include a pedal 5110 that couples to a left radially -adjustable coupling via a left pedal arm assembly 5600 disposed within a cavity of the left radially -adjustable coupling. The radially -adjustable coupling 5124 may be disposed in a circular opening of a left outer cover 5601 and the pedal arm assembly 5600 may be secured to a drive sub-assembly 5602. The drive sub-assembly 5602 may include the electric motor 5122 that is operatively coupled to the motor controller 120. The drive sub-assembly 5602 may include one or more braking mechanisms, such as disk brakes, that enable instantaneously locking the electric motor 5122 or stopping the electric motor 5122 over a period of time. The electric motor 5122 may be any suitable electric motor (e.g., a crystallite electric motor). The drive sub-assembly 5602 may be secured to a frame sub-assembly 5604. A top support sub-assembly 5606 may be seemed on top of the drive sub-assembly 5602.

[1110] A right pedal 5110 couples to a left radially-adjustable coupling 5124 via a right pedal arm assembly 5600 disposed within a cavity of the right radially-adjustable coupling 5124. The right radially-adjustable coupling 5124 may be disposed in a circular opening of a right outer cover 5608 and the right pedal arm assembly 5600 may be secured to the drive sub-assembly 5602. An internal volume may be defined when the left outer cover 5601 and the right outer cover 5608 are seemed together mound the frame sub-assembly 5604. The left outer cover 5601 and the right outer cover 5608 may also make up the frame of the device 104 when secured together. The drive sub-assembly 5602, top support sub-assembly 5606, and pedal arm assemblies 5600 may be disposed within the internal volume upon assembly. A storage compartment 5610 may be seemed to the frame. [1111] Further, a computing device arm assembly 5612 may be seemed to the frame and a computing device mount assembly 5614 may be secured to an end of the computing device arm assembly 5612. The computing device 5102 may be attached or detached from the computing device mount assembly 5614 as desired during operation of the device 5104.

[1112] FIG. 53 generally illustrates an exploded view of a pedal assembly 5600 according to principles of the present disclosure. The pedal assembly 5600 includes a stepper motor 5700. The stepper motor 5700 may be any suitable stepper motor. The stepper motor 5700 may include multiple coils organized in groups referred to as phases. Each phase may be energized in sequence to rotate the motor one step at a time. The control system may use the stepper motor 5700 to move the position of the pedal 5110 on the radially -adjustable coupling 5124. [1113] The stepper motor 5700 includes a barrel and pin that are inserted through a hole in a motor mount 5702. A shaft coupler 5704 and a bearing 5706 include through holes that receive an end of a first end leadscrew 5708. The leadscrew 5708 is disposed in a lower cavity of a pedal arm 5712. The pin of the electric motor 5122 may be inserted in the through holes of the shaft coupler 5704 and the bearing 5704 to secure to the first end of the leadscrew 5708. The motor mount 5702 may be secured to a frame of the pedal arm 5712. Another bearing 5706 may be disposed on another end of the leadscrew 5708. An electric slip ring 5710 may be disposed on the pedal arm 5712.

[1114] A linear rail 5714 is disposed in and secured to an upper cavity of the pedal arm 5712. The linear rail 5714 may be used to move the pedal to different positions as described further below. A number of linear bearing blocks 5716 are disposed onto a top rib and a bottom rib of the linear rail 5714 such that the bearing blocks 5716 can slide on the ribs. A spindle carriage 5718 is secured to each of the bearing blocks 5716. A support bearing 5720 is used to provide support. The lead screw may be inserted in through hole 5722 of the spindle carriage 5718. A lead screw unit 5724 may be secured at an end of the through hole 5722 to house an end of the lead screw 5708. A spindle 5724 is attached to a hole of the spindle carriage 5718. The end of the spindle 5724 protrudes through a hole of a pedal arm cover 5726 when the pedal arm assembly 5600 is assembled. When the stepper motor 5700 turns on, the lead screw 5708 can be rotated, thereby causing the spindle carriage 5718 to move radially along the linear rail 5714. As a result, the spindle 5724 may radially traverse the opening of the pedal arm cover 5726 as desired.

[1115] FIG. 54 generally illustrates an exploded view of adrive sub-assembly 5602 according to principles of the present disclosure. The drive sub-assembly 5602 includes an electric motor 5122. The electric motor 5122 is partially disposed in a crank bracket housing 5800. A side of the electric motor 5122 includes a small molded pulley 5802 seemed to it via a small pulley plate 5804 by screws 5806. Also disposed within the crank bracket housing 5800 is a timing belt 5808 and a large molded pulley 5810. The timing belt 5808 may include teeth on an interior side that engage with teeth on the small molded pulley 5802 and the large molded pulley 5810 to cause the large molded pulley 5810 to rotate when the electric motor 5122 operates. The crank bracket housing 5800 includes mounted bearing 5814 on both sides through which cranks 5814 of the large molded pulley 5810 protrude. The cranks 5814 may be operatively coupled to the pedal assemblies.

[1116] FIG. 55 generally illustrates an exploded view of a portion of a goniometer 5106 according to principles of the present disclosure. The goniometer 5106 includes an upper section 5900 and a lower section 5902. The upper section 5900 and the lower section 5902 are rotatably coupled via a lower leg side brace 5904. A bottom cap 5906 is inserted into a pro traded cavity of the lower leg side brace 5904. In some embodiments the bottom cap 5906 includes a microcontroller 5908. A thrust roller bearing 5910 fits over the protruded cavity of the lower leg side brace, which is inserted into a cavity of the upper section 5900 and secured to the upper section 5900 via a screw. Another cavity is located of the upper section 5900 is on a side of the upper section 5900 opposite to the side having the cavity with the inserted protruded cavity. A radial magnet 5912 and a microcontroller (e.g., printed control board) 5914 are disposed in another cavity and a top cap 916 is placed on top to cover the other cavity. The microcontroller 5908 and/or the microcontroller 5914 may include a network interface card or a radio configured to communicate via a short range wireless protocol (e.g., Bluetooth), a processing device, and a memory device. Further, either or both of the microcontrollers 5908 and 5914 may include a magnetic sensing encoder chip that senses the position of the radial magnet 5912. The position of the radial magnet 5912 may be used to determine an angle of bend or extension of the goniometer 5106 by the processing device(s) of the microcontrollers 5908 and/or 5914. The angles of bend/extension may be transmitted via the radio to the computing device 5102.

[1117] FIG. 56 generally illustrates a top view of a wristband 5108 according to principles of the present disclosure. The wristband 5108 includes a strap with a clasp to secure the strap to a wrist of a person. The wristband 5108 may include one or more processing devices, memory devices, network interface cards, and so forth. The wristband 5108 may include a display 51000 configured to present information measured by the wristband 5108. The wristband 5108 may include an accelerometer, gyroscope, and/or an altimeter, as discussed above. The wristband 5108 may also include a light sensor to detect a heartrate of the user wearing the wristband 5108. In some embodiments, the wristband 5108 may include a pulse oximeter to measure an amount of oxygen (oxygen saturation) in the blood by sending infrared light into capillaries and measuring how much light is reflected off the gases. The wristband 5108 may transmit the measurement data to the computing device 5102. [1118] FIG. 57 generally illustrates an exploded view of a pedal 5110 according to principles of the present disclosure. The pedal 5110 includes a molded pedal top 51100 disposed on top of a molded pedal top support plate 51102. The molded pedal top 51100 and the molded pedal top support plate 51102 are seemed to a molded pedal base plate 51104 via screws, for example. The molded pedal base plate 51104 includes a strain gauge 51106 configured to measure force exerted on the pedal 5110. The pedal 5110 also includes a molded pedal bottom 51108 where a microcontroller 51110 is disposed. The microcontroller 51110 may include processing devices, memory devices, and/or a network interface card or radio configured to communicate via a short range communication protocol, such as Bluetooth. The strain gauge 51106 is operatively coupled to the microcontroller 51110 and the strain gauge 51106 transmits the measured force to the microcontroller 51110. The microcontroller 51110 transmits the measured force to the computing device 5102 and/or the motor controller 5120 of the electromechanical device 5104. The molded pedal top 51100, the molded pedal top support plate 51102, the molded pedal base plate 51104 are secured to the molded pedal bottom 51108, which is further secured to a molded pedal bottom cover 51112. The pedal 110 also includes a spindle 51114 that couples with the pedal arm assembly.

[1119] FIG. 58 generally illustrates additional views of the pedal according to principles of the present disclosure. A top view 51200 of the pedal is depicted, a perspective view 51202 of the pedal is depicted, a front view 51204 of the pedal is depicted, and a side view 51206 of the pedal is depicted.

[1120] FIGs. 59-73 generally illustrate different user interfaces of the user portal 5118. A user may use the computing device 5102, such as a tablet, to execute the user portal 5118. In some embodiments, the user may hold the tablet in their hands and view the user portal 5118 as they perform a pedaling session. Various user interfaces of the user portal 5118 may provide prompts for the user to affirm that they are wearing the goniometer 5106 and the wristband 5108, and that their feet are on the pedals 5110.

[1121] FIG. 59 generally illustrates an example user interface 51300 of the user portal 5118, the user interface 51300 presenting a treatment plan 51302 for a user according to principles of the present disclosure. The treatment plan 51302 may be received from the computing device 5114 executing the clinical portal 5126 and/or downloaded from the cloud-based computing system 5116. The physician may have generated the treatment plan 51302 using the clinical portal 5126 or the trained machine learning model(s) 5132 may have generated the treatment plan 51302 for the user. As depicted, the treatment plan 51302 presents the type of procedure (“right knee replacement”) that the patient underwent. Further, the treatment plan 51302 presents a pedaling session including a combination of the modes in which to operate the electromechanical device 5104, as well as a respective set period of time for operating each of the modes. For example, the treatment plan 51302 indicates operating the electromechanical device 5104 in a passive mode for 5 minutes, an active-assisted mode for 5 minutes, an active mode for 5 minutes, a resistive mode for 2 minutes, an active mode for 3 minutes, and a passive mode for 2 minutes. The total duration of the pedaling session is 22 minutes and the treatment plan 51302 also specifies that the position of the pedal may be set according to a comfort level of the patient. The user interface 51300 may be displayed as an introductory user interface prior to the user beginning the pedaling session.

[1122] FIG. 60 generally illustrates an example user interface 51400 of the user portal 5118, the user interface 51400 presenting pedal settings 51402 for a user according to principles of the present disclosure. As depicted graphical representation of feet are presented on the user interface 51400 and two sliders including positions corresponding to portions of the feet. For example, a left slider includes positions LI, L2, L3, L4, and L5. A right slider includes positions Rl, R2, R3, R4, and R5. A button 1404 may be slid up or down on the sliders to automatically adjust the pedal position on the radially -adjustable coupling via the pedal arm assembly. The pedal positions may be automatically populated according to the treatment plan but the user has the option to modify them based on comfort level. The changed positions may be stored locally on the computing device 102, sent to the computing device 5114 executing the clinical portal 5126, and/or sent to the cloud-based computing system 5116.

[1123] FIG. 61 generally illustrates an example user interface 51500 of the user portal 5118, the user interface 51500 presenting a scale 51502 for measuring discomfort of the user at a beginning of a pedaling session according to principles of the present disclosure. The scale 51502 may provide options ranging for no discomfort (e.g., smiley face), mild discomfort, to high discomfort. This discomfort information may be stored locally on the computing device 5102, sent to the computing device 5114 executing the clinical portal 5126, and/or sent to the cloud-based computing system 5116.

[1124] FIG. 62 generally illustrates an example user interface 51600 of the user portal 5118, the user interface 5118 presenting that the electromechanical device 5104 is operating in a passive mode 51602 according to principles of the present disclosure. The user interface 51600 presents which pedaling session 51604 (session 1) is being performed and how many other pedaling sessions are scheduled for the day. The user interface 51600 also presents an amount of time left in the pedaling session 51604 and an amount of time left in the current mode (passive mode). The full lineup of modes in the pedaling session 51604 are displayed inbox 51606. While in the passive mode, the computing device 5102 controls the electric motor 5122 to independently drive the radially -adjustable couplings so the user does not have to exert any force on the pedals but their affected body part and/or muscles are stretched and warmed up. At any time, if the user so desires, the user may select a stop button 51608, which causes the electric motor 5122 to lock and stop the rotation of the radially -adjustable couplings 124 instantaneously or over a set period of time. A descriptive box 51610 may provide instructions related to the current mode to the user.

[1125] FIGs. 63A-63D generally illustrate an example user interface 51700 of the user portal 5118, the user interface 51700 presenting that the electromechanical device 5104 is operating in active-assisted mode 51702 and the user is applying various amounts of force to the pedals 5110 according to principles of the present disclosure. Graphical representations 51702 of feet are presented on the user interface 51700 and the graphical representations may fill up based on the amount of force measured at the pedals 5110. The force sensors (e.g., strain gauge) in the pedal 5110 may measure the forces exerted by the user and the microcontroller of the pedal 5110 may transmit the force measurements to the computing device 5102. Notifications may be presented when the amount of force is outside of a threshold target force (e.g., either below a range of threshold target force or above the range of threshold target force). For example, in FIG. 63 A, the right foot includes a notification to apply more force with the right foot because the current force measured at the pedal 5110 is below the threshold target force.

[1126] A virtual tachometer 51706 is also presented that measures the revolutions per minute of the radially -adjustable couplings 5124 and displays the current speed that the user is pedaling. The tachometer 51706 includes areas 51708 (between 0 and 10 revolutions per minute and between 20 and 30 revolutions per minute) that the user should avoid according to their treatment plan. In the depicted example, the treatment plan specifies the user should keep the speed between 10 and 20 revolutions per minute. The electromechanical device 5104 transmits the speed to the computing device 5102 and the needle 51710 moves in real-time as the user operates the pedals 5110. Notifications are presented near the tachometer 51706 that may indicate that the user should keep the speed above a certain threshold revolutions per minute (e.g., 10 RPM). If the computing device 5102 receives a speed from the device 5104 and the speed is below the threshold revolutions per minute, the computing device 5102 may control the electric motor 5122 to drive the radially-adjustable couplings 5124 to maintain the threshold revolutions per minute.

[1127] FIG. 63B depicts the example user interface 51700 presenting a graphic 51720 for the tachometer 51706 when the speed is below the threshold revolutions per minute. As depicted, a notification is presented that says “Too slow - speed up”. Also, the user interface 51700 presents an example graphical representation 51721 of the right foot when the pressure exerted at the pedal is below the range of threshold target force. A notification may be presented that reads “Push more with your right foot.” FIG. 63 C depicts the example user interface 51700 presenting a graphic 51722 for the tachometer 51706 when the speed is within the desired target revolutions per minute. Also, the user interface 51700 presents an example graphical representation 51724 of the right foot when the pressure exerted at the pedal is within the range of threshold target force. FIG. 63D depicts the example user interface 51700 presenting a graphic 51726 for the tachometer 51706 when the speed is above the desired target revolutions per minute. As depicted, a notification is presented that reads “Too fast - slow down”. Also, the user interface 51700 presents an example graphical representation 51728 of the right foot when the pressure exerted at the pedal is above the range of threshold target force. A notification may be presented that reads “Push less with your right foot.”

[1128] FIG. 64 generally illustrates an example user interface 51800 of the user portal 5118, the user interface 51800 presenting a request 51802 to modify pedal position while the electromechanical device 5104 is operating in active-assisted mode according to principles of the present disclosure. The request 51802 may pop up on a regular interval as specified in the treatment plan. If the user selects the “Adjust Pedals” button, the user portal 5118 may present a screen that allows the user to modify the position of the pedals 5110.

[1129] FIG. 65 generally illustrates an example user interface 51900 of the user portal 5118, the user interface 51900 presenting a scale 51902 for measuring discomfort of the user at an end of a pedaling session according to principles of the present disclosure. The scale 51902 may provide options ranging for no discomfort (e.g., smiley face), mild discomfort, to high discomfort. This discomfort information may be stored locally on the computing device 5102, sent to the computing device 5114 executing the clinical portal 5126, and/or sent to the cloud-based computing system 5116.

[1130] FIG. 66 generally illustrates an example user interface 52000 of the user portal 5118, the user interface 52000 enabling the user to capture an image of the body part under prehabilitation according to principles of the present disclosure. For example, an image capture zone 52002 is presented on the user interface 52000 and the dotted lines 52004 will populate to show a rough outline of the leg, for example, with a circle to indicate where their kneecap (patella) should be in the image. This enables the patient to line up their leg/knee for the image. The user may select a camera icon 52006 to capture the image. If the user is satisfied with the image, the user can select a save button 52008 to store the image on the computing device 5102 and/or in the cloud-based computing system 5116. Also, the image may be transmitted to the computing device 5114 executing the clinical portal 5126.

[1131] FIGs. 67A-D generally illustrate an example user interface 52100 of the user portal 5118, the user interface 52100 presenting angles 52102 of extension and bend of a lower leg relative to an upper leg according to principles of the present disclosure. As depicted in FIG. 67A, the user interface 52100 presents a graphical animation 52104 of the user’s leg extending in real-time. The knee angle in the graphical animation 52104 may match the angle 52102 presented on the user interface 52100. The computing device 5102 may receive the angles 5210 of extension from the goniometer 5106 that is worn by the user during an extension session and/or a pedaling session. To that end, although the graphical animation 52104 depicts the user extending their leg during an extension session, it should be understood that the user portal 5118 may be configured to display the angles 52102 in real-time as the user operates the pedals 5110 of the electromechanical device 5104 in real-time. [1132] FIG. 67B illustrates the user interface 52100 with the graphical animation 52104 as the lower leg is extended farther away from the upper leg, and the angle 52102 changed from 84 degrees to 60 degrees of extension. FIG. 67C illustrates the user interface 52100 with the graphical animation 52104 as the lower leg is extended even farther away from the upper leg. The computing device 5102 may record the lowest angle that the user is able to extend their leg as measured by the goniometer 5106. That angle 52102 may be sent to the computing device 5114 and that lowest angle may be presented on the clinical portal 5126 as an extension statistic for that extension session. Further, a bar 52110 is presented and the bar may fill from left to right over a set amount of time. A notification may indicate that the patient should push down on their knee over the set amount of time. The user interface 52100 in FIG. 67D is similar to FIG. 67C but it presents the angle 52102 of bend, measured by the goniometer 5106, as the user retracts their lower leg closer to their upper leg. As depicted, the graphical animation 52104 depicts the angle of the knee matching the angle 52102 presented on the user interface 52100 in real-time. The computing device 5102 may record the highest angle that the user is able to bend their leg as measured by the goniometer 5106. That angle 52102 may be sent to the computing device 5114 and that highest angle may be presented on the clinical portal 5126 as a bend statistic for that bend session. [1133] FIG. 68 generally illustrates an example user interface 52200 of the user portal 5118, the user interface 52200 presenting a progress report 52202 for a user extending the lower leg away from the upper leg according to principles of the present disclosure. The user interface 52200 presents a graph 52204 with the degrees of extension on a y-axis and the days after surgery on the x-axis. The angles depicted in the graph 52204 are the lowest angles achieved each day. The user interface 52202 also depicts the lowest angle the user has achieved for extension and indicates an amount of improvement (83%) in extension since beginning the treatment plan. The user interface 52200 also indicates how many degrees are left before reaching a target extension angle.

[1134] FIG. 69 generally illustrates an example user interface 52300 of the user portal 5118, the user interface 52300 presenting a progress screen 52302 for a user bending the lower leg toward the upper leg according to principles of the present disclosure. The user interface 52300 presents a graph 52304 with the degrees of bend on a y-axis and the days after surgery on the x-axis. The angles depicted in the graph 52304 are the highest angles of bend achieved each day. The user interface 52202 also depicts the lowest angle the user has achieved for bending and indicates an amount of improvement (95%) in extension since beginning the treatment plan. The user interface 52200 also indicates how many degrees are left before reaching a target bend angle.

[1135] FIG. 70 generally illustrates an example user interface 52400 of the user portal 5118, the user interface 52400 presenting a progress screen 52402 for a discomfort level of the user according to principles of the present disclosure. The user interface 52400 presents a graph 52404 with the discomfort level on a y-axis and the days after surgery on the x-axis. The user interface 52400 also depicts the lowest discomfort level the user has reported and a notification indicating the amount of discomfort level the user has improved throughout the treatment plan.

[1136] FIG. 71 generally illustrates an example user interface 52500 of the user portal 5118, the user interface 5118 presenting a progress screen 52502 for a strength of a body part according to principles of the present disclosure. The user interface 52500 presents a graph 52504 with the pounds of force exerted by the patient for both the left leg and the right leg on a y-axis and the days after surgery on the x-axis. The graph 52504 may show an average for left and right leg for a current session. For the number of sessions a user does each day, the average pounds of force for those sessions may be displayed for prior days as well. The user interface 52500 also depicts graphical representations 52506 of the left and right feet and a maximum pound of force the user has exerted for the left and right leg. The maximum pounds of force depicted may be derived from when the electromechanical device 5104 is operating in the active mode. The user may select to see statistics for prior days and the average level of active sessions for that day may be presented as well. The user interface 52500 indicates the amount of improvement in strength in the legs and the amount of strength improvement needed to satisfy a target strength goal.

[1137] FIG. 72 generally illustrates an example user interface 52600 of the user portal 5118, the user interface 5118 presenting a progress screen 52602 for an amount of steps of the user according to principles of the present disclosure. The user interface 52600 presents a graph 52604 with the number of steps taken by the user on a y-axis and the days after surgery on the x-axis. The user interface 52500 also depicts the highest number of steps the user has taken for amongst all of the days in the treatment plan, the amount the user has improved in steps per day since starting the treatment plan, and the amount of additional steps needed to meet a target step goal. The user may select to view prior days to see their total number of steps they have taken per day.

[1138] FIG. 73 generally illustrates an example user interface 52700 of the user portal 5118, the user interface 52700 presenting that the electromechanical device 5104 is operating in a manual mode 52702 according to principles of the present disclosure. During the manual mode 52702, the user may set the speed, resistance, time to exercise, position of pedals 5110, etc. That is, essentially the control system for the electromechanical device 5104 may provide no assistance to operation of the electromechanical device 104. When the user selects any of the modes in the box 52704, a pedaling session may begin. Further, when the user selects button 52706, the user portal 5118 may return to the user interface 51300..

[1139] FIG. 74 generally illustrates an example user interface 52800 of the user portal 5118, the user interface 52800 presenting an option 52802 to modify a speed of the electromechanical device 5104 operating in the passive mode 52802 according to principles of the present disclosure. The user may slide button 52806 to adjust the speed as desired during the passive mode where the electric motor 5122 is providing the driving force of the radially -adjustable couplings 5124.

[1140] FIG. 75 generally illustrates an example user interface 52900 of the user portal 5118, the user interface 52900 presenting an option 52902 to modify a minimum speed of the electromechanical device 5104 operating in the active-assisted mode 52904 according to principles of the present disclosure. The user may slide button 52906 to adjust the minimum speed that the user should maintain before the electric motor 5122 begins providing driving force.

[1141] FIG. 76 generally illustrates an example user interface 53000 of the clinical portal 5126, the user interface 53000 presenting various options available to the clinician/physician according to principles of the present disclosure. The clinical portal 5126 may retrieve a list of patients for a particular physician who logs into the clinical portal 5126. The list of patients may be stored on the computing device 5114 or retrieved from the cloud-based computing system 5116. A first option 53002 may enable the clinician to generate treatment plans for one or more of the patients, as described above. A second option 53004 may enable the clinician to view the number of sessions that each of the patients have completed in 24 hours. This may enable the clinician to determine whether the patients are keeping up with the treatment plan and send notifications to those patients that are not completing the sessions. A third option 53006 may enable the clinician to view the patients who have poor extension (e.g., angle of extension above a target extension for a particular stage in the treatment plan). A fourth 5option 3008 may enable the clinician to view the patients who have poor flexion (e.g., angle of bend below a target bend for a particular stage in the treatment plan). A fifth option 53010 may enable the clinician to view the patients reporting high pain levels. Regarding any of the options, the clinician can contact the user and inquire as to the status of their lack of participation, extension, flexion, pain level etc. The clinical portal 5126 provides the benefit of direct monitoring of the patients progress by the clinician, which may enable faster and more effective recoveries.

[1142] Further, the clinical portal 5126 may include an option to control aspects of operating the electromechanical device 5104. For example, the clinician may use the clinical portal 5126 to adjust a position of a pedal based on angles of extension/bend received from the computing device 5102 and/or the goniometer 5106 in real-time while the user is engaged in a pedaling session or when the user is not engaged in the pedaling session. The clinical portal 5126 may enable the clinician to adjust the amount of resistance provided by the electric motor 5122 in response to determining an amount of force exerted by the user exceeds a target force threshold. The clinical portal 5126 may enable the clinician to adjust the speed of the electric motor 5122, and so forth.

[1143] FIG. 77 generally illustrates example computer system 53100 which can perform any one or more of the methods described herein, in accordance with one or more aspects of the present disclosure. In one example, computer system 53100 may correspond to the computing device 5102 (e.g., user computing device), the computing device 5114 (e.g., clinician computing device), one or more servers 5128 of the cloud-based computing system 5116, the training engine 5130, the servers 5128, the motor controller 5120, the pedals 5110, the goniometer 5106, and/or the wristband 5108 of FIG. 36. The computer system 53100 may be capable of executing user portal 1518 and/or clinical portal 5126 of FIG. 36. The computer system may be connected (e.g., networked) to other computer systems in a LAN, an intranet, an extranet, or the Internet. The computer system may operate in the capacity of a server in a client-server network environment. The computer system may be a personal computer (PC), a tablet computer, the motor controller 5120, the goniometer 5106, a wearable (e.g., wristband 5108), a set-top box (STB), a personal Digital Assistant (PDA), a mobile phone, a camera, a video camera, or any device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that device. Further, while only a single computer system is illustrated, the term “computer” shall also be taken to include any collection of computers that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methods discussed herein.

[1144] The computer system 53100 includes a processing device 53102, a main memory 53104 (e.g., read only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM)), a static memory 53i06 (e.g., flash memory, static random access memory (SRAM)), and a data storage device 53108, which communicate with each other via a bus 53110.

[1145] Processing device 53102 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device 53102 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing device 53102 may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processing device 53102 is configured to execute instructions for performing any of the operations and steps discussed herein.

[1146] The computer system 53100 may further include a network interface device 53112. The computer system 53100 also may include a video display 53114 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), one or more input devices 53116 (e.g., a keyboard and/or a mouse), and one or more speakers 3118 (e.g., a speaker). In one illustrative example, the video display 53114 and the input device(s) 53116 may be combined into a single component or device (e.g., an LCD touch screen).

[1147] The data storage device 53116 may include a computer-readable medium 53120 on which the instructions 53122 (e.g., implementing control system, user portal 5118, clinical portal 5126, and/or any functions performed by any device and/or component depicted in the FIGs. and described herein) embodying any one or more of the methodologies or functions described herein is stored. The instructions 53122 may also reside, completely or at least partially, within the main memory 53104 and/or within the processing device 53102 during execution thereof by the computer system 53100. As such, the main memory 53104 and the processing device 53102 also constitute computer-readable media. The instructions 53122 may further be transmitted or received over a network via the network interface device 53112.

[1148] While the computer-readable storage medium 53120 is shown in the illustrative examples to be a single medium, the term “computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer-readable storage medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure. The term “computer-readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media.

[1149] None of the description in this application should be read as implying that any particular element, step, or function is an essential element that must be included in the claim scope. The scope of patented subject matter is defined only by the claims. Moreover, none of the claims is intended to invoke 35 U.S.C. § 112(f) unless the exact words “means for” are followed by a participle.

[1150] FIGs. 78A-78G generally illustrate an example prehabilitation system 53200 that utilizes machine learning to generate, monitor, and/or optimize a treatment plan, such as a prehabilitation plan of a patient. As will be described, the prehabilitation plan may be generated using a machine learning model trained on historical data, such as treatment data relating to a set of treatment plans of patients who have undergone (or that are undergoing) prehabilitation, rehabilitation, and/or any other type of health-related program that may be analyzed using machine learning to derive useful insights for generating an optimized prehabilitation plan for the patient. Nothing in this specification shall be construed to prevent the prehabilitation system 3200 being configured for purposes approved by veterinary healthcare professionals with respect to veterinary applications, and any veterinary application possible in hereby contemplated and any pronouns referring herein to humans may also be construed to apply to non-human animals.

[1151] A treatment plan, as used herein, may refer to a plan for a patient who is receiving treatment relating to a past, present, or future illness, condition, or ailment; an exercise plan, strength training plan, or endurance- increasing plan for an individual trying to improve his or her fitness; and/or any other plan capable of affecting the health of the patient. A treatment plan may, for example, include a prehabilitation plan for an individual who is to undergo surgery or who may have to undergo surgery at a later time period, a rehabilitation plan for a patient who has undergone surgery or who has a particular illness, condition, or ailment, and/or the like. [1152] In some embodiments, the physician may prescribe a treatment plan that includes operating one or more electrical, mechanical, optic, electro-optical and/or electromechanical devices 5104 (e.g., pedaling devices for arms or legs) for a period of time to exercise the affected area in an attempt to improve one or more characteristics of the affected body part and to attempt to regain as much normal operability of that affected body part as possible. For example, a treatment plan may include a set of pedaling sessions comprising use of the electromechanical device 5104, a set of joint extension sessions, a set of flex sessions, a set of walking sessions, a set of heartrates achieved per pedaling session and/or walking session, and the like. Additionally, or alternatively, the treatment plan may include a medical procedure to perform on the patient, a treatment protocol for the patient using the electromechanical device 5104, a diet regimen for the patient, a medical regimen for the patient, a sleep regimen for the patient, and/or the like.

[1153] Additionally, while one or more embodiments in FIGs. 78A-78G refer to a prehabilitation plan, it is to be understood that this is provided by way of example, and that in practice, the health management server 53202 may generate and recommend any type of treatment plan or health-related plan for any type of patient or user. Additional information regarding treatment plans is provided elsewhere herein. Furthermore, while one or more embodiments in FIGs. 78A-78G refer to preparation for surgery as a reason for a patient undergoing prehabilitation, it is to be understood that this is provided by way of example. In practice, any number of different health-related events may affect the health of a patient, such that there is a reason for the patient to undergo prehabilitation, rehabilitation, and/or any other health-related process. For example, one or more embodiments described herein may be applied to a patient who has an iatrogenic medical condition or side effects (e.g., de novo or exacerbated), whether pharmacologically, diagnostically, intentionally or omissively caused. The intensity of that iatrogenic medical condition may be of greater or lesser severity, and that degree of severity have an effect, likely concomitant, correlative or related in some manner, on a patient undergoing prehabilitation and/or rehabilitation.

[1154] The prehabilitation system 53200 may include a health management server 53202, the computing device 5102, the electromechanical device 5104, the goniometer 5106, the wristband 5108, and the computing device 5114. The health management server 53202 may be part of the cloud-based computing system 116, and may include the one or more servers 5128, the training engine 5130, and one or more machine learning models (e.g., the one or more machine learning models 5132).

[1155] FIG. 78A generally illustrates the health management server 53202 receiving training data for training a machine learning model to generate prehabilitation plans for users (e.g., patients undergoing prehabilitation). For example, the health management server 53202 may receive training data from one or more data storage devices. The training data may be received via an application programming interface (API) and/or another type of communication interface. The training data may include user data for a group of users who received treatment relating to past, present, or future illnesses, conditions, or ailments, treatment data relating to a set of treatment plans and/or outcomes, device data and/or sensor data for devices (e.g., electromechanical devices 5104) used for exercises performed as part of the treatment plans, and/or the like. The treatment data may include rehabilitation plan data relating to rehabilitation plans of a first group of users who have undergone rehabilitation for a condition, injury, or ailment; prehabilitation plan data relating to prehabilitation plans of a second group of users who are to undergo surgery or who may have to undergo surgery at a later time period; and/or the like.

[1156] As shown by reference number 53204, the health management server 53202 may receive user data relating to users involved in treatment plans. For example, the health management server 53202 may receive user data for the first group of users using or who have used various electromechanical devices 5104 while undergoing rehabilitation for various conditions, injuries, and/or ailments. Additionally, or alternatively, the health management server 53202 may receive user data for the second group of users using or who have used various electromechanical devices 5104 while undergoing prehabilitation. A user may undergo prehabilitation for any purpose approved of and/or prescribed by a healthcare professional, including, but not limited to, the following purposes: because of an upcoming health-related event (e.g., a surgery, etc.), to reduce the likelihood of experiencing a health-related event (e.g., an injury, such as a recurring injury) at a later time period, to improve one or more health indicators of the user, and/or for any other purpose contemplated by a healthcare professional and/or user. A treatment plan may have been completed by a user or the user may be in the process of completing the treatment plan.

[1157] User data for a user may include demographic data relating to one or more demographics of the user, health history data relating to one or more health indicators of the user, and/or the like. The demographic data may specify an age of the user, a race of the user, a sex of the user, an income of the user, and/or the like. The health history data may include data relating to a medical history of the user, data relating to a medical history of one or more family members of the user, data relating to a medical history of one or more individuals with whom the user has been in physical or otherwise proximate contact, data relating to a medical history of one or more physical locations (e.g., hospitals, outpatient clients, doctors’ offices, etc.) where the user has physically been, and/or the like. For example, the health history data may include data that specifies one or more medical conditions of the user, allergies, vital signs recorded over one or more visits with a healthcare professional, notes taken by the healthcare professional, and/or any other information relating to the user’s medical history. The health history data may include information collected before, during, and/or after undergoing prehabilitation and/or rehabilitation. The notes data may include data relating to a prognosis made by a physician, data relating to a patient description of the condition, injury, or ailment (e.g., symptoms, duration of symptoms, etc.), data relating to a pre-existing condition, injury, or ailment, and/or the like.

[1158] As shown by reference number 53206, the health management server 53202 may receive treatment data relating to treatment plans of the users. For example, the health management server 3202 may receive a first dataset of treatment data relating to rehabilitation plans of a first group of users undergoing or who have undergone rehabilitation for a condition, injury, or ailment. Additionally, or alternatively, the health management server 3202 may receive a second dataset of treatment data relating to prehabilitation plans of a second group of users who are to undergo surgery or who may have to undergo surgery at a later time period. [1159] The treatment data may include treatment plan data relating to the treatment plans of the users and/or treatment outcome data relating to outcomes of the treatment plans. For example, the treatment data may include treatment plan data relating to a prehabilitation plan of a user who has completed (or is presently undergoing) prehabilitation, treatment outcome data relating to outcomes of the treatment plans, and/or the like. An outcome of a treatment plan may relate to a result of treatment, a health indicator or health status of a user after a treatment plan has been completed, feedback relating to the treatment plan (e.g., provided by the user, a healthcare professional, etc.), and/or the like.

[1160] A treatment plan (e.g., a prehabilitation plan, a rehabilitation plan, etc.) may include one or more exercise sessions that may be performed, using an electromechanical device 104, by the user. An exercise session may include one or more exercises the user can complete to strengthen, make more pliable, reduce inflammation and/or swelling in, and/or increase endurance in an area of the body, tasks the user can complete, a start date and end date for a treatment plan, goals relating to a treatment plan (e.g., dietary goals, sleep goals, exercise goals, etc.), description and/or identifier of a medical procedure that was performed (or that is to be performed) on the user, and/or the like. The one or more exercises may, for example, include a set of pedaling sessions to be performed using an electromechanical device 104, a set of joint extension sessions, a set of flex sessions, a set of walking sessions, a set of heartrates per pedaling session and/or walking session, and/or the like. For example, a patient undergoing prehabilitation may perform a pedaling session as part of a prehabilitation plan created to assist a patient in strengthening or improving a condition of one or more body parts which may be affected by an upcoming surgery, to strengthen or improve a condition of one or more body parts to reduce the chance of an injury at a later time period, and/or the like.

[1161] As shown by reference number 53208, the health management server 53202 may receive device data and/or sensor data relating to devices involved in exercise sessions performed as part of the treatment plans. For example, the health management server 53202 may receive device data from an electromechanical device 5104. The device data may include data relating to a selected exercise session, data relating to a device configuration that corresponds to the selected exercise session, data relating to one or more user-selected preferences, and/or the like.

[1162] Additionally, or alternatively, the health management server 53202 may receive sensor data from one or more monitoring devices (e.g., the electromechanical device 5104, the wristband 5108, the goniometer 5108, a pad, and/or the like). The one or more monitoring devices may be implemented while a patient is exercising to track patient progress, monitor one or more patient health indicators, and/or the like. The sensor data may include vital signs data, goniometer data, component data for one or more components of an electromechanical device 5104, and/or the like. For example, a sensor of the electromechanical device 5104 may measure a force exerted by a patient on the pedals 5110 during an exercise session. Additionally, or alternatively, a sensor of the electromechanical device 5104 may measure a distance traveled by the user during an exercise session (e.g., based on the number of pedal revolutions completed over an interval).

[1163] Additionally, or alternatively, a wristband 5108 may capture a number of steps taken by a patient over an interval, may measure vital signs of the patient (e.g., heartrate, blood pressure, oxygen level, etc.), and/or the like. Additionally, or alternatively, a goniometer 5106 may measure a range of motion (e.g., angles of extension and/or bend) of a body part to which the goniometer 5106 is attached. Sensor data captured by the one or more monitoring devices may be provided to the health management server 53202.

[1164] In some embodiments, the health management server 53202 may receive one or more other types of training data. For example, the health management server 53202 may receive classification data relating to medical classifications of conditions, injuries, or ailments. The classification data may, for example, include a set of International Classification of Diseases and Related Health Problems (ICD) codes, suchas ICD-10 codes, or Diagnosis-Related Group (DRGs) codes. Additionally, or alternatively, the health management server 53202 may receive feedback data relating to patient feedback of treatment plans, healthcare professional feedback relating to treatment plans, and/or the like.

[1165] Additionally, or alternatively, the health management server 53202 may receive safety data relating to a set of constraints approved by one or more healthcare professionals. For example, one or more of the treatment plans may have been configured to comply with a set of constraints, such as a first constraint relating to one or more maximum permissible ranges of motion on the electromechanical device 5104, a second constraint relating to one or more maximum permissible resistances that can be applied to one or more components of the electromechanical device 5104, a third constraint relating to one or more minimum measures of force permissible to apply to the one or more components of the electromechanical device 5104, and/or the like.

[1166] In some embodiments, the training data may have been stored using one or more cloud storage devices. In some embodiments, the training data may be provided to the health management server 53202 in real-time or near real-time (e.g., provided periodically over a data collection time period) . In some embodiments, the health management server 53202 may receive the training data from one or more cloud storage devices (e.g., rather than needing to be provided the training data in real-time throughout a data collection time period). [1167] In some embodiments, the health management server 53202 may perform one or more pre processing operations to standardize the training data. For example, to use the training data to train a machine learning model, the health management server 53202 may have to perform one or more pre-processing operations to standardize the training data to a uniform format (n.b. a “uniform format” may be referred to as a “canonical format” or a “canonical form,” and the terms as meant as equivalents). As an example, the health management server 53202 may receive training data in multiple formats, multiple file types, and/or the like, and the health management server 53202 may convert one or more types of training data to a uniform format. [1168] The health management server 53202 thus receives the training data that is to be used to train the machine learning model 5132 to generate prehabilitation plans for users.

[1169] FIG. 78B generally illustrates the health management server 53202 training a machine learning model to generate prehabilitation plans for users. While one or more embodiments describe the machine learning model as being trained by the health management server 53202, it is to be understood that this is provided by way of example. In practice, another server or device may train the machine learning model (e.g., a desktop computer of a software developer, etc.) and may provide the trained machine learning model to the health management server 53202 or to another host device that allows the trained machine learning model to be accessed by the health management server 53202 (e.g., using an API or another type of communication interface).

[1170] As shown by reference number 53210, the health management server 53202 may train the machine learning model to generate prehabilitation plans for users. For example, the health management server 53202 may train the machine learning model to generate prehabilitation plans optimized for each user, that reduce the likelihood of the user experiencing a health-related event, that reduce the effect of the health-related event, that improve one or more health indicators of the user (e.g., ROM, strength, endurance, etc.), and/or the like. [1171] In some embodiments, a prehabilitation plan for the user may be an optimal prehabilitation plan based on a likelihood that one or more exercise sessions of the prehabilitation plan, if performed by the user, will improve a health indicator (e.g., ROM, strength, endurance, etc.) of the user. Additionally, or alternatively, a prehabilitation plan for a user may be an optimal prehabilitation plan based on a likelihood that one or more exercise sessions, if performed by the user, will reduce a likelihood of the user experiencing an adverse health- related event (e.g., an injury, ailment, etc.) and/or will reduce an effect of the adverse health-related event were the adverse health-related event to occur. It is to be understood that these are provided by way of example. In practice, any number of different values may be implemented to define an optimal prehabilitation plan.

[1172] A machine learning model, as used herein, may refer to a framework able to apply one or more machine learning techniques to analyze input values and to generate output values that are to be used to generate prehabilitation plans and/or modifications to prehabilitation plans that are optimal for users. In some embodiments, a machine learning model may be supported by a data structure such as a data model. For example, a data model may be a structural framework that is organized according to one or more schemata. A machine learning model may use the data model by applying one or more machine learning techniques to the data model to generate output values or to identify specific data points. The one or more machine learning techniques may include one or more supervised machine learning techniques, one or more unsupervised machine learning techniques, one or more reinforcement-driven machine learning techniques, and/or the like. For example, the one or more machine learning techniques may include a classification technique, a regression technique, a clustering technique, and/or any other technique that may be used to train the machine learning model.

[1173] In some embodiments, the machine learning model may include a graphical machine learning model, such as a Markov decision process (MDP), a Hidden Markov Model (HMM), a Gaussian Mixture Model (GMM), a model based on a neural network, and/or the like. While one or more embodiments described below refer to the machine learning model as including an MDP, it is to be understood that this is provided by way of example. In practice, the machine learning model may include a neural network, any other type of model driven by machine learning, or any combination of models.

[1174] In some embodiments, to train the machine learning model to include an MDP, the health management server 53202 may be configured with (or may generate) a data structure that includes a set of decision states (referred to hereafter as states) and a set of state transitions. An example illustration is provided in FIG. 78B. The set of states may represent steps or features of prehabilitation plans, and may include an initial state, sets of intermediary states, and a set of final states. The initial state may include state parameters relating to characteristics of a user before, during, and/or after a health-related event occurs. In some embodiments, the initial state parameters may define characteristics of the user before, during, and/or after a trial exercise session is completed. The state parameters for the initial state may include user data, such as user data relating to demographic information, patient health history (e.g., pre-existing conditions, information impacting overall health, such as whether the user is an athlete, etc.), user vital signs (e.g., a heartrate, a blood pressure level, an oxygen level, and/or the like), physical capabilities of the user (e.g., a range of motion (ROM) of the user, a force the user applied to one or more pedals 5110 while exercising on an electromechanical device 5104, etc.), and/or the like.

[1175] In some embodiments, one or more sets of intermediary states may be used to define steps or features of a prehabilitation plan. For example, an intermediary state may include state parameters relating to characteristics of steps or features of the prehabilitation plan. For example, the intermediary state parameters may include a state parameter relating to a duration of an exercise session, a state parameter relating to a mode in which the electromechanical device 5104 is to engage and/or a duration during which the electromechanical device 5104 is to be engaged in that mode, a state parameter identifying a target heartrate for a patient while performing the exercise session, and/or the like. Additionally, or alternatively, the intermediary state parameters may include state parameters relating to instructions for aerobic exercises performed off the electromechanical device 5104, such as instructions for a joint extension session, instructions for a flex session, and/or the like. [1176] In some embodiments, the final states may include state parameters relating to specific prehabilitation plans that can be selected for or presented to a user. For example, the final states may include state parameters, such as exercise session identifiers, where such state parameters relate to a variety of different exercise sessions (and/or a variety of variations to an exercise session). The exercise sessions may, for example, vary based on the degree of progress the patient has made in the prehabilitation process, the physical fitness of the patient, whether the patient has had any injuries or recurring injuries, a severity of the injuries or recurring injuries, and/or the like.

[1177] To provide a specific example in the field of orthopedics, and without limiting applications in any other medical discipline performed by a healthcare professional, a user may have previously tom an ACL and may want to use prehabilitation to reduce the likelihood of a second ACL tear. In this case, the final states of the machine learning model may include prehabilitation plans with exercise sessions that relate to corresponding rehabilitation exercise sessions for patients recovering from an existing ACL tear, prehabilitation plans with exercise sessions that improve one or more health indicators of the user (e.g., such as by strengthening one or more muscles or tendons around the knee), and/or the like. A prehabilitation exercise session may be said to relate to a corresponding rehabilitation exercise session based on the prehabilitation exercise session’s being identical to, modeled after, and/or sharing a threshold number of characteristics and/or features with the rehabilitation exercise session.

[1178] While one or more examples described herein refer to a patient who has recovered from an existing ACL tear and who is using prehabilitation to reduce the likelihood of reinjuring his or her ACL, it is to be understood that this is provided only by way of example. In practice, a patient may undergo prehabilitation for a variety of different reasons. For example, a patient may need surgery and may be using prehabilitation pre- operatively to reduce swelling, improve ROM, and/or the like.

[1179] In some embodiments, the one or more sets of intermediate states may be segmented into layers. For example, the layers may include a first layer with a subset of states that represent durations of prehabilitation plans and/or durations of different parts of the prehabilitation plans, a second layer with a subset of states that represent modes of the electromechanical device 5104 and/or configuration values for one or more configurations that can be implemented during an exercise session, a third layer with a subset of states that each represent a target number of pedals 5110 for the patient to make over an interval, a fourth layer with a subset of states that each represent a target heartrate of the patient over the interval, and/or the like. It is to be understood that this is provided by way of example, and that in practice, the set of states may be segmented into any number of finite layers or related using any number of different data types and/or logical schemes.

[1180] In some embodiments, the data structure (e.g., a data model) supporting the MDP may relate states to each other using a set or sets of state transitions. In some embodiments, a state transition may include a value that represents a probability that transitioning from a source state to a destination state will be an optimal transition (e.g., relative to one or more other transition values relating to the destination state). For example, a set of intermediary states may represent proposed durations of an exercise session. Each respective state may be initially configured with equal probability values. As will be described, certain input values may cause the probability values to change in order to recommend an optimal prehabilitation plan for a user. For example, if a user has a history of injuries, exercising for long time periods may increase a likelihood of injury or re-injury. In this example, the health management server 53202 may train the machine learning model such that lower probability values are assigned to states with longer exercise session durations (e.g., based on the longer exercise session durations being linked to increased risk of injury or re-injury).

[1181] One or more embodiments herein refer to probabilities or probability values. It is to be understood that this is provided by way of example, and that in practice, the state transitions of the MDP may be implemented using one or more non-parametric (i.e., ranked) means. Further, the probabilities or probability values may, in one or more embodiments herein, represent Bayesian probabilities.

[1182] In some embodiments, the health management server 53202 may train the machine learning model to generate prehabilitation plans for users. For example, the health management server 53202 may process the training data using one or more machine learning techniques, such that the machine learning model is configured to receive training data values and, based on the training data values, to assign state transition probabilities to state transitions. The health management server 53202 may select a combination of states associated with highest state transition probabilities, where selected states collectively represent a prehabilitation plan generated for a user. Additionally, the health management server 53202 may compare state data for the selected states with outcome data for known outcomes in order to indicate whether certain prehabilitation plans were successful, to indicate a degree to which said plans were successful, to indicate a degree to which said plans were optimal for a particular user or characteristic of a user, and/or the like. Based on comparing the state data with the outcome data, the health management server 53202 may update programming used to assign the state transition values. For example, the health management server 53202 may update programming by adjusting threshold values used to assign the state transition values. The state transition values may relate to or may be used to generate a set of machine learning scores, as described below.

[1183] A machine learning score may relate to, without limitation, a risk value (e.g., a risk score), a configuration value (e.g., a configuration score, such as a score for a configuration of the electromechanical device 5104), and/or any other score or value capable of being used to generate a machine learning score. The risk value and/or the configuration value may be represented as a probability value, a confidence interval, a non- probabilistic value, a numerical value, a summation, an expected value, and/or the like. For example, a machine learning score may relate to a risk score that represents a probability of a change to a health indicator of the user. To provide a specific example, if a device configuration is implemented on the electromechanical device 5104 while the user performs the exercise session, a risk score may represent a probability that the user will experience a health-related event, such as an injury, while performing the exercise session (or at a time period after the exercise session has been performed).

[1184] Additionally, or alternatively, and provided as another example, a configuration score may represent a probability that an implemented device configuration (e.g., implemented on an electromechanical device 5104) is an optimal device configuration for a user. Additionally, or alternatively, a configuration score may represent a probability that a modification to the implemented device configuration is an optimal modification. A device configuration may be optimal (or a modification may be optimal) based on a likelihood of the device configuration or modification improving and/or maximizing, given a particular context, a health indicator of a user. For example, depending on the context, a device configuration may be optimal if the device configuration improves or maximizes a recovery time and/or life expectancy of the user, improves or maximizes a ROM of the user, and/or that improves or maximizes any other value or metric capable of measuring a health indicator of the user. Context that can affect optimality may include demographic information, medical history, accessibility to medical care, user work ethic, and/or the like.

[1185] In some embodiments, the health management server 53202 may train the machine learning model such that the machine learning model is configured to generate real-time modifications to a prehabilitation plan. For example, the health management server 53202 may receive training data that includes sensor data related to progress users have made in prehabilitation plans. In this example, the health management server 53202 may use one or more machine learning techniques to process the sensor data and to generate, based on processing the sensor data, one or more configuration scores. The one or more configuration scores represent one or more probabilities that an implemented device configuration is an optimal device configuration for a user and/or that represent one or more probabilities that a modification to the implemented device configuration is an optimal modification.

[1186] In some embodiments, a first module of the machine learning model may be trained to generate a prehabilitation plan and a second module of the machine learning model may be trained to generate real-time or near real-time modifications to the prehabilitation plan. In some embodiments, a first machine learning model may be trained to generate the prehabilitation plan and a second machine learning model may be trained to generate the real-time or near real-time modifications to the prehabilitation plan. Generation or transmission of data may occur in real-time or near real-time. Real-time may refer to less than 2 seconds, or any other suitable amount of time. “Real-time” may also refer to near real-time, which may be less than 10 seconds or any reasonably proximate difference between two different times. Near real-time may refer to any interaction of a sufficiently short time to enable two individuals to engage in a dialogue via a user interface, and will generally be less than 10 seconds (or any suitably proximate difference between two different times) but greater than 2 seconds. For example, near real-time may include a range of 2-5 seconds, 2-10 seconds, or any other suitable amount of time.

[1187] In this way, the health management server 53202 trains the machine learning model to be able to generate prehabilitation plans for users and/or updates to prehabilitation plans for the users.

[1188] FIG. 78C generally illustrates the health management server 53202 using machine learning to determine a prehabilitation plan for a patient undergoing prehabilitation. As shown by reference number 53212, the health management server 53202 may receive user data relating to the patient. The patient (also referred to hereafter as a user) may be a user or an operator of the electromechanical device 5104. For example, the user may have (or may have previously had) an injury and may want to reduce the risk of a recurring injury by taking part in a prehabilitation program. By having the user exercise on the electromechanical device 5104, the prehabilitation program may reduce the risk of the recurring injury. The user data of the user may be provided to the health management server 53202 to allow the health management server 53202 to process the user data when generating the prehabilitation plan.

[1189] In some embodiments, the user may interact with the user portal 5118 to consent to have the user data of the user provided to the health management server 53202. For example, the user may have access to a patient portal used to sign up for the prehabilitation program. The user portal 5118 may request that the user consent to providing user data such that the user data may be processed and used to recommend an optimal prehabilitation plan. In some embodiments, a healthcare professional may interact with the clinical portal 5126 to provide the health management server 53202 with the user data. For example, the user may have provided consent and a healthcare professional may interact with an interface of the clinical portal 5126 to provide the health management server 53202 with the user data.

[1190] In some embodiments, the health management server 53202 may already store the user data (e.g., using a data structure) or may already have access to the user data via any suitable source. In this case, the health management server 53202 may reference the data structure or suitable source to identify or obtain the user data for further processing.

[1191] While one or more embodiments describe a prehabilitation plan for the user to reduce the likelihood of the user reinjuring a particular body part, it is to be understood that this is provided by way of example. In practice, the health management server 53202 may generate prehabilitation plans for any number of different health related reasons, such as to prevent an injury that may be at risk of occurring but which has in fact yet to occur, to improve a condition of the user, to improve an overall health status of the user, and/or the like. Furthermore, in some embodiments, the health management server 53202 may generate one or more other types of health improvement plans, such as rehabilitation plans, exercise plans, and/or the like.

[1192] As shown by reference number 53214, the health management server 53202 may receive (or obtain) treatment data for a subset of treatment plans relating to a health status or health indicator of the user. For example, a data structure may include a master set of treatment data that includes treatment data for treatment plans relating to a number of different conditions, injuries, and/or ailments. The data structure may associate treatment data relating to each treatment plan with one or more identifiers that relate to each respective condition, injury, or ailment that may be treated by a given treatment plan.

[1193] In some embodiments, to obtain a subset of treatment plans relating to a condition, injury, or ailment of the user, an authorized device may reference the data structure using an identifier relating to a condition, injury, or ailment of the user. The authorized device may be the health management server 53202, a computing device 5116-1 (e.g., a device used by the healthcare professional), a computing device 5116-2 (e.g., a device used by the user), and/or another device. For example, the health management server 53202 may receive the user data for the user, where the user data includes an injury identifier relating to the recurring injury of the user. In this case, the health management server 53202 may use the injury identifier to reference the data structure to identify a subset of treatment plans relating to the recurring injury of the user.

[1194] As shown by reference number 53216, the health management server 53202 may use the machine learning model to generate a prehabilitation plan for the user. For example, the health management server 53202 may provide the user data and the treatment data as inputs to the machine learning model to cause the machine learning model to generate a set of machine learning scores.

[1195] In some embodiments, the set of machine learning scores may relate to probabilities of a given device configuration (e.g., of the electromechanical device 5104) being suitable for a given application or applications for the user. For example, a device configuration may be suitable for a given user application based on the device configuration corresponding to a threshold probability of improving performance of an area of the user’s body that would be affected if the user’s injury were recur. To provide another example, a device configuration may be suitable for a given user application based on the selected device configuration corresponding to a threshold probability of preventing a health-related event from occurring with respect to the user. Additionally, or alternatively, the set of machine learning scores may include one or more risk scores relating to probabilities of a change in one or more health indicators of the user, one or more configuration scores relating to different prehabilitation plans being an optimal prehabilitation plan for the user, and/or the like.

[1196] As an example, the health management server 53202 may receive health history data for the user that includes data relating to the user having a history of recurring knee problems, data relating to the user having above average physical strength and conditioning, data relating to the user having a history of participating in sports, and/or the like. Furthermore, the health management server 53202 may receive treatment data relating to a subset of treatment plans used to treat recurring knee injuries. In this example, the health management server 53202 may provide the user data and the treatment data as inputs to the machine learning model to cause the machine learning model to generate a set of machine learning scores. The set of machine learning scores may correspond to the subset of available prehabilitation plans (e.g., which may include device configurations for exercise sessions performed on the electromechanical device 5104), where one of the machine learning scores represents a highest probability of a given prehabilitation plan being an optimal prehabilitation plan for the user. [1197] As shown by reference number 53218, the health management server 53202 may provide prehabilitation plan data to the computing device 5116-1. For example, the health management server 53202 may provide the computing device 5116-1 with prehabilitation plan data relating to one or more prehabilitation plans that are part of the subset of prehabilitation plans. The computing device 5116-1, as described above, may be a device accessible to a healthcare professional. The prehabilitation plan may be provided via a communication interface, such as an API or another type of communication interface. In some embodiments, the health management server 53202 may provide the computing device 5116-1 with prehabilitation plan data relating to the prehabilitation plan that has the highest probability of being the optimal prehabilitation plan for the user. In some embodiments, the health management server 53202 may provide the computing device 5116- 1 with prehabilitation plan data relating to one or more prehabilitation plans that correspond to one or more machine learning scores that satisfy a threshold machine learning score.

[1198] As shown by reference number 53220, the healthcare professional may interact with the computing device 5116-1 to review, modify, and/or approve the prehabilitation plan. In some embodiments, the healthcare professional may, during a telemedicine session or telehealth session, interact with the interface of the clinical portal 5126. In some embodiments, the healthcare professional may interact with an interface of the clinical portal 5126 to review and approve the prehabilitation. In this case, the interface may display the prehabilitation plan and the healthcare professional may review and submit the healthcare professional’s approval of the prehabilitation plan.

[1199] In some embodiments, the healthcare professional may interact with the interface of the clinical portal 5126 to modify and approve the prehabilitation plan. In this case, the interface may display the prehabilitation plan and the healthcare professional may interact with the interface by marking up the prehabilitation plan, by selecting one or more modifications from a drop-down menu, by inputting one or more modifications as free-form text, and/or the like. [1200] In some embodiments, the healthcare professional may interact with the interface of the clinical portal 126 to reject the prehabilitation. In this case, the healthcare professional may interact with the interface to input one or more suggested changes for generating a new prehabilitation plan. When the healthcare professional finalizes the one or more suggested changes, data relating to the suggestions may be provided back to the health management server 53202. The health management server 53202 may then use the one or more suggestions to retrain the machine learning model such that programming used to generate outputs may be updated based on such suggestions.

[1201] As shown by reference number 53222, the computing device 5116-1 may provide, to the computing device 5116-2, prehabilitation plan data for the approved prehabilitation plan. The computing device 5116-2 may, for example, be a device accessible to the user. In some embodiments, the computing device 5116-1 may provide the approved prehabilitation plan to the user portal 5118 accessible to the user. Additionally, or alternatively, the computing device 5116-1 may provide the approved prehabilitation plan to the computing device 5116-2 as an image in a short message service (SMS) message or a private messenger (e.g., Telegram, Signal, skype, Google Hangouts, Facebook Messenger, WickrPro, WickrMe, WhatsApp, snapchat, Instagram, etc.) message. Additionally, or alternatively, the approved prehabilitation plan may be provided to an e-mail account associated with the user and/or to one or more other accounts associated with the user.

[1202] In this way, the health management server 53202 generates the prehabilitation plan using machine learning and enables the prehabilitation plan to be provided to a reviewing healthcare professional and to the user. In other situations, the electromechanical device 5104 may generate the prehabilitation plan. For example, a lightweight machine learning model may be hosted or supported by the electromechanical device 5104 (e.g., rather than by the health management server 53202), such that the electromechanical device 5104 may generate the prehabilitation plan. The prehabilitation plan may be generated based on the electromechanical device 5104 receiving a request from a device of a healthcare professional, based on a user uploading user data and/or other related data about the user’s health history, and/or via another type of trigger.

[1203] FIG. 78D generally illustrates the electromechanical device 5104 implementing a device configuration corresponding to an exercise session of the approved prehabilitation plan. As shown by reference number 53224, the electromechanical device 5104 may provide, to the health management server 53202, a message relating to an exercise session that the user selected for an exercise session of the prehabilitation plan. For example, the user may interact with an interface of the electromechanical device 5104 that displays exercise sessions capable of being performed by the user. In this case, the user may select an exercise session specified in the prehabilitation plan, such that the message relating to the exercise session is provided to the health management server 53202.

[1204] As shown by reference number 53226, the health management server 53224 may select the device configuration corresponding to the exercise session of an exercise session of the prehabilitation plan. For example, the health management server 53202 may use an exercise session identifier to reference a data structure that associates the exercise session identifier with a corresponding device configuration.

[1205] The device configuration may include mode data related to one or more modes in which the electromechanical device 5104 is capable of operating during the exercise session. The mode data may include a first component configuration including data related to one or more positions at which to configure one or more components of the electromechanical device 5104, a second component configuration including data related to one or more forces to apply to the one or more components of the electromechanical device 5104, a user interface configuration including data related to exercise instructions for the exercise session, wherein the exercise instructions are capable of being provided for display via an interface, and/or the like. The first component configuration may define a position at which to configure a seat of the electromechanical device 5104, a position at which to configure one or more pedals 5110 of the electromechanical device 5104, and/or the like.

[1206] As shown by reference number 53228, the health management server 53226 may provide the device configuration corresponding to the exercise session of the prehabilitation plan to the electromechanical device 5104. For example, the device configuration may be provided to the electromechanical device 5104 to enable the electromechanical device 5104 to implement the device configuration. In some embodiments, the electromechanical device 5104 may receive the device configuration data from another device. For example, the computing device 5116-1 may, upon the healthcare professional's approval of the prehabilitation plan, provide the electromechanical device 5104 with device configuration data corresponding to the selected exercise session. [1207] In some embodiments, the electromechanical device 5104 may implement the device configuration. For example, the electromechanical device 5104 may implement the device configuration to adjust a position of a seat, to adjust a position of one or more pedals 5110, to adjust a position of one or more brake mechanisms, to power on one or more motors (e.g., an electric motor, a stepper motor, and/or the like), to power on one or more sensors, to display exercise instructions for the exercise session on an interface associated with the electromechanical device 104, and/or the like.

[1208] Additionally, or alternatively, the electromechanical device 5104 may implement the device configuration such that an assisting force may be applied to the one or more pedals 5110. For example, a motor or related component may be configured such that torque is applied to the one or more pedals 5110 to assist the user in rotating the pedals 5110. The assisting force may be applied based on a trigger condition being satisfied. For example, the assisting force may be applied while the user is performing the exercise session, while a position of a pedal is at a certain angle (e.g., such that the assisting force is applied for a portion of the total 360- degree rotation of the pedal), based on a user interacting with a user interface to request the assisting force, and/or the like.

[1209] Additionally, or alternatively, the electromechanical device 5104 may implement the device configuration such that a resistive force may be applied to the one or more pedals 5110. For example, one or more braking mechanisms may be configured such that a resistive force increases an amount of force needed by the user to rotate the one or more pedals 5110. The resistive force may be applied based on a trigger condition being satisfied.

[1210] Additionally, or alternatively, the electromechanical device 5104 may implement the device configuration such that the user, when performing one or more exercises of the exercise session, is enabled to repeat one or more motions associated with developing or improving muscle memory. For example, the electromechanical device 104 may implement a device configuration that enables the user to repeat motions that can be made to assist in preventing an ACL tear from ever occurring (e.g., such as by strengthening muscles and/ortendons around the knee). As another example, the electromechanical device 104 may implement a device configuration that enables the user to repeat motions that might be made after an ACL tear occurs (e.g., motions made as part of an exercise of a rehabilitation program). In this example, the user is enabled to develop or improve muscle memory of motions restricted so as to mirror or simulate, had the user tom an ACL, the user’s ROM, strength, flexibility, etc.

[1211] Additionally, or alternatively, the electromechanical device 5104 may implement the device configuration such that one or more sensors may be configured to monitor progress of the user while the user is performing the exercise session. For example, the one or more sensors may be configured to monitor and report vital signs of the user, angles of extension of bend of at least one body part of the user, force the user applies to the one or more pedals 5110, and/or the like.

[1212] In this way, the electromechanical device 5104 is enabled to implement the device configuration. [1213] FIG. 78E generally illustrates one or more sensors capturing and providing sensor data to the health management server 53202. The sensor data may be related to determining the user’s progress in the prehabilitation plan.

[1214] As shown by reference number 53230, the computing device 5102 may provide a first set of sensor data to the health management server 53202. For example, one or more sensors, such as one or more strain gauges, may be configured to measure a force that the user applies to one or more pedals 5110 of the electromechanical device 5104. This allows the computing device 5102 to provide the health management server 53202 with a first set of sensor data related to one or more measurements of force that the user applies to the one or more pedals 5110.

[1215] As shown by reference number 53232, the wristband 5108 may provide a second set of sensor data to the health management server 53202. For example, the wristband 5108 may include sensors such as an accelerometer, a gyroscope, an altimeter, a light sensor, a pulse oximeter, and/or the like. The sensors of the wristband 5108 may generate the second set of sensor data by monitoring the user throughout the exercise session and a processor of the wristband 5108 may provide the second set of sensor data to the health management server 53202.

[1216] As an example, the wristband 5108 may be configured to use the light sensor to detect a heart rate of the user. Additionally, or alternatively, and as provided in another example, the wristband 5108 may be configured to use the pulse oximeter to measure an amount of oxygen in the blood of the user (e.g., by sending infrared light into capillaries and measuring how much light is reflected off the gases). Sensor data (e.g., vital signs data) relating to the heart rate of the user and to the amount of oxygen in the user’s blood may be provided to the health management server 53202.

[1217] As shown by reference number 53234, the goniometer 5108 may provide a third set of sensor data to the health management server 53202. For example, the goniometer 5108 may include a radial magnet and one or more processors with a magnetic sensing encoder chip capable of sensing a position of the radial magnet. The position of the magnet may be measured periodically and used to determine one or more angles of extension or bend. A third set of sensor data relating to the one or more angles of extension or bend may be provided to the health management server 53202.

[1218] In this way, sensor data related to determining the user’s progress in the prehabilitation plan may be provided to the health management server 53202.

[1219] FIG. 78F generally illustrates the health management server 53202 performing one or more actions to optimize the exercise session of the user. Optimizing the exercise session may include modifying the device configuration to reduce a likelihood that the user is injured, to improve a rate at which the user strengthens an area of the body targeted for prehabilitation, and/or the like.

[1220] As shown by reference number 53236, the health management server 53202 may select a modification to the device configuration based on the sensor data. In some embodiments, the health management server 53202 may provide the sensor data as an input to the machine learning model such that the machine learning model is configured to output a set of machine learning scores. The set of machine learning scores may relate to (e.g., be stored in association with) a set of configuration values capable of being used to modify the device configuration. A machine learning score may represent a confidence that implementing a particular configuration value will optimize the exercise session for the user (relative to a current device configuration implementation, relative to implementing one or more other configuration values, etc.). For example, a scale of 1-100 may be implemented, where a value of one represents a low confidence that implementing a particular configuration value will optimize the exercise session for the user and a value of one hundred represents a high confidence that implementing the particular configuration value will optimize the exercise session for the user. [1221] In some embodiments, the health management server 53202 may select, as the modification, a configuration value relating to a highest available machine learning score. In some embodiments, the health management server 53202 may select one or more configuration values based on one or more corresponding machine learning scores satisfying a threshold machine learning score. For example, the health management server 53202 may compare the set of machine learning scores and the threshold machine learning score and may determine that one or more machine learning scores satisfy the threshold machine learning score. In this case, the health management server 53202 may select, as the modification, one or more configuration values corresponding to the one or more machine learning scores.

[1222] As shown by reference number 53238, the health management server 53202 may provide the modification to the device configuration to the motor controller 5120 of the electromechanical device 5104. As shown by reference number 53240, the electromechanical device 5104 may implement the modification to the device configuration. For example, the electromechanical device 5104 may implement the modification by reading the one or more configuration values and adjusting the device configuration based on the one or more configuration values.

[1223] In this way, the health management server 53202 enables the electromechanical device 5104 to implement the modification to the device configuration.

[1224] FIG. 78G generally illustrates the health management server 53202 generating and providing a message. As shown by reference number 53242, the health management server 53202 may generate a message (written, spoken, visually displayed, etc.) based on a machine-leaming-driven analysis of the sensor data. For example, the health management server 53202 may provide the sensor data as an input to the machine learning model, such that the machine learning model is configured to output one or more risk scores that represent a probability of change to a health indicator of the user. The health management server 53202 may determine whether the one or more risk scores satisfy a threshold risk score and may generate the message for the user based on the one or more risk scores satisfying the threshold risk score.

[1225] The text of the message may include a warning message that the user is exercising in a manner that may increase a likelihood of injury or delaying the prehabilitation process, a confirmation message indicating that the user is exercising within an optimal ROM or at an optimal speed, a recommendation to modify the device configuration, a recommendation for the user to change form or posture while performing the exercise session, a recommendation for the user to change an amount of force exerted on one or more pedals 5110 of the electromechanical device 5104, and/or the like.

[1226] As shown by reference number 53244-1, the health management server 53202 may provide the message to the electromechanical device 5104. For example, the health management server 53202 may provide the message for display via an interface associated with the electromechanical device 104. The interface may be an interface of the user portal 5118, an interface of an exercise application running on the electromechanical device 5104, and/or the like.

[1227] As shown by reference number 53244-2, the health management server 53202 may provide the message to one or more computing devices 5116. For example, the health management server 53202 may provide the message to the computing device 5116-1 that is accessible to the healthcare professional, to the computing device 5116-2 that is accessible to the user, and/or the like.

[1228] As shown by reference number 53246-1, the electromechanical device 5104 may display the message. For example, the message may be displayed such that the user is enabled to view the message while performing the exercise session. As shown by reference number 53246-2, the computing device 5116 may display the message. For example, the message may be displayed on the computing device 5116-1 associated with the healthcare professional and/or on the computing device 5116-2 associated with the user.

[1229] One or more embodiments described herein may be implemented during a telemedicine or telehealth session with a healthcare professional. For example, the prehabilitation plans (and/or other prehabilitation plans not selected) may be presented, during a telemedicine or telehealth session, to a healthcare professional. The healthcare professional may select a particular prehabilitation plan for the user to cause that prehabilitation plan to be transmitted to the user and/or to control, based on the prehabilitation plan, the electromechanical device 5104. In some embodiments, to facilitate telehealth or telemedicine applications, including remote diagnoses, determination of prehabilitation plans and rehabilitative and/or pharmacologic prescriptions, the health management server 53202 may receive and/or operate distally from the user and the electromechanical device 5104. In such cases, the recommended prehabilitation plans and/or other health improvement plans may be presented simultaneously with a video of the user in real-time or near real-time during a telemedicine or telehealth session on a user interface of a computing device of a healthcare professional (e.g., computing device 5116-1). The term “medical action(s)” may refer to any suitable action(s) performed by a healthcare professional, and such action or actions may include diagnoses, prescriptions for treatment plans, prescriptions for treatment devices, and the making, composing and/or executing of appointments, telemedicine sessions, prescriptions of medicines, telephone calls, emails, text messages, and the like.

[1230] By using machine learning to process received data, the health management server 53202 generates a prehabilitation plan that is optimal for the user. For example, the health management server 53202 may generate a prehabilitation plan that includes an exercise session, where the exercise session may be performed by the user when a device configuration is implemented on the electromechanical device 5104. The device configuration allows the exercise session to be performed using an optimal ROM, performed at an optimal strength, performed at an optimal endurance, and/or the like. Additionally, by using machine learning to generate an optimal prehabilitation plan that accounts for a number of factors that influence optimality (e.g., user demographics, medical history, surgical results, and/or the like), the health management server 53202 reduces a likelihood of injury or re-injuiy and improves a speed at which the user can recover. This reduces a utilization of resources (e.g., power resources, processing resources, network resources, and/or the like) of the electromechanical device 5104 and related devices relative to using an inferior plan more likely to injure or re injure the user and that will require more time to recover.

[1231] FIG. 79 generally illustrates a flowchart of an example method 53300 for using machine learning to generate a prehabilitation plan for a user and for enabling the electromechanical device 5104 to implement an electromechanical device configuration for an exercise session that is part of the prehabilitation plan. In some embodiments, the method 53300 is implemented on the health management server 53202 shown in FIGs. 78A- 78G. In some embodiments, the health management server 53202 may be part of the cloud-based computing system 5116. The method 53300 may include operations implemented in computer instructions stored in a memory and executed by a processor of the health management server 53202.

[1232] At block 53302, the method 53300 may include receiving user data for a user that is to operate the electromechanical device 5104. For example, the health management server 53202 may receive user data that identifies a health status of a user that is to operate an electromechanical device 5104.

[1233] At block 53304, the method 53300 may include receiving treatment data relating to a set of treatment plans and outcomes. For example, the health management server 53202 may receive treatment data relating to a set of treatment plans and outcomes that is capable of being offered to the user.

[1234] At block 53306, the method 53300 may include generating a prehabilitation plan by using a machine learning model to process the user data and the treatment data. For example, the health management server 53202 may generate a prehabilitation plan by using a machine learning model to process the user data and the treatment data, where the health improvement plan includes an exercise session to be performed on the electromechanical device 5104.

[1235] At block 53308, the method 5330 may include enabling the prehabilitation plan to be made distally accessible by one or more user portals. For example, the health management server 53202 may enable remote users to access the prehabilitation plan by providing the prehabilitation plan to one or more user portals, such as the user portal 5118, the clinical portal 5126, an administrative (admin) portal, a software developer portal, and/or the like.

[1236] At block 53310, the method 53300 may include selecting, for the electromechanical device 5104, a device configuration that enables the one or more exercises of the prehabilitation plan to be performed by the user. For example, the health management server 53202 may select, for the electromechanical device 5104, a device configuration that enables the one or more exercises of the prehabilitation plan to be performed by the user. This improves performance of an area of the user’s body that would be affected if the health-related event occurs with respect to the user.

[1237] At block 53312, the method 53300 may include enabling the electromechanical device 5104 to implement the electromechanical device configuration. For example, the health management server 53202 may enable the electromechanical device 5104 to implement the electromechanical device 5104 configuration by providing the electromechanical device configuration to the electromechanical device 5104.

[1238] FIG. 80 shows an example embodiment of a method 53400 for receiving a selection of an optimal treatment plan and controlling, based on the optimal treatment plan, a treatment apparatus (e.g., the electromechanical device 5104) while the patient uses the treatment apparatus according to the present disclosure. Method 53400 includes operations performed by processors of a computing device (e.g., any component of FIG. 47, such as server 5128 executing the training engine 5130). In some embodiments, one or more operations of the method 53400 are implemented in computer instructions stored on a memory device and executed by a processing device. The method 53400 may be performed in the same or a similar manner as described above in regard to method 53300. The operations of the method 53400 may be performed in some combination with any of the operations of any of the methods described herein.

[1239] Prior to the method 53400 being executed, various optimal treatment plans may be generated by one or more trained machine learning models 5132 of the training engine 5130. For example, based on a set of treatment plans pertaining to a medical condition of a patient, the one or more trained machine learning models 5132 may generate the optimal treatment plans. The various treatment plans may be transmitted to one or computing devices of a patient and/or healthcare professional.

[1240] At block 53402 of the method 53400, the processing device may receive a selection of an optimal treatment plan from the optimal treatment plans. The selection may have been entered on a user interface presenting the optimal treatment plans on the patient interface and/or the assistant interface.

[1241] At block 53404, the processing device may control, based on the selected optimal treatment plan, the treatment apparatus while the patient uses the treatment apparatus. In some embodiments, the controlling is performed distally by the server 5128. If the selection is made using a patient interface, one or more control signals may be transmitted from the patient interface to the treatment apparatus to configure, according to the selected treatment plan, a setting of the treatment apparatus to control operation of the treatment apparatus. Further, if the selection is made using an assistant interface, one or more control signals may be transmitted from the assistant interface to the treatment apparatus to configure, according to the selected treatment plan, a setting of the treatment apparatus to control operation of the treatment apparatus.

[1242] It should be noted, that as the patient uses the treatment apparatus, sensors may transmit measurement data to a processing device. The processing device may dynamically control, according to the treatment plan, the treatment apparatus by modifying, based on the sensor measurements, a setting of the treatment apparatus. For example, if the force measured by the sensors indicates the user is not applying enough force to a pedal, the treatment plan may indicate to reduce the required amount of force for an exercise.

[1243] It should be noted, that as the patient uses the treatment apparatus, the user may use the patient interface to enter input pertaining to a pain level experienced by the patient as the patient performs the treatment plan. For example, the user may enter a high degree of pain while pedaling with the pedals 5110 set to a certain range of motion on the treatment apparatus. The pain level may cause the range of motion to be dynamically adjusted based on the treatment plan. For example, the treatment plan may specify alternative range of motion settings if a certain pain level is indicated when the user is performing an exercise at a certain range of motion. [1244] Clause 1.4. A computer-implemented system, comprising: an electromechanical device configured to be manipulated by a user while the user performs a prehabilitation procedure; a user portal comprising an output device and an input device, the output device configured to communicate a prehabilitation plan to the user, wherein the prehabilitation plan includes at least one exercise session comprising one or more exercises to be performed on the electromechanical device; and a computing device configured to: receive user data relating to the user, wherein the user data comprises health history data relating to health indicators of the user, receive treatment data relating to a set of treatment plans and outcomes, wherein the set of treatment plans is capable of being offered to the user, generate the prehabilitation plan by using a machine learning model to process the user data and the treatment data, select, for the electromechanical device, an electromechanical device configuration that enables the one or more exercises of the prehabilitation plan to be performed by the user, wherein the electromechanical device configuration enables the one or more exercises to be performed to improve performance of an area of the user’s body, and enable the electromechanical device to implement the electromechanical device configuration by providing the electromechanical device configuration to the electromechanical device.

[1245] Clause 2.4. The computer-implemented system of any clause herein, wherein the treatment data comprises treatment plan data relating to the set of treatment plans and treatment outcome data relating to outcomes of the set of treatment plans.

[1246] Clause 3.4. The computer-implemented system of any clause herein, wherein the computing device, when generating the prehabilitation plan, is configured to: provide the user data and the treatment data as inputs to the machine learning model such that the machine learning model is configured to generate machine learning scores for electromechanical device configurations capable of being selected for the prehabilitation plan, wherein the machine learning scores relate to probabilities of a given device configuration being suitable for a given application or applications for the user.

[1247] Clause 4.4. The computer-implemented system of any clause herein, wherein, based on the selected device configuration corresponding to a threshold probability of improving the performance of the area of the user’s body, the selected electromechanical device is suitable for the given user application or applications. [1248] Clause 5.4. The computer-implemented system of any clause herein, wherein, based on the selected device configuration corresponding to a threshold probability of preventing a health-related event from occurring that affects the area of the user’s body, the selected electromechanical device configuration is suitable for the given application or applications for the user.

[1249] Clause 6.4. The computer-implemented system of any clause herein, wherein the electromechanical device configuration is configured such that the user, when performing the one or more exercises on the electromechanical device, is enabled to repeat one or more motions associated with at least one of developing muscle memory or improving muscle memory.

[1250] Clause 7.4. The computer-implement system of any clause herein, wherein the one or more exercises are one or more rehabilitation exercises, wherein the electromechanical device configuration comprises data related to one or more positions at which to configure one or more components of the electromechanical device, and wherein the one or more positions are configured such that the one or more rehabilitation exercises are performed by the user prior to a health-related event occurring that affects the area of the user’s body.

[1251] Clause 8.4. A method for using machine learning to control an electromechanical device, comprising: receiving user data relating to a user capable of operating the electromechanical device as part of a prehabilitation procedure, wherein the user data comprises health history data relating to one or more health indicators of the user; receiving treatment data relating to a set of treatment plans and outcomes, wherein the set of treatment plans is capable of being offered to the user; generating a prehabilitation plan by using a machine learning model to process the user data and the treatment data, wherein the prehabilitation plan includes at least one exercise session comprising one or more exercises to be performed on the electromechanical device; enabling the prehabilitation plan to be distally accessible by one or more user portals; selecting, for the electromechanical device, an electromechanical device configuration that enables the one or more exercises of the prehabilitation plan to be performed by the user, wherein the electromechanical device configuration enables the one or more exercises to be performed to improve performance of an area of the user’s body; and enabling the electromechanical device to implement the electromechanical device configuration by providing the electromechanical device configuration to the electromechanical device.

[1252] Clause 9.4. The method of any clause herein, wherein the treatment data comprises treatment plan data relating to the set of treatment plans and treatment outcome data relating to outcomes of the set of treatment plans.

[1253] Clause 10.4. The method of any clause herein, wherein generating the prehabilitation plan comprises: providing the user data and the treatment data as inputs to the machine learning model such that the machine learning model is configured to generate machine learning scores for electromechanical device configurations capable of being selected for the prehabilitation plan, wherein the machine learning scores relate to probabilities of a given device configuration being suitable for a given application or applications for the user. [1254] Clause 11.4. The method of any clause herein, wherein, based on the selected device configuration corresponding to a threshold probability of improving the performance of the area of the user’s body, the electromechanical device configuration that has been selected is suitable for the given application or applications for the user.

[1255] Clause 12.4. The method of any clause herein, wherein, based on the selected device configuration corresponding to a threshold probability of preventing a health-related event from occurring that affects the area of the user’s body, the electromechanical device configuration that has been selected is suitable for the given application or applications for the user.

[1256] Clause 13.4. The method of any clause herein, wherein the electromechanical device configuration is configured such that the user, when performing the one or more exercises on the electromechanical device, is enabled to repeat one or more motions that develop muscle memory.

[1257] Clause 14.4. The method of any clause herein, wherein the one or more exercises are one or more rehabilitation exercises; wherein the electromechanical device configuration comprises data related to one or more positions at which to configure one or more components of the electromechanical device, and wherein the one or more positions are configured such that the one or more rehabilitation exercises are performed by the user prior to a health-related event occurring that affects the arear of the user’s body.

[1258] Clause 15.4. The method of any clause herein, wherein the one or more exercises relate to one or more rehabilitation exercises; wherein the electromechanical device configuration comprises data related to one or more forces to apply to one or more components of the electromechanical device, and wherein said one or more forces, when applied to the one or more components of the electromechanical device, are to be applied by a motor of the electromechanical device as part of a rehabilitation exercise that is one of the one or more rehabilitation exercises. [1259] Clause 16.4. The method of any clause herein, wherein the electromechanical device configuration is configured such that the one or more exercises, when performed by the user on the electromechanical device, enable the user to improve at least one of ROM, strength, and endurance.

[1260] Clause 17.4. The method of any clause herein, further comprising distally controlling, while the user is performing the one or more exercises, the electromechanical device based on the prehabilitation plan. [1261] Clause 18.4. The method of any clause herein, further comprising: receiving sensor data comprising one or more data values related to determining the user’s progress in the prehabilitation plan; and controlling, while the user is performing the one or more exercises, the electromechanical device based on the sensor data. [1262] Clause 19.4. The method of any clause herein, further comprising: receiving sensor data comprising one or more data values related to determining the user’s progress in the prehabilitation plan; providing the sensor data as an input to the machine learning model such that the machine learning model is configured to output a set of machine learning scores, wherein the set of machine learning scores relates to a set of configuration values capable of being used to modify the electromechanical device configuration; selecting one or more configuration values, from the set of configuration values, based on the one or more configuration values relating to a machine learning score that satisfies a threshold machine learning score; and providing, to the electromechanical device, a modification comprising the one or more configuration values, such that the electromechanical device is enabled to implement the modification.

[1263] Clause 19.4. The method of any clause herein, wherein providing the modification to the electromechanical device comprises: providing the modification to the electromechanical device such that the electromechanical device is enabled to implement the modification to compensate for a difference between a ROM applied by the user and an optimal ROM for the user.

[1264] Clause 20.4. The method of any clause herein, wherein providing the modification to the electromechanical device comprises: providing the modification to the electromechanical device such that the modification compensates for a difference between a force the user applied to one or more components of the electromechanical device and an optimal force the user is to apply to the one or more components.

[1265] Clause 21.4. The method of any clause herein, wherein providing the modification to the electromechanical device comprises: providing the modification to the electromechanical device such that the modification enables a motor of the electromechanical device to control one or more rotations of one or more components of the electromechanical device.

[1266] Clause 22.4. The method of any clause herein, further comprising: receiving sensor data comprising one or more data values related to determining the user’s progress in the prehabilitation plan; providing the sensor data as an input to the machine learning model such that the machine learning model is configured to output one or more risk scores, wherein each of the one or more risk scores represents a probability of a health- related event occurring that affects the area of the user’s body; determining that at least one of the one or more risk scores satisfy a threshold risk score; and responsive to determining that at least one of the one or more risk scores satisfy a corresponding threshold risk score, performing one or more actions to reduce or eliminate a probability of the health-related event from occurring to the user.

[1267] Clause 23.4. The method of any clause herein, wherein the exercise session is a prehabilitation exercise session, and wherein the method further comprises: receiving sensor data comprising one or more data values related to the user’s progress in the prehabilitation plan; receiving medical procedure data relating to an updated health indicator that comprises the one or more health indicators of the user; providing the user data, the sensor data, and the medical procedure data as inputs to the machine learning model such that the machine learning model is configured to output a set of machine learning scores, wherein the set of machine learning scores relates to a set of configuration values capable of being used in a device configuration for a rehabilitation plan; selecting one or more configuration values, from the set of configuration values, based on the one or more configuration values relating to one or more machine learning scores that satisfy a threshold machine learning score; and providing the one or more configuration values to the electromechanical device, wherein the one or more configuration values are part of the electromechanical device configuration for the rehabilitation plan, and wherein said one or more configuration values are provided to the electromechanical device such that the electromechanical device is enabled to implement the electromechanical device configuration for the rehabilitation plan.

METHOD AND SYSTEM FOR CREATING AN IMMERSIVE ENAHNCED REALITY-DRIVEN

EXERCISE EXPERIENCE FOR A USER

[1268] Determining a treatment plan for a patient having certain characteristics (e.g., vital-sign or other measurements; performance; demographic; geographic; diagnostic; measurement- or test-based; medically historic; behavioral historic; cognitive; etiologic; cohort-associative; differentially diagnostic; surgical, physically therapeutic, pharmacologic and other treatment(s) recommended; etc.) may be a technically challenging problem. For example, a multitude of information may be considered when determining a treatment plan, which may result in inefficiencies and inaccuracies in the treatment plan selection process. In a rehabilitative setting, some of the multitude of information considered may include characteristics of the patient such as personal information, performance information, and measurement information. The personal information may include, e.g., demographic, psychographic or other information, such as an age, a weight, a gender, a height, a body mass index, a medical condition, a familial medication history, an injury, a medical procedure, a medication prescribed, or some combination thereof. The performance information may include, e.g., an elapsed time of using a treatment device, an amount of force exerted on a portion of the treatment device, a range of motion achieved on the treatment device, a movement speed of a portion of the treatment device, a duration of use of the treatment device, an indication of a plurality of pain levels using the treatment device, or some combination thereof. The measurement information may include, e.g., a vital sign, a respiration rate, a heartrate, a temperature, a blood pressure, a glucose level or other biomarker, or some combination thereof. It may be desirable to process and analyze the characteristics of a multitude of patients, the treatment plans performed for those patients, and the results of the treatment plans for those patients.

[1269] Further, another technical problem may involve distally treating, via a computing device during a telemedicine or telehealth session, a patient from a location different than a location at which the patient is located. An additional technical problem is controlling or enabling the control of, from the different location, a treatment device used by the patient at the location at which the patient is located. Oftentimes, when a patient undergoes rehabilitative surgery (e.g., knee surgery), a healthcare provider may prescribe a treatment device to the patient to use to perform a treatment protocol at their residence or any mobile location or temporary domicile. A healthcare provider may refer to a doctor, physician assistant, nurse, chiropractor, dentist, physical therapist, acupuncturist, physical trainer, coach, personal trainer, or the like. A healthcare provider may refer to any person with a credential, license, degree, or the like in the field of medicine, physical therapy, rehabilitation, or the like. [1270] When the healthcare provider is located in a different location from the patient and the treatment device, it may be technically challenging for the healthcare provider to monitor the patient’s actual progress (as opposed to relying on the patient’s word about their progress) using the treatment device, modify the treatment plan according to the patient’s progress, adapt the treatment device to the personal characteristics of the patient as the patient performs the treatment plan, and the like.

[1271] Additionally, or alternatively, performing the treatment plan may be perceived by the patient as being tedious or difficult, due to the repetitive nature of the treatment plan and/or the physical difficulties (e.g., muscle strain, pain, or other physical perceptions) perceived by the patient and/or the length of time to complete the treatment plan (e.g., the length of a session to complete the treatment plan, the number of times the patient has to perform the treatment plan, or a combination thereof).

[1272] Accordingly, systems and methods, such as those described herein, that provide, while the patient performs the treatment plan, an enhanced reality experience to engage, encourage, focus, or otherwise increase the patient’s desire to perform the treatment plan, may be desirable.

[1273] In some embodiments, the systems and methods described herein may be configured to receive treatment data pertaining to a user who uses a treatment device to perform a treatment plan. The user may include a patient user or person using the treatment device to perform various exercises. The treatment data may include various characteristics of the user, various measurement information pertaining to the user while the user uses the treatment device, various characteristics of the treatment device, the treatment plan, other suitable data, or a combination thereof. In some embodiments, the systems and methods described herein may be configured to receive the treatment data during a telemedicine session.

[1274] In some embodiments, while the user uses the treatment device to perform the treatment plan, at least some of the treatment data may correspond to sensor data of a sensor configured to sense various characteristics of the treatment device and/or the measurement information of the user. Additionally, or alternatively, while the user uses the treatment device to perform the treatment plan, at least some of the treatment data may correspond to sensor data from a sensor associated with a wearable device configured to sense and/or obtain the measurement information of the user.

[1275] The various characteristics of the treatment device may include one or more settings of the treatment device, a current revolutions per time period (e.g., such as one minute) of a rotating member (e.g., such as a wheel) of the treatment device, a resistance setting of the treatment device, other suitable characteristics of the treatment device, or a combination thereof. The measurement information may include one or more vital signs of the user, a respiration rate of the user, a heartrate of the user, a temperature of the user, a blood pressure of the user, a glucose level of the user, other suitable measurement information of the user, or a combination thereof.

[1276] In some embodiments, the systems and methods described herein may be configured to identify, using the treatment data, at least one enhanced component. The at least one enhanced component may comprise one or more of one or more augmented reality components, one or more immersive reality components, one or more mixed reality components, one or more virtual reality components, or a combination thereof. [1277] The augmented reality component may include a digital object configured to be presented to the user such that the user perceives the digital object to be overlaid onto a real-world environment. The digital object may include information pertaining to the performance of the treatment plan (e.g., speed information, distance information, resistance information, goal information, and/or other information), an image or video (e.g., or a person, landscape, and/or other suitable image or video), sound or other audible component, other suitable digital object, or a combination thereof. The virtual reality component may include at least a portion of a virtual world or environment, such as a sound component, a visual component, a tactile component, a haptic component, other suitable portion of the virtual world, or a combination thereof.

[1278] In some embodiments, by using a database that correlates enhanced components with corresponding treatment plans, the systems and methods described herein may be configured to identity the at least one enhanced component. For example, a treatment plan may correspond to various enhanced components. A relationship between the various enhanced components and the treatment plan may be indicated in the database. Additionally, or alternatively, the treatment plan may indicate various enhanced components to use with the treatment plan. Additionally, or alternatively, the user may indicate one or more desired enhanced components for the treatment plan. A relationship between the one or more desired enhanced components and the treatment plan may be indicated in the database.

[1279] In some embodiments, the systems and methods described herein may be configured to generate an enhanced environment using the at least one enhanced component and the treatment plan. While the user uses the treatment device to perform the treatment plan, the enhanced environment may be configured to enhance the experience perceived by the user. For example, the enhanced environment may be presented to the user, while the user uses the treatment device to perform the treatment plan, images, video, sound, tactile feedback, haptic feedback, and/or the like. While the user uses the treatment device to perform the treatment plan, the enhanced environment may be configured to encourage the user, focus the user, inform the user, distract the user, or otherwise assist the user in performing the treatment plan.

[1280] In some embodiments, the systems and methods described herein may be configured to output at least one aspect of the enhanced environment to an interface configured to communicate with the treatment device. In some embodiments, the systems and methods described herein may be configured to output, during a telemedicine session, the enhanced environment to the interface. The interface may include at least one enhanced reality device configured to present, while the user uses the treatment device and based on the outputted enhanced environment, the enhanced environment to the user. The at least one enhanced reality device may include an augmented reality device, a virtual reality device, a mixed reality device, an immersive reality device, or a combination thereof. The augmented reality device may include one or more speakers, one or more wearable devices (e.g., goggles, gloves, shoes, body coverings, mechanical devices, helmets, and the like), one or more restraints, a seat, a body halo, one or more controllers, one or more interactive positioning devices, other suitable augmented reality devices, one or more other augmented reality devices, or a combination thereof. For example, the augmented reality device may include a display with one or more integrated speakers.

[1281] The virtual reality device may include one or more displays, one or more speakers, one or more wearable devices (e.g., goggles, gloves, shoes, body coverings, mechanical devices, helmets, and the like), one or more restraints, a seat, a body halo, one or more controllers, one or more interactive positioning devices, other suitable virtual reality devices, or a combination thereof. The mixed reality device may include a combination of one or more augmented reality devices and one or more virtual reality devices. The immersive reality device may include a combination of one or more virtual reality devices, mixed reality devices, augmented reality devices, or a combination thereof.

[1282] In some embodiments, the at least one enhanced reality device may communicate or interact with the treatment device. For example, the at least one enhanced reality device may communicate with the treatment device via a wired or wireless connection, such as those described herein. The at least one enhanced reality device may send a signal to the treatment device to modify characteristics of the treatment device based on the at least one enhanced component and/or the enhanced environment. Based on the signal, a controller of the treatment device may selectively modify characteristics of the treatment device.

[1283] In some embodiments, the systems and methods described herein may be configured to receive, while the user engages in the enhanced environment while using the treatment device to perform the treatment plan, subsequent treatment data pertaining to the user.

[1284] In some embodiments, the systems and methods described herein may be configured to selectively modify the enhanced environment, at least one aspect of the treatment plan, a portion of the treatment plan, a portion of the at least one aspect of the treatment, a portion or a combination thereof. For example, the systems and methods described herein may be configured to determine whether the subsequent data indicates that the enhanced environment and/or the treatment plan are having a desired effect, as will be described. The systems and methods described herein may be configured to modify, in response to determining that the enhanced environment and/or the treatment plan are not having the desired effect, the enhanced environment, at least one aspect of the treatment plan, or a portion or a combination thereof, to attempt to achieve the desired effect or a portion of the desired effect.

[1285] In some embodiments, the systems and methods described herein may be configured to selectively modify, using the subsequent treatment data while the user uses the treatment device during a telemedicine session, the enhanced environment, the at least one aspect of the treatment plan, any other aspect of the treatment plan, or a portion or a combination thereof.

[1286] In some embodiments, the systems and methods described herein may determine that the enhanced environment and/or the treatment plan are having the desired effect and may modify the enhanced environment, at least one aspect of the treatment plan, or a potion or a combination thereof, to motivate the user to act or cease to act in a particular way or to achieve an alternative desired effect or a portion of the alternative desired effect (e.g., the systems and methods described herein may determine that the user is capable of handling a more rigorous treatment plan and may benefit from the more rigorous treatment plan).

[1287] In some embodiments, the systems and methods described herein may be configured to control, while the user uses the treatment device, the treatment device. For example, the systems and methods described herein may control one or more characteristics of the treatment device based on the enhanced environment, the modified at least one of the enhanced environment and the at least one aspect of the treatment plan, or portion or a combination thereof.

[1288] In some embodiments, the systems and methods described herein may be configured to generate treatment information using the treatment data. The treatment information may include a summary of the performance, by the user while using the treatment device, of the treatment plan formatted, such that the treatment data is presentable at a computing device of a healthcare provider responsible for the performance of the treatment plan by the user. The treatment data may be presented to the user via the user’s computing device, which may enable the user to better understand their progress, performance, and future goals. Further, presenting the treatment data to the user may motivate the user to continue to perform the treatment plan. In some embodiments, presenting the treatment data to the user may specify a problem of the treatment plan and/or non- compliance with the treatment plan, which may be subsequently addressed. The healthcare provider may include a medical professional (e.g., such as a doctor, a nurse, a therapist, and the like), an exercise professional (e.g., such as a coach, a trainer, a nutritionist, and the like), or another professional sharing at least one of medical and exercise attributes (e.g., such as an exercise physiologist, a physical therapist, an occupational therapist, and the like). As used herein, and without limiting the foregoing, a “healthcare provider” may be a human being, a robot, a virtual assistant, a virtual assistant in virtual and/or augmented reality, or an artificially intelligent entity, such entity including a software program, integrated software and hardware, or hardware alone.

[1289] The systems and methods described herein may be configured to write to an associated memory, for access at the computing device of the healthcare provider and/or the user. The systems and methods may provide, at the computing device of the healthcare provider and/or the user, the treatment information. For example, the systems and methods describe herein may be configured to provide the treatment information to an interface configured to present the treatment information to the healthcare provider. The interface may include a graphical user interface configured to provide the treatment information and receive input from the healthcare provider. The interface may include one or more input fields, such as text input fields, dropdown selection input fields, radio button input fields, virtual switch input fields, virtual lever input fields, audio, haptic, tactile, biometric, or otherwise activated and/or driven input fields, other suitable input fields, or a combination thereof.

[1290] In some embodiments, the healthcare provider may review the treatment information and determine whether to modify the enhanced environment, at least one aspect of the treatment plan, and/or one or more characteristics of the treatment device. For example, the healthcare provider may review the treatment information and compare the treatment information to the treatment plan being performed by the user.

[1291] The healthcare provider may compare the following (i) expected information, which pertains to the user’s expected or predicted performance when the user actually uses the treatment device to perform the treatment plan to (ii) the measurement information (e.g., indicated by the treatment information), which pertains to the user while the user is using the treatment device to perform the treatment plan.

[1292] The expected information may include one or more vital signs of the user, a respiration rate of the user, a heartrate of the user, a temperature of the user, a blood pressure of the user, other suitable information of the user, or a combination thereof. The healthcare provider may determine that the treatment plan is having the desired effect if one or more parts or portions of the measurement information are within an acceptable range associated with one or more corresponding parts or portions of the expected information. Alternatively, the healthcare provider may determine that the treatment plan is not having the desired effect (e.g., not achieving the desired effect or a portion of the desired effect) if one or more parts or portions of the measurement information are outside of the range associated with one or more corresponding parts or portions of the expected information.

[1293] For example, the healthcare provider may determine whether a blood pressure value (e.g., systolic pressure, diastolic pressure, and/or pulse pressure) corresponding to the user while the user uses the treatment device (e.g., indicated by the measurement information) is within an acceptable range (e.g., plus or minus 1%, plus or minus 5%, plus or minus a particular number of units suitable for the measurement (e.g., actual or digitally equivalent column inches of mercury for blood pressure, and the like), or any suitable range) of an expected blood pressure value indicated by the expected information. The healthcare provider may determine that the treatment plan is having the desired effect (e.g., achieving the desired effect or a portion of the desired effect) if the blood pressure value corresponding to the user while the user uses the treatment device is within the range of the expected blood pressure value. Alternatively, the healthcare provider may determine that the treatment plan is not having the desired effect if the blood pressure value corresponding to the user while the user uses the treatment device is outside of the range of the expected blood pressure value.

[1294] In some embodiments, the healthcare provider may compare the expected characteristics of the treatment device while the user uses the treatment device to perform the treatment plan with characteristics of the treatment device indicated by the treatment information. For example, the healthcare provider may compare an expected resistance setting of the treatment device with an actual resistance setting of the treatment device indicated by the treatment information. The healthcare provider may determine that the user is performing the treatment plan properly if the actual characteristics of the treatment device indicated by the treatment information are within a range of corresponding ones of the expected characteristics of the treatment device. Alternatively, the healthcare provider may determine that the user is not performing the treatment plan properly if the actual characteristics of the treatment device indicated by the treatment information are outside the range of corresponding ones of the expected characteristics of the treatment device.

[1295] If the healthcare provider determines that the treatment information indicates that the user is performing the treatment plan properly and/or that the treatment plan is having the desired effect, the healthcare provider may determine not to modify the enhanced environment, at least one aspect of the treatment plan, and/or the one or more characteristics of the treatment device. Alternatively, while the user uses the treatment device to perform the treatment plan, if the healthcare provider determines that the treatment information indicates that the user is not or has not been performing the treatment plan properly and/or that the treatment plan is not or has not been having the desired effect, the healthcare provider may determine to modify the enhanced environment, at least one aspect of the treatment plan and/or the one or more characteristics of the treatment device.

[1296] In some embodiments, the healthcare provider may interact with the interface to provide treatment plan input indicating one or more modifications to the enhanced environment, treatment plan, and/or one or more characteristics of the treatment device if the healthcare provider determines to modify the enhanced environment, treatment plan, and/or to one or more characteristics of the treatment device. For example, the healthcare provider may use the interface to provide input indicating an increase or decrease in the resistance setting of the treatment device, or other suitable modification to the one or more characteristics of the treatment device. Additionally, or alternatively, the healthcare provider may use the interface to provide input indicating a modification to the treatment plan. For example, the healthcare provider may use the interface to provide input indicating an increase or decrease in an amount of time the user is required to use the treatment device according to the treatment plan, or other suitable modifications to the treatment plan. Additionally, or alternatively, the healthcare provider may use the interface to provide input indicating one or more modifications to the at least one enhanced component used to generate the enhanced environment and/or other modifications to the enhanced environment. [1297] In some embodiments, the systems and methods described herein may be configured to modify the enhanced environment and/or treatment plan based on one or more modifications indicated by the treatment plan input. Additionally, or alternatively, the systems and methods described herein may be configured to modify the one or more characteristics of the treatment device based on the modified enhanced environment, the modified treatment plan and/or the treatment plan input. For example, the treatment plan input may indicate that the one or more characteristics of the treatment device and/or the modified enhanced environment should be modified and/or the modified treatment plan may require or indicate adjustments to the treatment device in order for the user to achieve the desired results of the modified treatment plan.

[1298] In some embodiments, while the user uses the treatment device to perform the modified treatment plan, the systems and methods described herein may be configured to receive the subsequent treatment data pertaining to the user. For example, after the healthcare provider provides input modifying the treatment plan and/or controlling the one or more characteristics of the treatment device, the user may continue using the treatment device to interact with the enhanced environment and/or the modified enhanced environment to perform the modified treatment plan. The subsequent treatment data may correspond to treatment data generated while the user uses the treatment device to interact with the modified enhanced environment and/or the enhanced environment to perform the modified treatment plan. In some embodiments, the subsequent treatment data may correspond to treatment data generated while the user continues to use the treatment device to perform the treatment plan, after the healthcare provider has received the treatment information and determined not to modify the treatment plan and/or control the one or more characteristics of the treatment device.

[1299] Based on subsequent treatment plan input received from the computing device of the healthcare provider, the systems and methods described herein may be configured to further modify the enhanced environment or the treatment plan, and/or to further control the one or more characteristics of the treatment device. The subsequent treatment plan input may correspond to input provided by the healthcare provider, at the interface, in response to receiving and/or reviewing subsequent treatment information corresponding to the subsequent treatment data. It should be understood that the systems and methods described herein may be configured to continuously and/or periodically provide treatment information to the computing device of the healthcare provider based on treatment data continuously and/or periodically received from the sensors or other suitable sources described herein.

[1300] The healthcare provider may receive and/or review treatment information continuously or periodically while the user uses the treatment device to interact with the enhanced environment to perform the treatment plan. Based on one or more trends indicated by the continuously and/or periodically received treatment information, the healthcare provider may determine whether to modify the enhanced environment or the treatment plan, and/or to control the one or more characteristics of the treatment device. For example, the one or more trends may indicate an increase in heartrate or other suitable trends indicating that the user is not performing the treatment plan properly and/or that performance of the treatment plan by the user is not having the desired effect. Additionally, or alternatively, the one or more trends may indicate an unacceptable increase in heartrate or the recognition of other suitable trends indicating that the enhanced environment is not having the desired effect.

[1301] In some embodiments, during an adaptive telemedicine session, the systems and methods described herein may be configured to use artificial intelligence and/or machine learning to assign patients to cohorts and to dynamically control a treatment device based on the assignment. The term “adaptive telemedicine” may refer to a telemedicine session that is dynamically adapted based on one or more factors, criteria, parameters, characteristics, or the like. The one or more factors, criteria, parameters, characteristics, or the like may pertain to the user (e.g., heartrate, blood pressure, perspiration rate, pain level, or the like), the treatment device (e.g., pressure, range of motion, speed of motor, etc.), details of the treatment plan, and so forth.

[1302] In some embodiments, numerous patients may be prescribed numerous treatment devices because the numerous patients are recovering from the same medical procedure and/or suffering from the same injury. The numerous treatment devices may be provided to the numerous patients. The treatment devices may be used by the patients to perform treatment plans in their residences, at gyms, at rehabilitative centers, at hospitals, or at any suitable locations, including permanent or temporary domiciles.

[1303] In some embodiments, the systems and methods described herein may be configured to use artificial intelligence engines and/or machine learning models to generate, modify, and/or control aspects of the enhanced environment. For example, the artificial intelligence engines and/or machine learning models may identify the one or more enhanced components based on the treatment data. The artificial intelligence engines and/or machine learning models may generate the enhanced environment using one or more enhanced components. The artificial intelligence engines and/or machine learning models may analyze the subsequent treatment data and selectively modify the enhanced environment in order to increase the likelihood of achieving desired results from the user performing the treatment plan while the user is interacting with the enhanced environment and using the treatment device.

[1304] In some embodiments, the treatment devices may be communicatively coupled to a server. Characteristics of the patients, including the treatment data, may be collected before, during, and/or after the patients perform the treatment plans. For example, any or each of the personal information, the performance information, and the measurement information may be collected before, during, and/or after a patient performs the treatment plans. The results (e.g., improved performance or decreased performance) of performing each exercise may be collected from the treatment device throughout the treatment plan and after the treatment plan is performed. The parameters, settings, configurations, etc. (e.g., position of pedal, amount of resistance, etc.) of the treatment device may be collected before, during, and/or after the treatment plan is performed.

[1305] Each characteristic of the patient, each result, and each parameter, setting, configuration, etc. may be timestamped and may be correlated with a particular step or set of steps in the treatment plan. Such a technique may enable the determination of which steps in the treatment plan lead to desired results (e.g., improved muscle strength, range of motion, etc.) and which steps lead to diminishing returns (e.g., continuing to exercise after 3 minutes actually delays or harms recovery).

[1306] Data may be collected from the treatment devices and/or any suitable computing device (e.g., computing devices where personal information is entered, such as the interface of the computing device described herein, a clinician interface, patient interface, and the like) over time as the patients use the treatment devices to perform the various treatment plans. The data that may be collected may include the characteristics of the patients, the treatment plans performed by the patients, the results of the treatment plans, any of the data described herein, any other suitable data, or a combination thereof.

[1307] In some embodiments, the data may be processed to group certain people into cohorts. The people may be grouped by people having certain or selected similar characteristics, treatment plans, and results of performing the treatment plans. For example, athletic people having no medical conditions who perform a treatment plan (e.g., use the treatment device for 30 minutes a day 5 times a week for 3 weeks) and who fully recover may be grouped into a first cohort. Older people who are classified obese and who perform a treatment plan (e.g., use the treatment plan for 10 minutes a day 3 times a week for 4 weeks) and who improve their range of motion by 75 percent may be grouped into a second cohort.

[1308] In some embodiments, an artificial intelligence engine may include one or more machine learning models that are trained using the cohorts. In some embodiments, the artificial intelligence engine may be used to identify trends and/or patterns and to define new cohorts based on achieving desired results from the treatment plans and machine learning models associated therewith may be trained to identify such trends and/or patterns and to recommend and rank the desirability of the new cohorts. For example, the one or more machine learning models may be trained to receive an input of characteristics of a new patient and to output a treatment plan for the patient that results in a desired result. The machine learning models may match a pattern between the characteristics of the new patient and at least one patient of the patients included in a particular cohort. When a pattern is matched, the machine learning models may assign the new patient to the particular cohort and select the treatment plan associated with the at least one patient. The artificial intelligence engine may be configured to control, distally and based on the treatment plan, the treatment device while the new patient uses the treatment device to perform the treatment plan.

[1309] As may be appreciated, the characteristics of the new patient (e.g., a new user) may change as the new patient uses the treatment device to perform the treatment plan. For example, the performance of the patient may improve quicker than expected for people in the cohort to which the new patient is currently assigned. Accordingly, the machine learning models may be trained to dynamically reassign, based on the changed characteristics, the new patient to a different cohort that includes people having characteristics similar to the now-changed characteristics as the new patient. For example, a clinically obese patient may lose weight and no longer meet the weight criterion for the initial cohort, result in the patient’ s being reassigned to a different cohort with a different weight criterion.

[1310] A different treatment plan may be selected for the new patient, and the treatment device may be controlled, distally (e.g., which may be referred to as remotely) and based on the different treatment plan, while the new patient uses the treatment device to perform the treatment plan. Such techniques may provide the technical solution of distally controlling a treatment device.

[1311] Further, the systems and methods described herein may lead to faster recovery times and/or better results for the patients because the treatment plan that most accurately fits their characteristics is selected and implemented, in real-time, at any given moment. “Real-time” may also refer to near real-time, which may be less than 10 seconds. As described herein, the term “results” may refer to medical results or medical outcomes. Results and outcomes may refer to responses to medical actions. The term “medical action(s)” may refer to any suitable action performed by the healthcare provider, such actions may include diagnoses, prescription of treatment plans, prescription of treatment devices, and the making, composing and/or executing of appointments, telemedicine sessions, prescription of medicines, telephone calls, emails, text messages, and the like.

[1312] Depending on what result is desired, the artificial intelligence engine may be trained to output several treatment plans. For example, one result may include recovering to a threshold level (e.g., 75% range of motion) in a fastest amount of time, while another result may include fully recovering (e.g., 100% range of motion) regardless of the amount of time. The data obtained from the patients and sorted into cohorts may indicate that a first treatment plan provides the first result for people with characteristics similar to the patient’s, and that a second treatment plan provides the second result for people with characteristics similar to the patient.

[1313] Further, the artificial intelligence engine may be trained to output treatment plans that are not optimal i.e., sub-optimal, nonstandard, or otherwise excluded (all referred to, without limitation, as “excluded treatment plans”) for the patient. For example, if a patient has high blood pressure, a particular exercise may not be approved or suitable for the patient as it may put the patient at unnecessary risk or even induce a hypertensive crisis and, accordingly, that exercise may be flagged in the excluded treatment plan for the patient. In some embodiments, the artificial intelligence engine may monitor the treatment data received while the patient (e.g., the user) with, for example, high blood pressure, uses the treatment device to perform an appropriate treatment plan and may modify the appropriate treatment plan to include features of an excluded treatment plan that may provide beneficial results for the patient if the treatment data indicates the patient is handling the appropriate treatment plan without aggravating, for example, the high blood pressure condition of the patient. In some embodiments, the artificial intelligence engine may modify the treatment plan if the monitored data shows the plan to be inappropriate or counterproductive for the user.

[1314] In some embodiments, the treatment plans and/or excluded treatment plans may be presented, during a telemedicine or telehealth session, to a healthcare provider. The healthcare provider may select a particular treatment plan for the patient to cause that treatment plan to be transmitted to the patient and/or to control, based on the treatment plan, the treatment device. In some embodiments, to facilitate telehealth or telemedicine applications, including remote diagnoses, determination of treatment plans and rehabilitative and/or pharmacologic prescriptions, the artificial intelligence engine may receive and/or operate distally from the patient and the treatment device.

[1315] In such cases, the recommended treatment plans and/or excluded treatment plans may be presented simultaneously with a video of the patient in real-time or near real-time during a telemedicine or telehealth session on a user interface of a computing device of a healthcare provider. The video may also be accompanied by audio, text and other multimedia information. Real-time may refer to less than or equal to 2 seconds. Near real-time may refer to any interaction of a sufficiently short time to enable two individuals to engage in a dialogue via such user interface, and will generally be less than 10 seconds but greater than 2 seconds.

[1316] Presenting the treatment plans generated by the artificial intelligence engine concurrently with a presentation of the patient video may provide an enhanced user interface because the healthcare provider may continue to visually and/or otherwise communicate with the patient while also reviewing the treatment plans on the same user interface. The enhanced user interface may improve the healthcare provider’s experience using the computing device and may encourage the healthcare provider to reuse the user interface. Such a technique may also reduce computing resources (e.g., processing, memory, network) because the healthcare provider does not have to switch to another user interface screen to enter a query for a treatment plan to recommend based on the characteristics of the patient. The artificial intelligence engine may be configured to provide, dynamically on the fly, the treatment plans and excluded treatment plans.

[1317] In some embodiments, the treatment device may be adaptive and/or personalized because its properties, configurations, and positions may be adapted to the needs of a particular patient. For example, the pedals may be dynamically adjusted on the fly (e.g., via a telemedicine session or based on programmed configurations in response to certain measurements being detected) to increase or decrease a range of motion to comply with a treatment plan designed for the user. In some embodiments, a healthcare provider may adapt, remotely during a telemedicine session, the treatment device to the needs of the patient by causing a control instruction to be transmitted from a server to treatment device. Such adaptive nature may improve the results of recovery for a patient, furthering the goals of personalized medicine, and enabling personalization of the treatment plan on a per-individual basis.

[1318] A technical problem may occur which relates to the information pertaining to the patient’s medical condition being received in disparate formats. For example, a server may receive the information pertaining to a medical condition of the patient from one or more sources (e.g., from an electronic medical record (EMR) system, application programming interface (API), or any suitable system that has information pertaining to the medical condition of the patient). That is, some sources used by various healthcare providers may be installed on their local computing devices and may use proprietary formats. Accordingly, some embodiments of the present disclosure may use an API to obtain, via interfaces exposed by APIs used by the sources, the formats used by the sources. In some embodiments, when information is received from the sources, the API may map, translate and/or convert the format used by the sources to a standardized format used by the artificial intelligence engine. Further, the information mapped, translated and/or converted to the standardized format used by the artificial intelligence engine may be stored in a database accessed by the artificial intelligence engine when performing any of the techniques disclosed herein. Using the information mapped, translated and/or converted to a standardized format may enable the more accurate determination of the procedures to perform for the patient and/or a billing sequence.

[1319] To that end, the standardized information may enable the generation of treatment plans and/or billing sequences having a particular format configured to be processed by various applications (e.g., telehealth). For example, applications, such as telehealth applications, may be executing on various computing devices of medical professionals and/or patients. The applications (e.g., standalone or web-based) may be provided by a server and may be configured to process data according to a format in which the treatment plans are implemented. Accordingly, the disclosed embodiments may provide a technical solution by (i) receiving, from various sources (e.g., EMR systems), information in non-standardized and/or different formats; (ii) standardizing the information; and (iii) generating, based on the standardized information, treatment plans having standardized formats capable of being processed by applications (e.g., telehealth applications) executing on computing devices of medical professional and/or patients.

[1320] FIG. 81 generally illustrates a block diagram of a computer-implemented system 6010, hereinafter called “the system” for managing a treatment plan. Managing the treatment plan may include using an artificial intelligence engine to recommend treatment plans and/or provide excluded treatment plans that should not be recommended to a patient.

[1321] The system 6010 also includes a server 6030 configured to store (e.g., write to an associated memory) and to provide data related to managing the treatment plan. The server 6030 may include one or more computers and may take the form of a distributed and/or virtualized computer or computers. The server 6030 also includes a first communication interface 6032 configured to communicate with the clinician interface 6020 via a first network 6034. In some embodiments, the first network 6034 may include wired and/or wireless network connections such as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC), cellular data network, etc. The server 6030 includes a first processor 6036 and a first machine-readable storage memory 38, which may be called a “memory” for short, holding first instructions 6040 for performing the various actions of the server 6030 for execution by the first processor 6036.

[1322] The server 6030 is configured to store data regarding the treatment plan. For example, the memory 6038 includes a system data store 6042 configured to hold system data, such as data pertaining to treatment plans for treating one or more patients. The server 6030 is also configured to store data regarding performance by a patient in following a treatment plan. For example, the memory 6038 includes a patient data store 6044 configured to hold patient data, such as data pertaining to the one or more patients, including data representing each patient’s performance within the treatment plan.

[1323] Additionally, or alternatively, the characteristics (e.g., personal, performance, measurement, etc.) of the people, the treatment plans followed by the people, the level of compliance with the treatment plans, and the results of the treatment plans may use correlations and other statistical or probabilistic measures to enable the partitioning of or to partition the treatment plans into different patient cohort-equivalent databases in the patient data store 6044. For example, the data for a first cohort of first patients having a first similar injury, a first similar medical condition, a first similar medical procedure performed, a first treatment plan followed by the first patient, and a first result of the treatment plan may be stored in a first patient database. The data for a second cohort of second patients having a second similar injury, a second similar medical condition, a second similar medical procedure performed, a second treatment plan followed by the second patient, and a second result of the treatment plan may be stored in a second patient database. Any single characteristic or any combination of characteristics may be used to separate the cohorts of patients. In some embodiments, the different cohorts of patients may be stored in different partitions or volumes of the same database. There is no specific limit to the number of different cohorts of patients allowed, other than as limited by mathematical combinatoric and/or partition theory.

[1324] This characteristic data, treatment plan data, and results data may be obtained from numerous treatment devices and/or computing devices and/or digital storage media over time and stored in the data store 44. The characteristic data, treatment plan data, and results data may be correlated in the patient-cohort databases in the patient data store 44. The characteristics of the people may include personal information, performance information, and/or measurement information.

[1325] In addition to the historical information about other people stored in the patient cohort-equivalent databases, real-time or near-real-time information based on the current patient’s characteristics about a current patient being treated may be stored in an appropriate patient cohort-equivalent database. The characteristics of the patient may be determined to match or be similar to the characteristics of another person in a particular cohort (e.g., cohort A) and the patient may be assigned to that cohort.

[1326] In some embodiments, the server 6030 may execute an artificial intelligence (AI) engine 6011 that uses one or more machine learning models 6013 to perform at least one of the embodiments disclosed herein. The server 6030 may include a training engine 6009 capable of generating the one or more machine learning models 6013. The machine learning models 6013 may be trained to assign people to certain cohorts based on their characteristics, select treatment plans using real-time and historical data correlations involving patient cohort-equivalents, and control a treatment device 6070, among other things. [1327] The one or more machine learning models 6013 may be generated by the training engine 6009 and may be implemented in computer instructions executable by one or more processing devices of the training engine 6009 and/or the servers 6030. To generate the one or more machine learning models 6013, the training engine 6009 may train the one or more machine learning models 6013. The one or more machine learning models 6013 may be used by the artificial intelligence engine 6011.

[1328] The training engine 6009 may be a rackmount server, a router computer, a personal computer, a portable digital assistant, a smartphone, a laptop computer, a tablet computer, a netbook, a desktop computer, an Internet of Things (IoT) device, any other suitable computing device, or a combination thereof. The training engine 6009 may be cloud-based or a real-time software platform, and it may include privacy software or protocols, and/or security software or protocols.

[1329] To train the one or more machine learning models 6013, the training engine 6009 may use a training data set of a corpus of the characteristics of the people that used the treatment device 6070 to perform treatment plans, the details (e.g., treatment protocol including exercises, amount of time to perform the exercises, how often to perform the exercises, a schedule of exercises, parameters/configurations/settings of the treatment device 6070 throughout each step of the treatment plan, etc.) of the treatment plans performed by the people using the treatment device 6070, and the results of the treatment plans performed by the people. The one or more machine learning models 6013 may be trained to match patterns of characteristics of a patient with characteristics of other people assigned to a particular cohort. The term “match” may refer to an exact match, a correlative match, a substantial match, etc. The one or more machine learning models 6013 may be trained to receive the characteristics of a patient as input, map the characteristics to characteristics of people assigned to a cohort, and select a treatment plan from that cohort. The one or more machine learning models 6013 may also be trained to control, based on the treatment plan, the treatment device 6070. The one or more machine learning models 6013 may also be trained to provide one or more treatment plans options to a a healthcare provider to select from to control the treatment device 6070.

[1330] Different machine learning models 6013 may be trained to recommend different treatment plans for different desired results. For example, one machine learning model may be trained to recommend treatment plans for most effective recovery, while another machine learning model may be trained to recommend treatment plans based on speed of recovery.

[1331] Using training data that includes training inputs and corresponding target outputs, the one or more machine learning models 6013 may refer to model artifacts created by the training engine 6009. The training engine 6009 may find patterns in the training data wherein such patterns map the training input to the target output and generate the machine learning models 6013 that capture these patterns. In some embodiments, the artificial intelligence engine 6011, the database 6033, and/or the training engine 6009 may reside on another component (e.g., assistant interface 6094, clinician interface 6020, etc.) depicted in FIG. 81.

[1332] The one or more machine learning models 6013 may comprise, e.g., a single level of linear or non linear operations (e.g., a support vector machine [SVM]) or the machine learning models 6013 may be a deep network, i.e., a machine learning model comprising multiple levels of non-linear operations. Examples of deep networks are neural networks including generative adversarial networks, convolutional neural networks, recurrent neural networks with one or more hidden layers, and fully connected neural networks (e.g., each neuron may transmit its output signal to the input of the remaining neurons, as well as to itself). For example, the machine learning model may include numerous layers and/or hidden layers that perform calculations (e.g., dot products) using various neurons.

[1333] The system 6010 also includes a patient interface 6050 configured to communicate information to a patient and to receive feedback from the patient. Specifically, the patient interface includes an input device 6052 and an output device 6054, which may be collectively called a patient user interface 6052, 6054. The input device 6052 may include one or more devices, such as a keyboard, a mouse, a touch screen input, a gesture sensor, and/or a microphone and processor configured for voice recognition. The output device 6054 may take one or more different forms including, for example, a computer monitor or display screen on a tablet, smartphone, or a smart watch. The output device 6054 may include other hardware and/or software components such as a projector, virtual reality capability, augmented reality capability, etc. The output device 6054 may incorporate various different visual, audio, or other presentation technologies. For example, the output device 6054 may include a non-visual display, such as an audio signal, which may include spoken language and/or other sounds such as tones, chimes, and/or melodies, which may signal different conditions and/or directions. The output device 6054 may comprise one or more different display screens presenting various data and/or interfaces or controls for use by the patient. The output device 6054 may include graphics, which may be presented by a web-based interface and/or by a computer program or application (App.). In some embodiments, the patient interface 6050 may include functionality provided by or similar to existing voice-based assistants such as Siri by Apple, Alexa by Amazon, Google Assistant, or Bixby by Samsung.

[1334] As is generally illustrated in FIG. 81, the patient interface 6050 includes a second communication interface 6056, which may also be called a remote communication interface configured to communicate with the server 6030 and/or the clinician interface 6020 via a second network 6058. In some embodiments, the second network 6058 may include a local area network (LAN), such as an Ethernet network. In some embodiments, the second network 6058 may include the Internet, and communications between the patient interface 6050 and the server 6030 and/or the clinician interface 6020 may be secured via encryption, such as, for example, by using a virtual private network (VPN). In some embodiments, the second network 6058 may include wired and/or wireless network connections such as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC), cellular data network, etc. In some embodiments, the second network 6058 may be the same as and/or operationally coupled to the first network 6034.

[1335] The patient interface 6050 includes a second processor 6060 and a second machine-readable storage memory 6062 holding second instructions 6064 for execution by the second processor 6060 for performing various actions of patient interface 6050. The second machine-readable storage memory 6062 also includes a local data store 6066 configured to hold data, such as data pertaining to a treatment plan and/or patient data, such as data representing a patient’s performance within a treatment plan. The patient interface 6050 also includes a local communication interface 6068 configured to communicate with various devices for use by the patient in the vicinity of the patient interface 6050. The local communication interface 6068 may include wired and/or wireless communications. In some embodiments, the local communication interface 6068 may include a local wireless network such as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC), cellular data network, etc.

[1336] The system 6010 also includes a treatment device 6070 configured to be manipulated by the patient and/or to manipulate a body part of the patient for performing activities according to the treatment plan. In some embodiments, the treatment device 6070 may take the form of an exercise and rehabilitation apparatus configured to perform and/or to aid in the performance of a rehabilitation regimen, which may be an orthopedic rehabilitation regimen, and the treatment includes rehabilitation of a body part of the patient, such as a joint or a bone or a muscle group. The treatment device 6070 may be any suitable medical, rehabilitative, therapeutic, etc. apparatus configured to be controlled distally via another computing device to treat a patient and/or exercise the patient. The treatment device 6070 may be an electromechanical machine including one or more weights, an electromechanical bicycle, an electromechanical spin-wheel, a smart-mirror, a treadmill, an interactive environment system, or the like. The body part may include, for example, a spine, a hand, a foot, a knee, or a shoulder. The body part may include a part of a joint, a bone, or a muscle group, such as one or more vertebrae, a tendon, or a ligament. As is generally illustrated in FIG. 81, the treatment device 6070 includes a controller 6072, which may include one or more processors, computer memory, and/or other components. The treatment device 6070 also includes a fourth communication interface 6074 configured to communicate with the patient interface 6050 via the local communication interface 6068. The treatment device 6070 also includes one or more internal sensors 6076 and an actuator 6078, such as a motor. The actuator 6078 may be used, for example, for moving the patient’s body part and/or for resisting forces by the patient.

[1337] The internal sensors 6076 may measure one or more operating characteristics of the treatment device 6070 such as, for example, a force, a position, a speed, and/or a velocity. In some embodiments, the internal sensors 6076 may include a position sensor configured to measure at least one of a linear motion or an angular motion of a body part of the patient. For example, an internal sensor 6076 in the form of a position sensor may measure a distance that the patient is able to move a part of the treatment device 6070, where such distance may correspond to a range of motion that the patient’s body part is able to achieve. In some embodiments, the internal sensors 6076 may include a force sensor configured to measure a force applied by the patient. For example, an internal sensor 6076 in the form of a force sensor may measure a force or weight the patient is able to apply, using a particular body part, to the treatment device 6070.

[1338] The system 6010 generally illustrated in FIG. 81 also includes an ambulation sensor 6082, which communicates with the server 6030 via the local communication interface 6068 of the patient interface 6050. The ambulation sensor 6082 may track and store a number of steps taken by the patient. In some embodiments, the ambulation sensor 6082 may take the form of a wristband, wristwatch, or smart watch. In some embodiments, the ambulation sensor 6082 may be integrated within a phone, such as a smartphone.

[1339] The system 6010 generally illustrated in FIG. 81 also includes a goniometer 6084, which communicates with the server 6030 via the local communication interface 6068 of the patient interface 6050. The goniometer 6084 measures an angle of the patient’s body part. For example, the goniometer 6084 may measure the angle of flex of a patient’s knee or elbow or shoulder.

[1340] The system 6010 generally illustrated in FIG. 81 also includes a pressure sensor 6086, which communicates with the server 6030 via the local communication interface 6068 of the patient interface 6050. The pressure sensor 6086 measures an amount of pressure or weight applied by a body part of the patient. For example, pressure sensor 6086 may measure an amount of force applied by a patient’s foot when pedaling a stationary bike.

[1341] The system 6010 generally illustrated in FIG. 81 also includes a supervisory interface 6090 which may be similar or identical to the clinician interface 6020. In some embodiments, the supervisory interface 6090 may have enhanced functionality beyond what is provided on the clinician interface 6020. The supervisory interface 6090 may be configured for use by a person having responsibility for the treatment plan, such as an orthopedic surgeon.

[1342] The system 6010 generally illustrated in FIG. 81 also includes a reporting interface 6092 which may be similar or identical to the clinician interface 6020. In some embodiments, the reporting interface 6092 may have less functionality from what is provided on the clinician interface 6020. For example, the reporting interface 6092 may not have the ability to modify a treatment plan. Such a reporting interface 6092 may be used, for example, by a biller to determine the use of the system 6010 for billing purposes. In another example, the reporting interface 6092 may not have the ability to display patient identifiable information, presenting only pseudonymized data and/or anonymized data for certain data fields concerning a data subject and/or for certain data fields concerning a quasi-identifier of the data subject. Such a reporting interface 6092 may be used, for example, by a researcher to determine various effects of a treatment plan on different patients.

[1343] The system 6010 includes an assistant interface 6094 for a healthcare provider, such as those described herein, to remotely communicate with the patient interface 6050 and/or the treatment device 6070. Such remote communications may enable the healthcare provider to provide assistance or guidance to a patient using the system 6010. More specifically, the assistant interface 6094 is configured to communicate a telemedicine signal 6096, 6097, 6098a, 6098b, 6099a, 6099b with the patient interface 6050 via a network connection such as, for example, via the first network 6034 and/or the second network 6058.

[1344] The telemedicine signal 6096, 6097, 6098a, 6098b, 6099a, 6099b comprises one of an audio signal 6096, an audiovisual signal 6097, an interface control signal 6098a for controlling a function of the patient interface 6050, an interface monitor signal 6098b for monitoring a status of the patient interface 6050, an apparatus control signal 6099a for changing an operating parameter of the treatment device 6070, and/or an apparatus monitor signal 6099b for monitoring a status of the treatment device 6070. In some embodiments, each of the control signals 6098a, 6099a may be unidirectional, conveying commands from the assistant interface 6094 to the patient interface 50. In some embodiments, in response to successfully receiving a control signal 6098a, 6099a and/or to communicate successful and/or unsuccessful implementation of the requested control action, an acknowledgement message may be sent from the patient interface 6050 to the assistant interface 94.

[1345] In some embodiments, each of the monitor signals 6098b, 6099b may be unidirectional, status- information commands from the patient interface 6050 to the assistant interface 6094. In some embodiments, an acknowledgement message may be sent from the assistant interface 6094 to the patient interface 6050 in response to successfully receiving one of the monitor signals 6098b, 6099b.

[1346] In some embodiments, the patient interface 6050 may be configured as a pass-through for the apparatus control signals 6099a and the apparatus monitor signals 6099b between the treatment device 6070 and one or more other devices, such as the assistant interface 6094 and/or the server 6030. For example, the patient interface 6050 may be configured to transmit an apparatus control signal 6099a in response to an apparatus control signal 6099a within the telemedicine signal 6096, 6097, 6098a, 6098b, 6099a, 6099b from the assistant interface 6094.

[1347] In some embodiments, the assistant interface 6094 may be presented on a shared physical device as the clinician interface 6020. For example, the clinician interface 6020 may include one or more screens that implement the assistant interface 6094. Alternatively or additionally, the clinician interface 6020 may include additional hardware components, such as a video camera, a speaker, and/or a microphone, to implement aspects of the assistant interface 6094.

[1348] In some embodiments, one or more portions of the telemedicine signal 6096, 6097, 6098a, 6098b, 6099a, 6099b may be generated from a prerecorded source (e.g., an audio recording, a video recording, or an animation) for presentation by the output device 6054 of the patient interface 6050. For example, a tutorial video may be streamed from the server 6030 and presented upon the patient interface 6050. Content from the prerecorded source may be requested by the patient via the patient interface 6050. Alternatively, via a control on the assistant interface 6094, the healthcare provider may cause content from the prerecorded source to be played on the patient interface 6050.

[1349] The assistant interface 6094 includes an assistant input device 6022 and an assistant display 6024, which may be collectively called an assistant user interface 6022, 6024. The assistant input device 6022 may include one or more of a telephone, a keyboard, a mouse, a trackpad, or a touch screen, for example. Alternatively or additionally, the assistant input device 6022 may include one or more microphones. In some embodiments, the one or more microphones may take the form of a telephone handset, headset, or wide-area microphone or microphones configured for the healthcare provider to speak to a patient via the patient interface 50.

[1350] In some embodiments, assistant input device 6022 may be configured to provide voice-based functionalities, with hardware and/or software configured to interpret spoken instructions by the healthcare provider by using the one or more microphones. The assistant input device 6022 may include functionality provided by or similar to existing voice-based assistants such as Siri by Apple, Alexa by Amazon, Google Assistant, or Bixby by Samsung. The assistant input device 6022 may include other hardware and/or software components. The assistant input device 6022 may include one or more general purpose devices and/or special- purpose devices.

[1351] The assistant display 6024 may take one or more different forms including, for example, a computer monitor or display screen on a tablet, a smartphone, or a smart watch. The assistant display 6024 may include other hardware and/or software components such as projectors, virtual reality capabilities, or augmented reality capabilities, etc. The assistant display 6024 may incorporate various different visual, audio, or other presentation technologies. For example, the assistant display 6024 may include a non-visual display, such as an audio signal, which may include spoken language and/or other sounds such as tones, chimes, melodies, and/or compositions, which may signal different conditions and/or directions. The assistant display 6024 may comprise one or more different display screens presenting various data and/or interfaces or controls for use by the healthcare provider. The assistant display 6024 may include graphics, which may be presented by a web-based interface and/or by a computer program or application (App.).

[1352] In some embodiments, the system 6010 may provide computer translation of language from the assistant interface 6094 to the patient interface 6050 and/or vice-versa. The computer translation of language may include computer translation of spoken language and/or computer translation of text. Additionally or alternatively, the system 6010 may provide voice recognition and/or spoken pronunciation of text. For example, the system 6010 may convert spoken words to printed text and/or the system 6010 may audibly speak language from printed text. The system 6010 may be configured to recognize spoken words by any or all of the patient, the clinician, and/or the healthcare provider. In some embodiments, the system 6010 may be configured to recognize and react to spoken requests or commands by the patient. For example, in response to a verbal command by the patient (which may be given in any one of several different languages), the system 6010 may automatically initiate a telemedicine session.

[1353] In some embodiments, the server 6030 may generate aspects of the assistant display 6024 for presentation by the assistant interface 6094. For example, the server 6030 may include a web server configured to generate the display screens for presentation upon the assistant display 6024. For example, the artificial intelligence engine 6011 may generate recommended treatment plans and/or excluded treatment plans for patients and generate the display screens including those recommended treatment plans and/or external treatment plans for presentation on the assistant display 6024 of the assistant interface 6094. In some embodiments, the assistant display 6024 may be configured to present a virtualized desktop hosted by the server6030. In some embodiments, the server 6030 may be configured to communicate with the assistant interface 6094 via the first network 6034. In some embodiments, the first network 6034 may include a local area network (LAN), such as an Ethernet network.

[1354] In some embodiments, the first network 6034 may include the Internet, and communications between the server 6030 and the assistant interface 6094 may be seemed via privacy enhancing technologies, such as, for example, by using encryption over a virtual private network (VPN). Alternatively or additionally, the server 6030 may be configured to communicate with the assistant interface 6094 via one or more networks independent of the first network 6034 and/or other communication means, such as a direct wired or wireless communication channel. In some embodiments, the patient interface 6050 and the treatment device 6070 may each operate from a patient location geographically separate from a location of the assistant interface 6094. For example, the patient interface 6050 and the treatment device 6070 may be used as part of an in-home rehabilitation system, which may be aided remotely by using the assistant interface 6094 at a centralized location, such as a clinic or a call center.

[1355] In some embodiments, the assistant interface 6094 may be one of several different terminals (e.g., computing devices) that may be grouped together, for example, in one or more call centers or at one or more clinicians’ offices. In some embodiments, a plurality of assistant interfaces 6094 may be distributed geographically. In some embodiments, a person may work as a healthcare provider remotely from any conventional office infrastructure. Such remote work may be performed, for example, where the assistant interface 6094 takes the form of a computer and/or telephone. This remote work functionality may allow for work-from-home arrangements that may include part time and/or flexible work horns for a healthcare provider. [1356] FIGS. 82-83 show an embodiment of a treatment device 6070. More specifically, FIG. 82 generally illustrates a treatment device 6070 in the form of a stationary cycling machine 6100, which may be called a stationary bike, for short. The stationary cycling machine 6100 includes a set of pedals 6102 each attached to a pedal arm 6104 for rotation about an axle 6106. In some embodiments, and as is generally illustrated in FIG. 82, the pedals 6102 are movable on the pedal arms 6104 in order to adjust a range of motion used by the patient in pedaling. For example, the pedals being located inwardly toward the axle 6106 corresponds to a smaller range of motion than when the pedals are located outwardly away from the axle 6106. One or more pressure sensors 6086 is attached to or embedded within one or both of the pedals 6102 for measuring an amount of force applied by the patient on a pedal 6102. The pressure sensor 6086 may communicate wirelessly to the treatment device 6070 and/or to the patient interface 6050.

[1357] FIG. 84 generally illustrates a person (a patient) using the treatment device of FIG. 82 and showing sensors and various data parameters connected to a patient interface 6050. The example patient interface 6050 is a tablet computer or smartphone, or a phablet, such as an iPad, an iPhone, an Android device, or a Surface tablet, which is held manually by the patient. In some other embodiments, the patient interface 6050 may be embedded within or attached to the treatment device 6070.

[1358] FIG. 84 generally illustrates the patient wearing the ambulation sensor 6082 on his wrist, with a note showing “STEPS TODAY 1355”, indicating that the ambulation sensor 6082 has recorded and transmitted that step count to the patient interface 6050. FIG. 84 also generally illustrates the patient wearing the goniometer 6084 on his right knee, with a note showing “KNEE ANGLE 72°”, indicating that the goniometer 6084 is measuring and transmitting that knee angle to the patient interface 6050. FIG. 84 also generally illustrates a right side of one of the pedals 6102 with a pressure sensor 6086 showing “FORCE 12.5 lbs.,” indicating that the right pedal pressure sensor 6086 is measuring and transmitting that force measurement to the patient interface 6050. [1359] FIG. 84 also generally illustrates a left side of one of the pedals 6102 with a pressure sensor 6086 showing “FORCE 27 lbs.”, indicating that the left pedal pressure sensor 6086 is measuring and transmitting that force measurement to the patient interface 6050. FIG. 84 also generally illustrates other patient data, such as an indicator of “ SES SION TIME 0 : 04 : 13 ” , indicating that the patient has been using the treatment device 6070 for 4 minutes and 13 seconds. This session time may be determined by the patient interface 6050 based on information received from the treatment device 6070. FIG. 84 also generally illustrates an indicator showing “PAIN LEVEL 3”. Such a pain level may be obtained from the patent in response to a solicitation, such as a question, presented upon the patient interface 6050.

[1360] FIG. 85 is an example embodiment of an overview display 6120 of the assistant interface 6094. Specifically, the overview display 6120 presents several different controls and interfaces for the healthcare provider to remotely assist a patient with using the patient interface 6050 and/or the treatment device 6070. This remote assistance functionality may also be called telemedicine or telehealth.

[1361] Specifically, the overview display 6120 includes a patient profile display 6130 presenting biographical information regarding a patient using the treatment device 6070. The patient profile display 6130 may take the form of a portion or region of the overview display 6120, as is generally illustrated in FIG. 85, although the patient profile display 6130 may take other forms, such as a separate screen or a popup window. [1362] In some embodiments, the patient profile display 6130 may include a limited subset of the patient’s biographical information. More specifically, the data presented upon the patient profile display 6130 may depend upon the healthcare provider’s need for that information. For example, a healthcare provider that is assisting the patient with a medical issue may be provided with medical history information regarding the patient, whereas a technician troubleshooting an issue with the treatment device 6070 may be provided with a much more limited set of information regarding the patient. The technician, for example, may be given only the patient’s name.

[1363] The patient profile display 6130 may include pseudonymized data and/or anonymized data or use any privacy enhancing technology to prevent confidential patient data from being communicated in a way that could violate patient confidentiality requirements. Such privacy enhancing technologies may enable compliance with laws, regulations, or other rules of governance such as, but not limited to, the Health Insurance Portability and Accountability Act (HIPAA), or the General Data Protection Regulation (GDPR), wherein the patient may be deemed a “data subject”.

[1364] In some embodiments, the patient profile display 6130 may present information regarding the treatment plan for the patient to follow in using the treatment device 6070. Such treatment plan information may be limited to a healthcare provider. For example, a healthcare provider assisting the patient with an issue regarding the treatment regimen may be provided with treatment plan information, whereas a technician troubleshooting an issue with the treatment device 6070 may not be provided with any information regarding the patient’s treatment plan.

[1365] In some embodiments, one or more recommended treatment plans and/or excluded treatment plans may be presented in the patient profile display 6130 to the healthcare provider. The one or more recommended treatment plans and/or excluded treatment plans may be generated by the artificial intelligence engine 6011 of the server 6030 and received from the server 6030 in real-time during a telemedicine or telehealth session. An example of presenting the one or more recommended treatment plans and/or ruled-out treatment plans is described below with reference to FIG. 87.

[1366] The example overview display 6120 generally illustrated in FIG. 85 also includes a patient status display 6134 presenting status information regarding a patient using the treatment device. The patient status display 6134 may take the form of a portion or region of the overview display 6120, as is generally illustrated in FIG. 85, although the patient status display 6134 may take other forms, such as a separate screen or a popup window.

[1367] The patient status display 6134 includes sensor data 6136 from one or more of the external sensors 6082, 6084, 6086, and/or from one or more internal sensors 6076 of the treatment device 6070. In some embodiments, the patient status display 6134 may include sensor data from one or more sensors of one or more wearable devices worn by the patient while using the treatment device 6070. The one or more wearable devices may include a watch, a bracelet, a necklace, a chest strap, and the like. The one or more wearable devices may be configured to monitor a heartrate, a temperature, a blood pressure, one or more vital signs, and the like of the patient while the patient is using the treatment device 6070. In some embodiments, the patient status display 6134 may present other data 6138 regarding the patient, such as last reported pain level, or progress within a treatment plan.

[1368] User access controls may be used to limit access, including what data is available to be viewed and/or modified, on any or all of the user interfaces 6020, 6050, 6090, 6092, 6094 of the system 6010. In some embodiments, user access controls may be employed to control what information is available to any given person using the system 6010. For example, data presented on the assistant interface 6094 may be controlled by user access controls, with permissions set depending on the healthcare provider/user’s need for and/or qualifications to view that information.

[1369] The example overview display 6120 generally illustrated in FIG. 85 also includes a help data display 6140 presenting information for the healthcare provider to use in assisting the patient. The help data display 6140 may take the form of a portion or region of the overview display 6120, as is generally illustrated in FIG. 85. The help data display 6140 may take other forms, such as a separate screen or a popup window. The help data display 6140 may include, for example, presenting answers to frequently asked questions regarding use of the patient interface 6050 and/or the treatment device 6070.

[1370] The help data display 6140 may also include research data or best practices. In some embodiments, the help data display 6140 may present scripts for answers or explanations in response to patient questions. In some embodiments, the help data display 6140 may present flow charts or walk-throughs for the healthcare provider to use in determining a root cause and/or solution to a patient’s problem.

[1371] In some embodiments, the assistant interface 94 may present two or more help data displays 6140, which may be the same or different, for simultaneous presentation of help data for use by the healthcare provider for example, a first help data display may be used to present a troubleshooting flowchart to determine the source of a patient’s problem, and a second help data display may present script information for the healthcare provider to read to the patient, such information to preferably include directions for the patient to perform some action, which may help to narrow down or solve the problem. In some embodiments, based upon inputs to the troubleshooting flowchart in the first help data display, the second help data display may automatically populate with script information.

[1372] The example overview display 6120 generally illustrated in FIG. 85 also includes a patient interface control 6150 presenting information regarding the patient interface 6050, and/or to modify one or more settings of the patient interface 6050. The patient interface control 6150 may take the form of a portion or region of the overview display 6120, as is generally illustrated in FIG. 85. The patient interface control 6150 may take other forms, such as a separate screen or a popup window. The patient interface control 6150 may present information communicated to the assistant interface 6094 via one or more of the interface monitor signals 6098b.

[1373] As is generally illustrated in FIG. 85, the patient interface control 6150 includes a display feed 6152 of the display presented by the patient interface 6050. In some embodiments, the display feed 6152 may include a live copy of the display screen currently being presented to the patient by the patient interface 6050. In other words, the display feed 6152 may present an image of what is presented on a display screen of the patient interface 6050.

[1374] In some embodiments, the display feed 6152 may include abbreviated information regarding the display screen currently being presented by the patient interface 50, such as a screen name or a screen number. The patient interface control 6150 may include a patient interface setting control 6154 for the healthcare provider to adjust or to control one or more settings or aspects of the patient interface 6050. In some embodiments, the patient interface setting control 6154 may cause the assistant interface 6094 to generate and/or to transmit an interface control signal 6098 for controlling a function or a setting of the patient interface 6050.

[1375] In some embodiments, the patient interface setting control 6154 may include collaborative browsing or co-browsing capability for the healthcare provider to remotely view and/or control the patient interface 6050. For example, the patient interface setting control 6154 may enable the healthcare provider to remotely enter text to one or more text entry fields on the patient interface 6050 and/or to remotely control a cursor on the patient interface 50 using a mouse or touchscreen of the assistant interface 6094.

[1376] In some embodiments, using the patient interface 6050, the patient interface setting control 6154 may allow the healthcare provider to change a setting that cannot be changed by the patient. For example, the patient interface 6050 may be precluded from accessing a language setting to prevent a patient from inadvertently switching, on the patient interface 6050, the language used for the displays, whereas the patient interface seting control 6154 may enable the healthcare provider to change the language setting of the patient interface 6050. In another example, the patient interface 6050 may not be able to change a font size seting to a smaller size in order to prevent a patient from inadvertently switching the font size used for the displays on the patient interface 6050 such that the display would become illegible to the patient, whereas the patient interface seting control 6154 may provide for the healthcare provider to change the font size seting of the patient interface 6050.

[1377] The example overview display 6120 generally illustrated in FIG. 85 also includes an interface communications display 6156 showing the status of communications between the patient interface 6050 and one or more other devices 6070, 6082, 6084, such as the treatment device 6070, the ambulation sensor 6082, and/or the goniometer 6084. The interface communications display 6156 may take the form of a portion or region of the overview display 6120, as is generally illustrated in FIG. 85.

[1378] The interface communications display 6156 may take other forms, such as a separate screen or a popup window. The interface communications display 6156 may include controls for the healthcare provider to remotely modify communications with one or more of the other devices 6070, 6082, 6084. For example, the healthcare provider may remotely command the patient interface 6050 to reset communications with one of the other devices 6070, 6082, 6084, or to establish communications with a new one of the other devices 6070, 6082, 6084. This functionality may be used, for example, where the patient has a problem with one of the other devices 6070, 6082, 6084, or where the patient receives a new or a replacement one of the other devices 6070, 6082, 6084.

[1379] The example overview display 6120 generally illustrated in FIG. 85 also includes an apparatus control 6160 for the healthcare provider to view and/or to control information regarding the treatment device 6070. The apparatus control 6160 may take the form of a portion or region of the overview display 6120, as is generally illustrated in FIG. 85. The apparatus control 6160 may take other forms, such as a separate screen or a popup window. The apparatus control 6160 may include an apparatus status display 6162 with information regarding the current status of the apparatus. The apparatus status display 6162 may present information communicated to the assistant interface 6094 via one or more of the apparatus monitor signals 6099b. The apparatus status display 6162 may indicate whether the treatment device 6070 is currently communicating with the patient interface 6050. The apparatus status display 6162 may present other current and/or historical information regarding the status of the treatment device 6070.

[1380] The apparatus control 6160 may include an apparatus setting control 6164 for the healthcare provider to adjust or control one or more aspects of the treatment device 6070. The apparatus seting control 6164 may cause the assistant interface 6094 to generate and/or to transmit an apparatus control signal 6099 (e.g., which may be referred to as treatment plan input, as described) for changing an operating parameter and/or one or more characteristics of the treatment device 6070, (e.g., a pedal radius seting, a resistance seting, a target RPM, other suitable characteristics of the treatment device 6070, or a combination thereof).

[1381] The apparatus seting control 6164 may include a mode buton 6166 and a position control 6168, which may be used in conjunction for the healthcare provider to place an actuator 6078 of the treatment device 6070 in a manual mode, after which a seting, such as a position or a speed of the actuator 6078, can be changed using the position control 6168. The mode buton 6166 may provide for a seting, such as a position, to be toggled between automatic and manual modes. [1382] In some embodiments, one or more setings may be adjustable at any time, and without having an associated auto/manual mode. In some embodiments, the healthcare provider may change an operating parameter of the treatment device 6070, such as a pedal radius setting, while the patient is actively using the treatment device 6070. Such “on the fly” adjustment may or may not be available to the patient using the patient interface 6050.

[1383] In some embodiments, the apparatus seting control 6164 may allow the healthcare provider to change a seting that cannot be changed by the patient using the patient interface 6050. For example, the patient interface 6050 may be precluded from changing a preconfigured seting, such as a height or a tilt setting of the treatment device 6070, whereas the apparatus seting control 6164 may provide for the healthcare provider to change the height or tilt seting of the treatment device 6070.

[1384] The example overview display 6120 generally illustrated in FIG. 85 also includes a patient communications control 6170 for controlling an audio or an audiovisual communications session with the patient interface 6050. The communications session with the patient interface 6050 may comprise a live feed from the assistant interface 6094 for presentation by the output device of the patient interface 6050. The live feed may take the form of an audio feed and/or a video feed. In some embodiments, the patient interface 6050 may be configured to provide two-way audio or audiovisual communications with a person using the assistant interface 6094. Specifically, the communications session with the patient interface 6050 may include bidirectional (two- way) video or audiovisual feeds, with each of the patient interface 6050 and the assistant interface 6094 presenting video of the other one.

[1385] In some embodiments, the patient interface 6050 may present video from the assistant interface 6094, while the assistant interface 6094 presents only audio or the assistant interface 6094 presents no live audio or visual signal from the patient interface 6050. In some embodiments, the assistant interface 6094 may present video from the patient interface 6050, while the patient interface 6050 presents only audio or the patient interface 6050 presents no live audio or visual signal from the assistant interface 6094.

[1386] In some embodiments, the audio or an audiovisual communications session with the patient interface 6050 may take place, at least in part, while the patient is performing the rehabilitation regimen upon the body part. The patient communications control 6170 may take the form of a portion or region of the overview display 6120, as is generally illustrated in FIG. 85. The patient communications control 6170 may take other forms, such as a separate screen or a popup window.

[1387] The audio and/or audiovisual communications may be processed and/or directed by the assistant interface 6094 and/or by another device or devices, such as a telephone system, or a videoconferencing system used by the healthcare provider while the healthcare provider uses the assistant interface 6094. Alternatively or additionally, the audio and/or audiovisual communications may include communications with a third party. For example, the system 6010 may enable the healthcare provider to initiate a 3-way conversation regarding use of a particular piece of hardware or software, with the patient and a subject mater expert, such as a healthcare provider or a specialist. The example patient communications control 6170 generally illustrated in FIG. 85 includes call controls 6172 for the healthcare provider to use in managing various aspects of the audio or audiovisual communications with the patient. The call controls 6172 include a disconnect buton 6174 for the healthcare provider to end the audio or audiovisual communications session. The call controls 6172 also include a mute buton 6176 to temporarily silence an audio or audiovisual signal from the assistant interface 6094. In some embodiments, the call controls 6172 may include other features, such as a hold buton (not shown). [1388] The call controls 6172 also include one or more record/playback controls 6178, such as record, play, and pause butons to control, with the patient interface 6050, recording and/or playback of audio and/or video from the teleconference session. The call controls 6172 also include a video feed display 6180 for presenting still and/or video images from the patient interface 6050, and a self-video display 6182 showing the current image of the healthcare provider using the assistant interface 6094. The self-video display 6182 may be presented as a picture-in-picture format, within a section of the video feed display 6180, as is generally illustrated in FIG. 85. Alternatively or additionally, the self-video display 6182 may be presented separately and/or independently from the video feed display 6180.

[1389] The example overview display 6120 generally illustrated in FIG. 85 also includes a third party communications control 6190 for use in conducting audio and/or audiovisual communications with a third party . The third party communications control 6190 may take the form of a portion or region of the overview display 6120, as is generally illustrated in FIG. 85. The third party communications control 6190 may take other forms, such as a display on a separate screen or a popup window.

[1390] The third party communications control 6190 may include one or more controls, such as a contact list and/or butons or controls to contact a third party regarding use of a particular piece of hardware or software, e.g., a subject mater expert, such as a healthcare provider or a specialist. The third party communications control 6190 may include conference calling capability for the third party to simultaneously communicate with both the healthcare provider via the assistant interface 6094, and with the patient via the patient interface 6050. For example, the system 6010 may provide for the healthcare provider to initiate a 3 -way conversation with the patient and the third party.

[1391] FIG. 86 generally illustrates an example block diagram of training a machine learning model 6013 to output, based on data 6600 pertaining to the patient, a treatment plan 6602 for the patient according to the present disclosure. Data pertaining to other patients may be received by the server 6030. The other patients may have used various treatment devices to perform treatment plans.

[1392] The data may include characteristics of the other patients, the details of the treatment plans performed by the other patients, and/or the results of performing the treatment plans (e.g., a percent of recovery of a portion of the patients’ bodies, an amount of recovery of a portion of the patients’ bodies, an amount of increase or decrease in muscle strength of a portion of patients’ bodies, an amount of increase or decrease in range of motion of a portion of patients’ bodies, etc.).

[1393] As depicted, the data has been assigned to different cohorts. Cohort A includes data for patients having similar first characteristics, first treatment plans, and first results. Cohort B includes data for patients having similar second characteristics, second treatment plans, and second results. For example, cohort A may include first characteristics of patients in their twenties without any medical conditions who underwent surgery for a broken limb; their treatment plans may include a certain treatment protocol (e.g., use the treatment device 6070 for 30 minutes 5 times a week for 3 weeks, wherein values for the properties, configurations, and/or setings of the treatment device 70 are set to X (where X is a numerical value) for the first two weeks and to Y (where Y is a numerical value) for the last week). [1394] Cohort A and cohort B may be included in a training dataset used to train the machine learning model 6013. The machine learning model 6013 may be trained to match a pattern between characteristics for each cohort and output the treatment plan or a variety of possible treatment plans for selectionby a healthcare provider that provides the result. Accordingly, when the data 6600 for a new patient is input into the trained machine learning model 6013, the trained machine learning model 6013 may match the characteristics included in the data 6600 with characteristics in either cohort A or cohort B and output the appropriate treatment plan or plans 6602. In some embodiments, the machine learning model 6013 may be trained to output one or more excluded treatment plans that should not be performed by the new patient.

[1395] FIG. 87 generally illustrates an embodiment of an overview display 6120 of the assistant interface 6094 presenting recommended treatment plans and excluded treatment plans in real-time during a telemedicine session according to the present disclosure. As depicted, the overview display 6120 just includes sections for the patient profile 6130 and the video feed display 6180, including the self-video display 6182. Any suitable configuration of controls and interfaces of the overview display 6120 described with reference to FIG. 85 may be presented in addition to or instead of the patient profile 6130, the video feed display 6180, and the self-video display 6182.

[1396] The healthcare provider using the assistant interface 6094 (e.g., computing device) during the telemedicine session may be presented in the self-video 6182 in a portion of the overview display 6120 (e.g., user interface presented on a display screen 6024 of the assistant interface 6094) that also presents a video from the patient in the video feed display 6180. Further, the video feed display 6180 may also include a graphical user interface (GUI) object 6700 (e.g., a button) that enables the healthcare provider to share, in real-time or near real-time during the telemedicine session, the recommended treatment plans and/or the excluded treatment plans with the patient on the patient interface 6050. The healthcare provider may select the GUI object 6700 to share the recommended treatment plans and/or the excluded treatment plans. As depicted, another portion of the overview display 6120 includes the patient profile display 6130.

[1397] The patient profile display 6130 is presenting two example recommended treatment plans 6600 and one example excluded treatment plan 6602. As described herein, the treatment plans may be recommended in view of characteristics of the patient being treated. To generate the recommended treatment plans 6600 the patient should follow to achieve a desired result, a pattern between the characteristics of the patient being treated and a cohort of other people who have used the treatment device 6070 to perform a treatment plan may be matched by one or more machine learning models 6013 of the artificial intelligence engine 6011. Each of the recommended treatment plans may be generated based on different desired results.

[1398] For example, as depicted, the patient profile display 6130 presents “The characteristics of the patient match characteristics of uses in Cohort A. The following treatment plans are recommended for the patient based on his characteristics and desired results.” Then, the patient profile display 6130 presents recommended treatment plans from cohort A, and each treatment plan provides different results.

[1399] As depicted, treatment plan “A” indicates “Patient X should use treatment device for 30 minutes a day for 4 days to achieve an increased range of motion of Y%; Patient X has Type 2 Diabetes; and Patient X should be prescribed medication Z for pain management during the treatment plan (medication Z is approved for people having Type 2 Diabetes).” Accordingly, the treatment plan generated achieves increasing the range of motion of Y%. As may be appreciated, the treatment plan also includes a recommended medication (e.g., medication Z) to prescribe to the patient to manage pain in view of a known medical disease (e.g., Type 2 Diabetes) of the patient. That is, the recommended patient medication not only does not conflict with the medical condition of the patient but thereby improves the probability of a superior patient outcome. This specific example and all such examples elsewhere herein are not intended to limit in any way the generated treatment plan from recommending multiple medications, or from handling the acknowledgement, view, diagnosis and/or treatment of comorbid conditions or diseases.

[1400] Recommended treatment plan “B” may specify, based on a different desired result of the treatment plan, a different treatment plan including a different treatment protocol for a treatment device, a different medication regimen, etc.

[1401] As depicted, the patient profile display 6130 may also present the excluded treatment plans 6602. These types of treatment plans are shown to the healthcare provider using the assistant interface 6094 to alert the healthcare provider not to recommend certain portions of a treatment plan to the patient. For example, the excluded treatment plan could specify the following: “Patient X should not use treatment device for longer than 30 minutes a day due to a heart condition; Patient X has Type 2 Diabetes; and Patient X should not be prescribed medication M for pain management during the treatment plan (in this scenario, medication M can cause complications for people having Type 2 Diabetes). Specifically, the excluded treatment plan points out a limitation of a treatment protocol where, due to a heart condition, Patient X should not exercise for more than 30 minutes a day. The mled-out treatment plan also points out that Patient X should not be prescribed medication M because it conflicts with the medical condition Type 2 Diabetes.

[1402] The healthcare provider may select the treatment plan for the patient on the overview display 6120. For example, the healthcare provider may use an input peripheral (e.g., mouse, touchscreen, microphone, keyboard, etc.) to select from the treatment plans 6600 for the patient. In some embodiments, during the telemedicine session, the healthcare provider may discuss the pros and cons of the recommended treatment plans 6600 with the patient.

[1403] In any event, the healthcare provider may select the treatment plan for the patient to follow to achieve the desired result. The selected treatment plan may be transmitted to the patient interface 6050 for presentation. The patient may view the selected treatment plan on the patient interface 6050. In some embodiments, the healthcare provider and the patient may discuss during the telemedicine session the details (e.g., treatment protocol using treatment device 6070, diet regimen, medication regimen, etc.) in real-time or in near real-time. In some embodiments, the server 6030 may control, based on the selected treatment plan and during the telemedicine session, the treatment device 6070 as the user uses the treatment device 6070.

[1404] FIG. 88 generally illustrates an embodiment of the overview display 6120 of the assistant interface 6094 presenting, in real-time during a telemedicine session, recommended treatment plans that have changed as a result of patient data changing according to the present disclosure. As may be appreciated, the treatment device 6070 and/or any computing device (e.g., patient interface 6050) may transmit data while the patient uses the treatment device 6070 to perform a treatment plan. The data may include updated characteristics of the patient and/or other treatment data. For example, the updated characteristics may include new performance information and/or measurement information. The performance information may include a speed of a portion of the treatment device 6070, a range of motion achieved by the patient, a force exerted on a portion of the treatment device 6070, a heartrate of the patient, a blood pressure of the patient, a respiratory rate of the patient, and so forth. [1405] In some embodiments, the data received at the server 6030 may be input into the trained machine learning model 6013, which may determine that the characteristics indicate the patient is on track for the current treatment plan. Determining the patient is on track for the current treatment plan may cause the trained machine learning model 6013 to adjust a parameter of the treatment device 6070. The adjustment may be based on a next step of the treatment plan to further improve the performance of the patient.

[1406] In some embodiments, the data received at the server 6030 may be input into the trained machine learning model 6013, which may determine that the characteristics indicate the patient is not on track (e.g., behind schedule, not able to maintain a speed, not able to achieve a certain range of motion, is in too much pain, etc.) for the current treatment plan or is ahead of schedule (e.g., exceeding a certain speed, exercising longer than specified with no pain, exerting more than a specified force, etc.) for the current treatment plan.

[1407] The trained machine learning model 6013 may determine that the characteristics of the patient no longer match the characteristics of the patients in the cohort to which the patient is assigned. Accordingly, the trained machine learning model 6013 may reassign the patient to another cohort that includes qualifying characteristics the patient’s characteristics. As such, the trained machine learning model 6013 may select a new treatment plan from the new cohort and control, based on the new treatment plan, the treatment device 6070. [1408] In some embodiments, prior to controlling the treatment device 6070, the server 6030 may provide the new treatment plan 6800 to the assistant interface 6094 for presentation in the patient profile 6130. As depicted, the patient profile 6130 indicates “The characteristics of the patient have changed and now match characteristics of uses in Cohort B. The following treatment plan is recommended for the patient based on his characteristics and desired results.” Then, the patient profile 6130 presents the new treatment plan 6800 (“Patient X should use the treatment device for 10 minutes a day for 3 days to achieve an increased range of motion of L%.” The healthcare provider may select the new treatment plan 6800, and the server 630 may receive the selection. The server 6030 may control the treatment device 6070 based on the new treatment plan 6800. In some embodiments, the new treatment plan 6800 may be transmitted to the patient interface 6050 such that the patient may view the details of the new treatment plan 6800.

[1409] In some embodiments, the server 6030 may be configured to receive treatment data pertaining to a user who uses a treatment device 6070 to perform a treatment plan. The user may include a patient, user, or person using the treatment device to perform various exercises. The treatment data may include various characteristics of the user, various measurement information pertaining to the user while the user uses the treatment device 6070, various characteristics of the treatment device 6070, the treatment plan, other suitable data, or a combination thereof. In some embodiments, the systems and methods described herein may be configured to receive the treatment data during a telemedicine session.

[1410] In some embodiments, while the user uses the treatment device 6070 to perform the treatment plan, at least some of the treatment data may include the sensor data 6136 from one or more of the external sensors 6082, 6084, 6086, and/or from one or more internal sensors 6076 of the treatment device 6070. Any sensor referred to herein may be standalone, part of a neural net, a node on the Internet of Things, or otherwise connected or configured to be connected to a physical or wireless network. In some embodiments, at least some of the treatment data may include sensor data from one or more sensors of one or more wearable devices worn by the patient while using the treatment device 6070. The one or more wearable devices may include a watch, a bracelet, a necklace, a headband, a wristband, an ankle band, eyeglasses or eyewear (such as, without limitation, Google Glass) a chest or torso strap, a device configured to be work on, attached to, or communicatively coupled to a body, and the like. While the patient is using the treatment device 6070, the one or more wearable devices may be configured to monitor, with respect to the patient, a heartrate, a temperature, a blood pressure, eye dilation, one or more vital signs, one or more metabolic markers, biomarkers, and the like.

[1411] The various characteristics of the treatment device 6070 may include one or more settings of the treatment device 6070, a current revolutions per time period (e.g., such as one minute) of a rotating member (e.g., such as a wheel) of the treatment device 6070, a resistance setting of the treatment device 6070, other suitable characteristics of the treatment device 6070, or a combination thereof. The measurement information may include one or more vital signs of the user, a respiration rate of the user, a heartrate of the user, a temperature of the user, a blood pressure of the user, other suitable measurement information of the user, or a combination thereof.

[1412] In some embodiments, the server 6030 may be configured to identify at least one enhanced component using the treatment data. In some embodiments, the server 6030 may be configured to identify the at least one enhanced component using a database, such as the database 6044 or in another suitable database, that correlates enhanced components with corresponding treatment plans. For example, a treatment plan may include various enhanced components. A relationship between the various enhanced components and the treatment plan may be indicated in the database 6044 or in another suitable database. Additionally, or alternatively, the treatment plan may indicate various enhanced components to use with the treatment plan. Additionally, or alternatively, the user may indicate one or more desired enhanced components for the treatment plan. A relationship between the one or more desired enhanced components and the treatment plan may be indicated in the database 44 or in another suitable database.

[1413] In some embodiments, the server 6030 may be configured to generate an enhanced environment using the at least one enhanced component and the treatment plan. The enhanced environment may be configured to enhance the experience perceived by the user while the user uses the treatment device 6070 to perform the treatment plan. For example, the enhanced environment may present to the user, while the user uses the treatment device 6070 to perform the treatment plan, various aspects that include, without limitation, one or more of the following: images, video, sound, tactile feedback, haptic feedback, and/or the like. The enhanced environment may be configured to perform, while the user uses the treatment device 6070 to perform the treatment plan, one or more of the following actions: encourage the user, focus the user, inform the user, distract the user, or otherwise assist the user in performing the treatment plan.

[1414] In some embodiments, the server 6030 may be configured to output at least one aspect of the enhanced environment to an interface configured to communicate with the treatment device 6070. In some embodiments, the server 6030 may be configured to output, during a telemedicine session, the enhanced environment to the interface. The interface may include at least one enhanced reality device configured to present, while the user uses the treatment device 6070 and based on the outputted enhanced environment, the enhanced environment to the user. The at least one enhanced reality device may include an augmented reality device, a virtual reality device, a mixed reality device, an immersive reality device, or a combination thereof. [1415] The augmented reality device may include one or more speakers, one or more wearable devices (e.g., goggles, gloves, shoes, body coverings, mechanical devices, helmets, and the like), one or more restraints, a seat, a body halo, one or more controllers, one or more interactive positioning devices, other suitable augmented reality devices, or a combination thereof. For example, the augmented reality device may include one or more displays interactively coupled to one or more speakers.

[1416] The virtual reality device may include one or more displays, one or more speakers, one or more wearable devices (e.g., goggles, gloves, shoes, body coverings, mechanical devices, helmets, and the like), one or more restraints, a seat, a body halo, one or more controllers, one or more interactive positioning devices, other suitable virtual reality devices, or a combination thereof. The mixed reality device may include a combination of one or more augmented reality devices and one or more virtual reality devices. The immersion reality device may include a combination of a plurality of virtual reality devices. For example, the immersion reality device may include a system comprising one or more screens (e.g., disposed in or on a wearable device, such as goggles, a mask, and the like), one or more audio output devices (e.g., such as speakers or other suitable audio output devices disposed in or on a wearable device, such as headphones, or disposed in an environment proximate the user), one or more wearable tactile or haptic feedback devices (e.g., gloves, shirts, pants, shoes, hoods, hats, and the like), one or more restraints (e.g., a body halo or other restraint), a seat, a treadmill, or other suitable virtual reality devices.

[1417] In some embodiments, the at least one enhanced reality device may communicate or interact with the treatment device 6070. For example, the at least one enhanced reality device may communicate with the treatment device 6070 via a wired or wireless connection, such as those described herein. The at least one enhanced reality device may send a signal to the treatment device 6070 to modify characteristics of the treatment device 6070 based on the at least one enhanced component and/or the enhanced environment. The controller 6072 of the treatment device 6070 may receive the signal. The controller 6072 may selectively modify characteristics of the treatment device 6070 based on the signal.

[1418] In some embodiments, the server 6030 may be configured to receive, while the user engages in the enhanced environment while using the treatment device 70 to perform the treatment plan, subsequent treatment data pertaining to the user. The server 6030 may modify, using the subsequent treatment data, the enhanced environment, at least one aspect of the treatment plan, or a combination thereof. For example, the server 6030 may determine whether the subsequent data indicates that the enhanced environment and/or the treatment plan are having a desired effect (e.g., not achieving the desired effect or a portion of the desired effect), as described. The server 6030 may modify, in response to determining that the enhanced environment and/or the treatment plan are not having the desired effect, the enhanced environment, at least one aspect of the treatment plan, or a combination thereof, to attempt to achieve the desired effect or, if not possible, to achieve some portion of or degree of the desired effect.

[1419] In some embodiments, the server 6030 may modify, using the subsequent treatment data while the user uses the treatment device 6070 during a telemedicine session, the enhanced environment, the at least one aspect of the treatment plan, any other aspect of the treatment plan, or a combination thereof.

[1420] In some embodiments, the server 6030 may determine that the enhanced environment and/or the treatment plan are having the desired effect and may modify the enhanced environment, at least one aspect of the treatment plan, or a combination thereof, to push the user or to achieve an alternative desired effect or at least a portion of the alternative desired effect (e.g., server 6030 may determine that the user is capable of handling a more rigorous treatment plan and that the user may benefit from the more rigorous treatment plan). [1421] In some embodiments, the server 6030 may control, while the user uses the treatment device 6070, the treatment device 6070. For example, the server 6030 may determine to that one or more characteristics of the treatment device 6070 should be modified based on the enhanced environment, the modified at least one of the enhanced environment and the at least one aspect of the treatment plan, or a combination thereof. The server 6030 may communicate a signal to the controller 6072 of the treatment device 6070 indicating the modifications to the one or more characteristics of the treatment device 6070. The controller 6072 may modify the one or more characteristics of the treatment device 6070 based on the signal.

[1422] In some embodiments, using the treatment data, the server 6030 may generate treatment information. The treatment information may include a formatted summary of the performance of the treatment plan indicated by the treatment data while the user uses the treatment device 6070, such that the treatment data is presentable at a computing device of a healthcare provider responsible for the performance of the treatment plan by the user. In some embodiments, the patient profile display 6120 may include and/or display the treatment information. [1423] The server 6030 may be configured to provide, at the overview display 6120, the treatment information. For example, the server 6030 may store the treatment information for access by the overview display 6120 and/or communicate the treatment information to the overview display 6120. In some embodiments, the server 6030 may provide the treatment information to patient profile display 6130, to another suitable section, portion, or component of the overview display 6120 or to any other suitable display or interface . [1424] In some embodiments, the healthcare provider assisting the patient while using the treatment device 6070 may review the treatment information and determine whether to modify the enhanced environment, at least one aspect of the treatment plan, and/or one or more characteristics of the treatment device 6070. For example, the healthcare provider may review the treatment information and compare the treatment information to the treatment plan being performed by the patient.

[1425] While the patient uses the treatment device 6070, the healthcare provider may compare one or more parts or portions of expected information pertaining to the patient’s ability to perform the treatment plan with one or more corresponding parts or portions of the measurement information (e.g., indicated by the treatment information), where the measurement information pertains to the patient while the patient interacts with the enhanced environment by using the treatment device 6070 to perform the treatment plan.

[1426] The expected information may include one or more vital signs of the user, a respiration rate of the user, a heartrate of the user, a temperature of the user, a blood pressure of the user, other suitable information of the user, or a combination thereof. The healthcare provider may determine that the enhanced environment and/or the treatment plan is having the desired effect if one or more parts or portions of the measurement information are within predetermined, computed, computable, projected, or otherwise acceptable ranges of one or more corresponding parts or portions of the expected information. Alternatively, the healthcare provider may determine that the enhanced environment and/or the treatment plan is not having the desired effect if one or more parts or portions of the measurement information are outside of the predetermined, computed, computable, projected, or otherwise acceptable range of one or more corresponding parts or portions of the expected information.

[1427] In some embodiments, while the patient interacts with the enhanced environment using the treatment device 6070 to perform the treatment plan, the healthcare provider may compare the expected respective characteristics of the treatment device 6070 with corresponding characteristics of the treatment device 6070 indicated by the treatment information. For example, the healthcare provider may compare an expected resistance setting of the treatment device 6070 with an actual resistance setting of the treatment device 6070 indicated by the treatment information.

[1428] The healthcare provider may determine that the patient is performing the treatment plan properly if the actual characteristics of the treatment device 6070 indicated by the treatment information are within a range of the expected characteristics of the treatment device 6070. Alternatively, the healthcare provider may determine that the patient is not performing the treatment plan properly if the actual characteristics of the treatment device 6070 indicated by the treatment information are outside the range of the expected characteristics of the treatment device 6070.

[1429] If the healthcare provider determines that the treatment information indicates that the patient is performing the treatment plan properly and/or that the enhanced environment and/or treatment plan is having the desired effect, the healthcare provider may determine not to modify the enhanced environment, the at least one aspect of the treatment plan, and/or one or more characteristics of the treatment device 6070. Alternatively, if the healthcare provider determines that the treatment information indicates that the patient is not performing the treatment plan properly and/or that the enhanced environment and/or the treatment plan is not having the desired effect, the healthcare provider may modify, while the user uses the treatment device 6070 to perform the treatment plan, the enhanced environment, at least one aspect of the treatment plan, and/or one or more characteristics of the treatment device 6070.

[1430] In some embodiments, while the patient interacts with the enhanced environment (e.g., either modified or unmodified) using the treatment device 6070 to perform the modified treatment plan, the server 6030 may receive subsequent treatment data pertaining to the patient. For example, after the healthcare provider provides input modifying the enhanced environment, at least one aspect of the treatment plan, and/or one or more characteristics of the treatment device 6070, the patient may continue to interact with the enhanced environment (e.g., either modified or unmodified) to perform the modified treatment plan using the treatment device 6070. The subsequent treatment data may correspond to treatment data generated while the patient interacts with the enhanced environment to use the treatment device 6070 to perform the modified treatment plan. In some embodiments, the subsequent treatment data may correspond to treatment data generated after the healthcare provider has received the treatment information and determined not to modify the enhanced environment, at least one aspect of the treatment plan, and/or one or more characteristics of the treatment device 6070, and while the patient continues to interact with the enhanced environment to perform the treatment plan using the treatment device 6070.

[1431] Additionally, or alternatively, the subsequent treatment data may correspond to treatment data generated after the healthcare provider has received the treatment information and determined to modify the enhanced environment and not to modify at least one of the at least one aspect of the treatment plan and one or more characteristics of the treatment device 70. Further additionally or alternatively, the subsequent treatment data may correspond to treatment data generated while the patient interacts with the modified enhanced environment in order to perform the unmodified treatment plan using the treatment device 6070.

[1432] Based on subsequent treatment plan input received from overview display 6120, the server 6030 may further modify the enhanced environment, at least one aspect of the treatment plan, and/or one or more characteristics of the treatment device 6070. The subsequent treatment plan input may correspond to input provided by the healthcare provider, at the overview display 6120, in response to receiving and/or reviewing subsequent treatment information corresponding to the subsequent treatment data. It should be understood that, based on continuously and/or periodically received treatment data, the server 6030 may continuously and/or periodically provide treatment information to the patient profile display 6130 and/or to other sections, portions, or components of the overview display 6120.

[1433] The healthcare provider may receive and/or review treatment information continuously or periodically while the user interacts with the enhanced environment using the treatment device 6070 to perform the treatment plan. Based on one or more trends indicated by the continuously and/or periodically received treatment information, the healthcare provider may determine whether to modify the enhanced environment, at least one aspect of the treatment plan, and/or one or more characteristics of the treatment device 70. For example, the one or more trends may suggest an increase in heartrate or changes in other applicable trends, indicating that the user is not performing the treatment plan properly and/or that performance of the treatment plan by the user is not having the desired effect. Additionally, or alternatively, the one or more trends may suggest an increase in heartrate or changes in other applicable trends, indicating that the enhanced environment is not having the desired effect.

[1434] In some embodiments, the user may provide feedback indicating one or more enhanced environment preferences to the server 6030. For example, to provide feedback to the server 6030, the user may interact with a computing device, such as a mobile computing device, an interface of the treatment device 6070, or another suitable computing device. The feedback may indicate one or more preferred enhanced environment characteristics. The one or more preferred enhanced environment characteristics may include one or more enhanced components (e.g., desired landscapes (e.g., visual depictions of mountains, hills, flatlands, oceans, planets, virtual locations, real-world locations, and the like), desired information, desired digital objects, and the like). The server 6030 may associate the one or more preferred enhanced environment characteristics with the treatment plan associated with the user in a database, such as the database 6044 or in another suitable database. The server 6030 may generate the enhanced environment using the one or more preferred enhanced environment characteristics.

[1435] In some embodiments, the treatment plan, including the configurations, settings, range of motion settings, pain level, force settings, and speed settings, etc. of the treatment device 6070 for various exercises, may be transmitted to the controller of the treatment device 6070. In one example, if the user provides an indication, via the patient interface 6050, that he is experiencing a high level of pain at a particular range of motion, the controller may receive the indication. Based on the indication, the controller may electronically adjust the range of motion of the pedal 6102 by adjusting the pedal inwardly, outwardly, or along or about any suitable axis, via one or more actuators, hydraulics, springs, electric motors, or the like. The treatment plan may define alternative range of motion settings for the pedal 6102 when the user indicates certain pain levels during an exercise. Accordingly, once the treatment plan is uploaded to the controller of the treatment device 6070, the treatment device 6070 may continue to operate without further instruction, further external input, and the like. It should be noted that the patient (via the patient interface 6050) and/or the assistant (via the assistant interface 6094) may override any of the configurations or settings of the treatment device 6070 at any time. For example, the patient may use the patient interface 6050 to cause the treatment device 6070 to immediately stop, if so desired. [1436] FIG. 89 is a flow diagram generally illustrating a method 6900 for providing, based on treatment data received while a user uses the treatment device 6070, an enhanced environment to the user, according to the principles of the present disclosure. The method 6900 is performed by processing logic that may include hardware (circuitry, dedicated logic, etc.), software (such as is ran on a general-purpose computer system or a dedicated machine), or a combination of both. The method 6900 and/or each of its individual functions, routines, subroutines, or operations may be performed by one or more processors of a computing device (e.g., any component of FIG. 81, such as server 6030 executing the artificial intelligence engine 6011). In some embodiments, the method 6900 may be performed by a single processing thread. Alternatively, the method 6900 may be performed by two or more processing threads, each thread implementing one or more individual functions, routines, subroutines, or operations of the methods.

[1437] For simplicity of explanation, the method 6900 is depicted and described as a series of operations. However, operations in accordance with this disclosure can occur in various orders and/or concurrently, and/or with other operations not presented and described herein. For example, the operations depicted in the method 900 may occur in combination with any other operation of any other method disclosed herein. Furthermore, not all illustrated operations may be required to implement the method 6900 in accordance with the disclosed subject matter. In addition, those skilled in the art will understand and appreciate that the method 6900 could alternatively be represented as a series of interrelated states via a state diagram or events.

[1438] At 6902, the processing device may receive treatment data pertaining to a user who uses a treatment device, such as the treatment device 6070, to perform a treatment plan. The treatment data may include characteristics of the user, measurement information pertaining to the user while the user uses the treatment device 6070, characteristics of the treatment device 6070, at least one aspect of the treatment plan, any another suitable data, or a combination thereof.

[1439] At 6904, using the treatment data, the processing device may identify at least one enhanced component.

[1440] At 6906, the processing device may generate an enhanced environment that uses the at least one enhanced component and the treatment plan. The enhanced environment may be configured to enhance the experience perceived by the user while the user uses the treatment device 6070 to perform the treatment plan. [1441] At 6908, the processing device may output at least one aspect of the enhanced environment to an interface configured to communicate with the treatment device 6070. While the user uses the treatment device 6070 and based on the outputted enhanced environment, the interface may include at least one enhanced reality device configured to present the enhanced environment to the user. The at least one enhanced reality device may include an augmented reality device, a virtual reality device, a mixed reality device, an immersive reality device, or a combination thereof.

[1442] In some embodiments, the at least one enhanced reality device may communicate or interact with the treatment device 6070. Based on the at least one enhanced component and/or the enhanced environment, the at least one enhanced reality device may send a signal to the treatment device 6070 to modify characteristics of the treatment device 6070. Based on the signal, the controller 6072 of the treatment device 6070 may selectively modify characteristics of the treatment device 6070. [1443] At 6910, the processing device may receive, while the user engages in the enhanced environment while using the treatment device 6070 to perform the treatment plan, subsequent treatment data pertaining to the user.

[1444] At 6912, the processing device may selectively modify at least one of the enhanced environment, at least one aspect of the treatment plan, and any other aspect of the treatment plan. For example, the processing device may determine whether the subsequent data indicates that the enhanced environment and/or the treatment plan is having a desired effect, as will be described. The processing device may modify, in response to determining that the enhanced environment and/or the treatment plan is not having the desired effect, at least one of the enhanced environment, at least one aspect of the treatment plan, and any other aspect of the treatment plan, to attempt to achieve the desired effect or, if not possible, to achieve some portion of or degree of the desired effect.

[1445] FIG. 90 is a flow diagram generally illustrating an alternative method 61000 for providing, based on treatment data received while a user uses the treatment device 6070, an enhanced environment to the user while the user uses the treatment device 6070, according to the principles of the present disclosure. Method 61000 includes operations performed by processors of a computing device (e.g., any component of FIG. 81, such as server 6030 executing the artificial intelligence engine 6011). In some embodiments, one or more operations of the method 61000 are implemented in computer instructions stored on a memory device and executed by a processing device. The method 61000 may be performed in the same or a similar manner as described above in regard to method 6900. The operations of the method 61000 may be performed in some combination with any of the operations of any of the methods described herein.

[1446] At 61002, the processing device may receive, during a telemedicine session, treatment data pertaining to a user who uses a treatment device, such as the treatment device 6070, to perform a treatment plan. The treatment data may include characteristics of the user, measurement information pertaining to the user while the user uses the treatment device 6070, characteristics of the treatment device 6070, at least one aspect of the treatment plan, any another suitable data, or a combination thereof.

[1447] At 61004, using the treatment data, the processing device may identify at least one enhanced component.

[1448] At 61006, the processing device may generate an enhanced environment using the at least one enhanced component and the treatment plan. The enhanced environment may be configured to enhance the experience perceived by the user while the user uses the treatment device 6070 to perform the treatment plan. [1449] At 61008, the processing device may output, during the telemedicine session, the enhanced environment to an interface configured to communicate with the treatment device 6070. The interface may include at least one enhanced reality device configured to present to the user, while the user uses the treatment device 6070 and based on the outputted enhanced environment, the enhanced environment. The at least one enhanced reality device may include an augmented reality device, a virtual reality device, a mixed reality device, an immersive reality device, or a combination thereof.

[1450] In some embodiments, the at least one enhanced reality device may communicate or interact with the treatment device 6070. Based on the at least one enhanced component and/or the enhanced environment, the at least one enhanced reality device may send a signal to the treatment device 6070 to modify characteristics of the treatment device 6070. Based on the signal, the controller 6072 of the treatment device 6070 may selectively modify characteristics of the treatment device 6070.

[1451] At 61010, the processing device may receive, during the telemedicine session and while the user engages in the enhanced environment while using the treatment device 6070 to perform the treatment plan, subsequent treatment data pertaining to the user.

[1452] At 61012, the processing device may selectively modify at least one of the enhanced environment, at least one aspect of the treatment plan, and any other aspect of the treatment plan. For example, the processing device may determine whether the subsequent data indicates that the enhanced environment and/or the treatment plan is having a desired effect, as will be described. The processing device may modify, in response to determining that the enhanced environment and/or the treatment plan is not having the desired effect, at least one of the enhanced environment, at least one aspect of the treatment plan, and any other aspect of the treatment plan, to attempt to achieve the desired effect or, if not possible, to achieve some portion of or degree of the desired effect.

[1453] At 61014, the processing device may control, during the telemedicine session and while the user uses the treatment device, based on at least one of the enhanced environment, and at least one of the modified enhanced environment, the at least one of the at least one aspect of the treatment plan and any other aspect of the treatment plan, the treatment device 6070.

[1454] FIG. 91 is a flow diagram generally illustrating an alternative method 61100 for providing, based on treatment data received while a user uses the treatment device 6070, an enhanced environment to the user, according to the principles of the present disclosure. Method 61100 includes operations performed by processors of a computing device (e.g., any component of FIG. 81, such as server 6030 executing the artificial intelligence engine 6011). In some embodiments, one or more operations of the method 61100 are implemented in computer instructions stored on a memory device and executed by a processing device. The method 61100 may be performed in the same or a similar manner as described above in regard to method 6900 and/or method 61000. The operations of the method 61100 may be performed in some combination with any of the operations of any of the methods described herein.

[1455] At 61102, the processing device may receive, during a telemedicine session, treatment data pertaining to a user who uses a treatment device, such as the treatment device 6070, to perform a treatment plan. The treatment data may include characteristics of the user, measurement information pertaining to the user while the user uses the treatment device 6070, characteristics of the treatment device 6070, at least one aspect of the treatment plan, any another suitable data, or a combination thereof.

[1456] At 61104, using the treatment data, the processing device may identify at least one enhanced component.

[1457] At 61106, the processing device may generate an enhanced environment using the at least one enhanced component and the treatment plan and may output, during the telemedicine session, the enhanced environment to an interface configured to communicate with the treatment device 6070. The enhanced environment may be configured to enhance the experience perceived by the user while the user uses the treatment device 6070 to perform the treatment plan. The interface may include at least one enhanced reality device configured to present, while the user uses the treatment device 6070 and based on the outputted enhanced environment, the enhanced environment to the user. The at least one enhanced reality device may include an augmented reality device, a virtual reality device, a mixed reality device, an immersive reality device, or a combination thereof.

[1458] In some embodiments, the at least one enhanced reality device may communicate or interact with the treatment device 6070. Based on the at least one enhanced component and/or the enhanced environment, the at least one enhanced reality device may send a signal to the treatment device 6070 to modify characteristics of the treatment device 6070. Based on the signal, the controller 6072 of the treatment device 6070 may selectively modify characteristics of the treatment device 6070.

[1459] At 61108, using the treatment data, the processing device may generate treatment information. While the user interacts with the enhanced environment using the treatment device 6070, the treatment information may include a summary of the user’s performance of the treatment plan. The treatment information may be formatted, such that the treatment data is presentable at a computing device of a healthcare provider responsible for the performance of the treatment plan by the user.

[1460] At 61110, the processing device may write the treatment information to an associated memory for access by at least one of the computing devices of the healthcare provider and a machine learning model executed by the artificial intelligence engine 6011.

[1461] At 61112, the processing device may receive treatment plan input responsive to the treatment information. The treatment plan input may indicate at least one modification to the enhanced environment, the at least one aspect treatment plan, any other aspect of the treatment plan, or a combination thereof. In some embodiments, the treatment plan input may be provided by the healthcare provider, as described. In some embodiments, based on the treatment information, the artificial intelligence engine 6011 executing the machine learning model may generate the treatment plan input.

[1462] At 61114, the processing device may determine whether the treatment plan input indicates at least one modification to the enhanced environment, the at least one aspect treatment plan, any other aspect of the treatment plan, or a combination thereof.

[1463] If the processing device determines, using the treatment device 6070 to perform the treatment plan, that the treatment plan input does not indicate at least one modification to the enhanced environment, the at least one aspect of the treatment plan, any other aspect of the treatment plan, or a combination thereof, the processing device returns to 61102 and continues receiving treatment data pertaining to the user while the user interacts with the enhanced environment. If the processing device determines that the treatment plan input indicates at least one modification to the enhanced environment, the at least one aspect treatment plan, any other aspect of the treatment plan, or a combination thereof, the processing device continues at 61116.

[1464] At 61116, the processing device may selectively modify at least one of the enhanced environment, at least one aspect of the treatment plan, any other aspect of the treatment plan, or a combination thereof. For example, the processing device may determine whether the treatment data indicates that the enhanced environment and/or the treatment plan are having a desired effect. The processing device may modify, in response to determining that the enhanced environment and/or the treatment plan are not having the desired effect, at least one of the enhanced environment, at least one aspect of the treatment plan, and any other aspect of the treatment plan, to attempt to achieve the desired effect or, if not possible, to achieve some portion of or degree of the desired effect. [1465] At 61118, using the treatment device 6070, the processing device may control, and while the user interacts with the enhanced environment, based on at least one of the enhanced environment, at least one of the modified enhanced environment, the at least one of the at least one aspect of the treatment plan, and any other aspect of the treatment plan, the treatment device 6070.

[1466] FIG. 92 generally illustrates an example embodiment of a method 61200 for receiving a selection of an optimal treatment plan and controlling a treatment device while the patient uses the treatment device according to the present disclosure, based on the optimal treatment plan. Method 61200 includes operations performed by processors of a computing device (e.g., any component of FIG. 81, such as server 6030 executing the artificial intelligence engine 6011). In some embodiments, one or more operations of the method 61200 are implemented in computer instructions stored on a memory device and executed by a processing device. The method 61200 may be performed in the same or a similar manner as described above in regard to method 6900. The operations of the method 61200 may be performed in some combination with any of the operations of any of the methods described herein.

[1467] Prior to the method 61200 being executed, various optimal treatment plans may be generated by one or more trained machine learning models 6013 of the artificial intelligence engine 6011. For example, based on a set of treatment plans pertaining to a medical condition of a patient, the one or more trained machine learning models 6013 may generate the optimal treatment plans. The various treatment plans may be transmitted to one or more computing devices of a patient and/or medical professional.

[1468] At 61202 of the method 61200, the processing device may receive a selection of an optimal treatment plan from the optimal treatment plans. The selection may have been entered on a user interface presenting the optimal treatment plans on the patient interface 6050 and/or the assistant interface 6094.

[1469] At 61204, the processing device may control, while the patient uses the treatment device 6070, based on the selected optimal treatment plan, the treatment device 6070. In some embodiments, the controlling is performed distally by the server 30. For example, if the selection is made using the patient interface 6050, one or more control signals may be transmitted from the patient interface 6050 to the treatment device 6070 to configure, according to the selected treatment plan, a setting of the treatment device 6070 to control operation of the treatment device 6070. Further, if the selection is made using the assistant interface 6094, one or more control signals may be transmitted from the assistant interface 6094 to the treatment device 6070 to configure, according to the selected treatment plan, a setting of the treatment device 6070 to control operation of the treatment device 6070.

[1470] It should be noted that, as the patient uses the treatment device 6070, the sensors 6076 may transmit measurement data to a processing device. The processing device may dynamically control, according to the treatment plan, the treatment device 6070 by modifying, based on the sensor measurements, a setting of the treatment device 6070. For example, if the force measured by the sensor 6076 indicates the user is not applying enough force to a pedal 6102, the treatment plan may indicate to reduce the required amount of force for an exercise.

[1471] It should be noted that, as the patient uses the treatment device 6070, the user may use the patient interface 6050 to enter input pertaining to a pain level experienced by the patient as the patient performs the treatment plan. For example, the user may enter a high degree of pain while pedaling with the pedals 6102 set to a certain range of motion on the treatment device 6070. The pain level may cause the range of motion to be dynamically adjusted based on the treatment plan. For example, the treatment plan may specify alternative range of motion settings if a certain pain level is indicated when the user is performing an exercise at a certain range of motion.

[1472] FIG. 93 generally illustrates anexample computer system61300 whichcanperformany one ormore of the methods described herein, in accordance with one or more aspects of the present disclosure. In one example, computer system 61300 may include a computing device and correspond to the assistance interface 6094, reporting interface 6092, supervisory interface 6090, clinician interface 6020, server 6030 (including the AI engine 6011), patient interface 6050, ambulatory sensor 6082, goniometer 6084, treatment device 6070, pressure sensor 6086, or any suitable component of FIG. 81. The computer system 61300 may be capable of executing instructions implementing the one or more machine learning models 6013 of the artificial intelligence engine 6011 of FIG. 81. The computer system may be connected (e.g., networked) to other computer systems in a LAN, an intranet, an extranet, or the Internet, including via the cloud or a peer-to-peer network.

[1473] The computer system may operate in the capacity of a server in a client-server network environment. The computer system may be a personal computer (PC), a tablet computer, a wearable (e.g., wristband), a set top box (STB), a personal Digital Assistant (PDA), a mobile phone, a camera, a video camera, an Internet of Things (IoT) device, or any device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that device. Further, while only a single computer system is illustrated, the term “computed shall also be taken to include any collection of computers that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methods discussed herein.

[1474] The computer system 61300 includes a processing device 61302, a main memory 61304 (e.g., read only memory (ROM), flash memory, solid state drives (SSDs), dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM)), a static memory 61306 (e.g., flash memory, solid state drives (SSDs), static random access memory (SRAM)), and a data storage device 61308, which communicate with each other via a bus 61310.

[1475] Processing device 61302 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device 61302 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing device 1402 may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a system on a chip, a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processing device 61302 is configured to execute instructions for performing any of the operations and steps discussed herein.

[1476] The computer system 61300 may further include a network interface device 61312. The computer system 61300 also may include a video display 61314 (e.g., a liquid crystal display (LCD), a light-emitting diode (LED), an organic light-emitting diode (OLED), a quantum LED, a cathode ray tube (CRT), a shadow mask CRT, an aperture grille CRT, a monochrome CRT), one or more input devices 61316 (e.g., a keyboard and/or a mouse or a gaming-like control), and one or more speakers 61318 (e.g., a speaker). In one illustrative example, the video display 61314 and the input device(s) 61316 may be combined into a single component or device (e.g., an LCD touch screen). [1477] The data storage device 61316 may include a computer-readable medium 61320 on which the instructions 61322 embodying any one or more of the methods, operations, or functions described herein is stored. The instructions 61322 may also reside, completely or at least partially, within the main memory 1304 and/or within the processing device 61302 during execution thereof by the computer system 61300. As such, the main memory 61304 and the processing device 61302 also constitute computer-readable media. The instructions 61322 may further be transmitted or received over a network via the network interface device 61312. [1478] While the computer-readable storage medium 61320 is generally illustrated in the illustrative examples to be a single medium, the term “computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer-readable storage medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure. The term “computer-readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media.

[1479] Clause 1.5. A method comprising: receiving treatment data pertaining to a user who uses a treatment device to perform a treatment plan, wherein the treatment data comprises at least one of characteristics of the user, measurement information pertaining to the user, characteristics of the treatment device, and at least one aspect of the treatment plan; identifying at least one enhanced component using the treatment data; generating an enhanced environment using the at least one enhanced component and the treatment plan; outputting at least one aspect of the enhanced environment to an interface configured to communicate with the treatment device; receiving subsequent treatment data pertaining to the user; and selectively modifying, using the subsequent treatment data, at least one of the enhanced environment and at least one of the at least one aspect of the treatment plan and any other aspect of the treatment plan.

[1480] Clause 2.5. The method of claim 1, further comprising controlling, while the user uses the treatment device and based on at least one of the enhanced environment the modified at least one of the enhanced environment and the at least one of the at least one aspect of the treatment plan and any other aspect of the treatment plan, the treatment device.

[1481] Clause 3.5. The method of claim 1, wherein outputting at least one aspect of the enhanced environment to the interface further includes outputting, during a telemedicine session, the enhanced environment while the user uses the treatment device.

[1482] Clause 4.5. The method of claim 1, wherein selectively modifying the at least one of the enhanced environment and at least one of the at least one aspect of the treatment plan and any other aspect of the treatment plan further includes, selectively modifying, using the subsequent treatment data while the user uses the treatment device, the at least one of the enhanced environment and at least one of the at least one aspect of the treatment plan and any other aspect of the treatment plan.

[1483] Clause 5.5. The method of claim 1, wherein selectively modifying the at least one of the enhanced environment and at least one of the at least one aspect of the treatment plan and any other aspect of the treatment plan further includes selectively modifying, using the subsequent treatment data while the user uses the treatment device during a telemedicine session, the at least one of the enhanced environment and at least one of the at least one aspect of the treatment plan and any other aspect of the treatment plan. [1484] Clause 6.5. The method of claim 1, wherein the measurement information includes at least one of a vital sign of the user, a respiration rate of the user, a heartrate of the user, a temperature of the user, an eye dilation, a metabolic marker, a biomarker, and a blood pressure of the user.

[1485] Clause 7.5. The method of claim 1, wherein the interface includes at least one enhanced reality device configured to present, while the user uses the treatment device and based on the outputted enhanced environment, the enhanced environment to the user.

[1486] Clause 8.5. The method of claim 7, wherein the at least one enhanced reality device includes one of an augmented reality device, a virtual reality device, a mixed reality device, and an immersive reality device.

SYSTEMS AND METHODS FOR USING MACHINE LEARNING TO CONTROL A

REHABILITATION AND EXERCISE ELECTROMECHANICAL DEVICE

[1487] Improvement is desired in the field of devices used for rehabilitation and exercise. People may injure, sprain, or tear a body part and consult a physician to diagnose the injury. In some instances, the physician may prescribe a treatment plan that includes operating one or more electromechanical devices (e.g., pedaling devices for arms or legs) for a period of time to exercise the affected area in an attempt to rehabilitate the affected body part and regain normal movability. In other instances, the person with the affected body part may determine to operate a device without consulting a physician. In either scenario, the devices that are operated lack effective monitoring of progress of rehabilitation of the affected area and control over the electromechanical device during operation by the user. Conventional devices lack components that enable operating the electromechanical device in various modes that are designed to enhance the rate and effectiveness of rehabilitation.

[1488] Further, conventional rehabilitation systems lack monitoring devices that aid in determining one or more properties of the user (e.g., range of motion of the affected area, heartrate of the user, etc.) and enable adjusting components based on the determined properties. When the user is supposed to be adhering to a treatment plan, conventional rehabilitation systems may not provide real-time results of sessions to the physicians. That is, typically the physicians have to rely on the patient’s word as to whether they are adhering to the treatment plan. Additionally, conventional rehabilitations do not provide a mechanism to closely monitor patient progress in real-time. Consequently, the user may over-exert himself or herself while exercising, may exercise using improper form, may exercise using a sub-optimal range of motion, and/or may exercise in any other manner that risks delaying the user’s rehabilitation (e.g., by reinjuring a body part that was previously operated on or injured) and/or increasing the cost of the user’s rehabilitation without an attendant benefit in improvement to the underlying condition.

[1489] Furthermore, conventional rehabilitation systems are unable to generate optimal treatment plans for patients. For example, a patient that has undergone surgery may have a limited range of motion (ROM) of a body part affected by the surgery. The surgery may have also affected strength and/or endurance of the patient. Consequently, an optimal treatment plan should improve the patient’s ROM, strength, and/or endurance. Additionally, an optimal treatment plan for a patient may vary based on a degree to which a surgery was successful, based on the patient’s medical history, based on the patient’s demographic information, based on the patient’s ability to accurately cany out a treatment plan, and/or the like. Conventional rehabilitation systems that use electromechanical devices are unable to generate optimal treatment plans that account for these variances.

[1490] A technical problem may relate to the information pertaining to the patient’s medical condition being received in disparate formats. For example, a server may receive the information pertaining to a medical condition of the patient from one or more sources (e.g., from an electronic medical record (EMR) system, application programming interface (API), or any suitable system that has information pertaining to the medical condition of the patient). That is, some sources that are used by various medical professional entities may be installed on their local computing devices and may use proprietary formats. Accordingly, some embodiments of the present disclosure may use an API to obtain, via interfaces exposed by APIs used by the sources, the formats used by the sources. In some embodiments, when information is received from the sources, the API may map and convert the format used by the sources to a standardized format used by the artificial intelligence engine. Further, the information converted to the standardized format used by the artificial intelligence engine may be stored in a database accessed by the artificial intelligence engine when performing any of the techniques disclosed herein. Using the information converted to a standardized format may enable more accurately determining the procedures to perform for the patient.

[1491] To that end, the standardized information may enable generating treatment plans having a particular format that canbe processed by various applications (e.g., telehealth). For example, applications, such as telehealth applications, may be executing on various computing devices of medical professionals and/or patients. The applications (e.g., standalone or web-based) may be provided by a server and may be configured to process data according to a format in which the treatment plans are implemented. Accordingly, the disclosed embodiments may provide a technical solution by (i) receiving, from various sources (e.g., EMR systems), information in non-standardized and/or different formats; (ii) standardizing the information; and (iii) generating, based on the standardized information, treatment plans having standardized formats that are capable of being processed by applications (e.g., telehealth application) executing on computing devices of medical professional and/or patients.

[1492] Still further, another technical problem may involve distally treating, via a computing device during a telemedicine or telehealth session, a patient from a location different than a location at which the patient is located. An additional technical problem is controlling or enabling the control of, from the different location, an electromechanical device used by the patient at the location at which the patient is located. Oftentimes, when a patient undergoes rehabilitative surgery (e.g., knee surgery), a physical therapist or other medical professional may prescribe an electromechanical device to the patient to use to perform a treatment protocol at their residence or any mobile location or temporary domicile. A medical professional may refer to any person with a credential, license, degree, or the like in the field of medicine, physical therapy, rehabilitation, or the like. For example, a medical professional may refer to a doctor, physician’ s assistant, nurse, chiropractor, dentist, physical therapist, physiotherapist, kinesiologist, acupuncturist, personal trainer, or the like.

[1493] Since the medical professional is located in a different location from the patient and the electromechanical device, it may be technically challenging for the medical professional to monitor the patient’ s actual progress (as opposed to relying on the patient’s word about their progress) using the electromechanical device, to modify the treatment plan according to the patient’s progress, to adapt the electromechanical device to the personal characteristics of the patient as the patient performs the treatment plan, and the like. [1494] Accordingly, aspects of the present disclosure generally relate to a control system for a rehabilitation and exercise electromechanical device (referred to herein as “electromechanical device”). The electromechanical device may include an electric motor configured to drive one or more radially-adjustable couplings to rotationally move pedals coupled to the radially-adjustable couplings. The electromechanical device may be operated by a user engaging the pedals with their hands or their feet and rotating the pedals to exercise and/or rehabilitate a desired body part. The electromechanical device and the control system may be included as part of a larger rehabilitation system. The rehabilitation system may also include monitoring devices (e.g., goniometer, wristband, force sensors in the pedals, etc.) that provide valuable information about the user to the control system. As such, the monitoring devices may be in direct or indirect communication with the control system.

[1495] The monitoring devices may include a goniometer that is configured to measure range of motion (e.g., angles of extension and/or bend) of a body part to which the goniometer is attached. The measured range of motion may be presented to the user and/or a physician via a user portal and/or a clinical portal. Also the control system may use the measured range of motion to determine whether to adjust positions of the pedals on the radially-adjustable couplings and/or to adjust the mode types (e.g., passive, active-assisted, resistive, active) and/or durations to operate the electromechanical device during a treatment plan. The monitoring devices may also include a wristband configured to track the steps of the user over a time period (e.g., day, week, etc.) and/or measure vital signs of the user (e.g., heartrate, blood pressure, oxygen level). The monitoring devices may also include force sensors disposed in the pedals that are configured to measure the force exerted by the user on the pedals.

[1496] The control system may enable operating the electromechanical device in a variety of modes, such as a passive mode, an active-assisted mode, a resistive mode, and/or an active mode. The control system may use the information received from the measuring devices to adjust parameters (e.g., reduce resistance provided by electric motor, increase resistance provided by the electric motor, increase/decrease speed of the electric motor, adjust position of pedals on radially-adjustable couplings, etc.) while operating the electromechanical device in the various modes. The control system may receive the information from the monitoring devices, aggregate the information, make determinations using the information, and/or transmit the information to a cloud-based computing system for storage. The cloud-based computing system may maintain the information that is related to each user.

[1497] A clinician and/or a machine learning model may generate a health improvement plan, such as a treatment plan for a user, to rehabilitate a part of their body using at least the electromechanical device. A treatment plan may include a set of pedaling sessions using the electromechanical device, a set of joint extension sessions, a set of flex sessions, a set of walking sessions, a set of heartrates per pedaling session and/or walking session, and the like. Additionally, or alternatively, the treatment plan may include a medical procedure to perform on the patient, a treatment protocol for the patient using the electromechanical device 104, a diet regimen for the patient, a medical regiment for the patient, a sleep regiment for the patient, and/or the like. [1498] Each pedaling session may specify that a user is to operate the electromechanical device in a combination of one or more modes, including: passive, active-passive, active, and resistive. The pedaling session may specify that the user is to wear the wristband and the goniometer during the pedaling session. Further, each pedaling session may include a set amount of time that the electromechanical device is to operate in each mode, a target heartrate for the user during each mode in the pedaling session, target forces that the user is to exert on the pedals during each mode in the pedaling session, target ranges of motion the body parts are to attain during the pedaling session, positions of the pedals on the radially -adjustable couplings, and the like.

[1499] Each joint extension session may specify a target angle of extension at the joint, and each set of joint flex sessions may specify a target angle of flex at the joint. Each walking session may specify a target number of steps the user should take over a set period of time (e.g., day, week etc.) and/or a target heartrate to achieve and/or maintain during the walking session.

[1500] The treatment plans may be stored in the cloud-based computing system and downloaded to the computing device of the user when the user is ready to begin the treatment plan. In some embodiments, the computing device that executes a clinical portal may transmit the treatment plan to the computing device that executes a user portal and the user may initiate the treatment plan when ready.

[1501] In addition, the disclosed rehabilitation system may enable a physician to monitor the progress of the user in real-time using the clinical portal. The clinical portal may present information pertaining to when the user is engaged in one or more sessions, statistics (e.g., speed, revolutions per minute, position of pedals, force on the pedals, vital signs, number of steps taken by user, range of motion, etc.) of the sessions, and the like. The clinical portal may also enable the physician to view before and after session images of the affected body part of the user to enable the physician to judge how well the treatment plan is working and/or to make adjustments to the treatment plan. The clinical portal may enable the physician to dynamically change a parameter (e.g., position of pedals, amount of resistance provided by electric motor, speed of the electric motor, duration of one of the modes, etc.) of the treatment plan in real-time based on information received from the control system.

[1502] Furthermore, the disclosed rehabilitation system may generate a health improvement plan by using a machine learning model to process received user data. The health improvement plan may include an exercise session to be performed on an electromechanical device. The disclosed rehabilitation system may select a device configuration for the electromechanical device, where the device configuration corresponds to the health improvement plan. The disclosed rehabilitation system may provide the device configuration to the electromechanical device such that the device configuration may be implemented on the electromechanical device.

[1503] The disclosed techniques provide numerous benefits over conventional systems. For example, the rehabilitation system provides granular control over the components of the electromechanical device to enhance the efficiency and effectiveness of rehabilitation of the user. The control system enables operating the electromechanical device in any suitable combination of the modes described herein by controlling the electric motor. Further, the control system may use information received from the monitoring devices to adjust parameters of components of the electromechanical device in real-time during a pedaling session, for example. Additional benefits of this disclosure may include enabling a computing device operated by a physician to monitor the progress of a user participating in a treatment plan in real-time (e.g., during a telemedicine or telehealth session) and/or to control operation of the electromechanical device during a pedaling session. [1504] Furthermore, by using machine learning to process received data, the rehabilitation system generates a health improvement plan that is optimal for the user. For example, the rehabilitation system generates a health improvement plan that includes an exercise session, where the exercise session may be performed by the user when a device configuration is implemented on the electromechanical device. The device configuration allows the exercise session to be performed using an optimal ROM, performed at an optimal strength, and/or performed at an optimal endurance. Additionally, by using machine learning to generate an optimal health improvement plan that accounts for a number of factors that influence optimality (e.g., user demographic, medical history, surgery results, and/or the like), the rehabilitation system reduces a likelihood of injury or re injury and improves a speed at which the user can recover. This reduces a utilization of resources (e.g., power resources, processing resources, network resources, and/or the like) of the electromechanical device and related computing or other devices relative to using an inferior plan more likely to injure or re-injure the user and that may require more time for the user to recover using the electromechanical device.

[1505] FIGURES 94 through 127, discussed below, and the various embodiments used to describe the principles of this disclosure are by way of illustration only and should not be construed in any way to limit the scope of the disclosure.

[1506] FIGURE 94 illustrates a high-level component diagram of an illustrative rehabilitation system architecture 7100 according to certain embodiments of this disclosure. In some embodiments, the system architecture 7100 may include a computing device 7102 communicatively coupled to an electromechanical device 7104, a goniometer 7106, a wristband 7108, and/or pedals 7110 of the electromechanical device 7104. Each of the computing device 7102, the electromechanical device 7104, the goniometer 7106, the wristband 7108, and the pedals 7110 may include one or more processing devices, memory devices, and network interface cards. The network interface cards may enable communication via a wireless protocol for transmitting data over short distances, such as Bluetooth, ZigBee, etc. In some embodiments, the computing device 7102 is communicatively coupled to the electromechanical device 7104, goniometer 7106, the wristband 7108, and/or the pedals 7110 via Bluetooth.

[1507] Additionally, the network interface cards may enable communicating data over long distances, and in one example, the computing device 7102 may communicate with a network 7112. Network 7112 may be a public network (e.g., connected to the Internet via wired (Ethernet) or wireless (WiFi)), a private network (e.g., a local area network (LAN) or wide area network (WAN)), or a combination thereof. The computing device 7102 may be communicatively coupled with a computing device 7114 and a cloud-based computing system 7116.

[1508] The computing device 7102 may be any suitable computing device, such as a laptop, tablet, smartphone, or computer. The computing device 7102 may include a display that is capable of presenting a user interface, such as a user portal 7118. The user portal 7118 may be implemented in computer instructions stored on the one or more memory devices of the computing device 7102 and executable by the one or more processing devices of the computing device 7102. The user portal 7118 may present various screens to a user that enable the user to view a treatment plan, initiate a pedaling session of the treatment plan, control parameters of the electromechanical device 7104, view progress of rehabilitation during the pedaling session, and so forth as described in more detail below. The computing device 7102 may also include instructions stored on the one or more memory devices that, when executed by the one or more processing devices of the computing device 7102, perform operations to control the electromechanical device.

[1509] The computing device 7114 may execute a clinical portal 7126. The clinical portal 7126 may be implemented in computer instructions stored on the one or more memory devices of the computing device 7114 and executable by the one or more processing devices of the computing device 7114. The clinical portal 7114 may present various screens to a medical professional that enable the medical professional to create a treatment planfor a patient, view progress of the user throughout the treatment plan, view measured properties (e.g., angles of bend/extension, force exerted on pedals 7110, heartrate, steps taken, images of the affected body part) of the user during sessions of the treatment plan, view properties (e.g., modes completed, revolutions per minute, etc.) of the electromechanical device 7104 during sessions of the treatment plan. The treatment plan specific to a patient may be transmitted via the network 7112 to the cloud-based computing system 7116 for storage and/or to the computing device 7102 so the patient may begin the treatment plan.

[1510] The electromechanical device 7104 may be an adjustable pedaling device for exercising and rehabilitating arms and/or legs of a user. The electromechanical device 7104 may include at least one or more motor controllers 7120, one or more electric motors 7122, and one or more radially-adjustable couplings 7124. Two pedals 7110 may be coupled to two radially-adjustable couplings 7124 via a left and right pedal assemblies that each include a respective stepper motor. The motor controller 7120 may be operatively coupled to the electric motor 7122 and configured to provide commands to the electric motor 7122 to control operation of the electric motor 7122. The motor controller 7120 may include any suitable microcontroller including a circuit board having one or more processing devices, one or more memory devices (e.g., read-only memory (ROM) and/or random access memory (RAM)), one or more network interface cards, and/or programmable input/output peripherals. The motor controller 7120 may provide control signals or commands to drive the electric motor 7122. The electric motor 7122 may be powered to drive one or more radially-adjustable couplings 7124 of the electromechanical device 7104 in a rotational manner. The electric motor 7122 may provide the driving force to rotate the radially-adjustable couplings 7124 at configurable speeds. The couplings 7124 are radially- adjustable in that a pedal 7110 attached to the coupling 7124 may be adjusted to a number of positions on the coupling 7124 in a radial fashion. Further, the electromechanical device 7104 may include current shunt to provide resistance to dissipate energy from the electric motor 7122. As such, the electric motor 7122 may be configured to provide resistance to rotation of the radially-adjustable couplings 7124.

[1511] The computing device 7102 may be communicatively connected to the electromechanical device 7104 via the network interface card on the motor controller 7120. The computing device 7102 may transmit commands to the motor controller 7120 to control the electric motor 7122. The network interface card of the motor controller 7120 may receive the commands and transmit the commands to the electric motor 7122 to drive the electric motor 7122. In this way, the computing device 7102 is operatively coupled to the electric motor 7122.

[1512] The computing device 7102 and/or the motor controller 7120 may be referred to as a control system herein. The user portal 7118 may be referred to as a user interface of the control system herein. The control system may control the electric motor 7122 to operate in a number of modes: passive, active-assisted, resistive, and active. The passive mode may refer to the electric motor 7122 independently driving the one or more radially-adjustable couplings 7124 rotationally coupled to the one or more pedals 7110. In the passive mode, the electric motor 7122 may be the only source of driving force on the radially-adjustable couplings. That is, the user may engage the pedals 7110 with their hands or their feet and the electric motor 7122 may rotate the radially-adjustable couplings 7124 for the user. This may enable moving the affected body part and stretching the affected body part without the user exerting excessive force. [1513] The active-assisted mode may refer to the electric motor 7122 receiving measurements of revolutions per minute of the one or more radially-adjustable couplings 7124, and causing the electric motor 7122 to drive the one or more radially-adjustable couplings 7124 rotationally coupled to the one or more pedals 7110 when the measured revolutions per minute satisfy a threshold condition. The threshold condition may be configurable by the user and/or the physician. The electric motor 7122 may be powered off while the user provides the driving force to the radially-adjustable couplings 7124 as long as the revolutions per minute are above a revolutions per minute threshold and the threshold condition is not satisfied. When the revolutions per minute are less than the revolutions per minute threshold then the threshold condition is satisfied and the electric motor 7122 may be controlled to drive the radially-adjustable couplings 7124 to maintain the revolutions per minute threshold.

[1514] The resistive mode may refer to the electric motor 7122 providing resistance to rotation of the one or more radially-adjustable couplings 7124 coupled to the one or more pedals 7110. The resistive mode may increase the strength of the body part being rehabilitated by causing the muscle to exert force to move the pedals against the resistance provided by the electric motor 7122.

[1515] The active mode may refer to the electric motor 7122 powering off to provide no driving force assistance to the radially-adjustable couplings 7124. Instead, in this mode, the user provides the sole driving force of the radially-adjustable couplings using their hands or feet, for example.

[1516] During one or more of the modes, each of the pedals 7110 may measure force exerted by a part of the body of the user on the pedal 7110. For example, the pedals 7110 may each contain any suitable sensor (e.g., strain gauge load cell, piezoelectric crystal, hydraulic load cell, etc.) for measuring force exerted on the pedal 7110. Further, the pedals 7110 may each contain any suitable sensor for detecting whether the body part of the user separates from contact with the pedals 7110. In some embodiments, the measured force may be used to detect whether the body part has separated from the pedals 7110. The force detected may be transmitted via the network interface card of the pedal 7110 to the control system (e.g., computing device 7102 and/or motor controller 7120). As described further below, the control system may modify a parameter of operating the electric motor 7122 based on the measured force. Further, the control system may perform one or more preventative actions (e.g., locking the electric motor 7120 to stop the radially-adjustable couplings 7124 from moving, slowing down the electric motor 7122, presenting a notification to the user, etc.) when the body part is detected as separated from the pedals 7110, among other things.

[1517] The goniometer 7106 may be configured to measure angles of extension and/or bend of body parts and transmit the measured angles to the computing device 7102 and/or the computing device 7114. The goniometer 7106 may be included in an electronic device that includes the one or more processing devices, memory devices, and/or network interface cards. The goniometer 7106 may be disposed in a cavity of a mechanical brace. The cavity of the mechanical brace may be located near a center of the mechanical brace where the mechanical brace affords to bend and extend. The mechanical brace may be configured to secure to an upper body part (e.g., leg, arm, etc.) and a lower body part (e.g., leg, arm, etc.) to measure the angles of bend as the body parts are extended away from one another or retracted closer to one another.

[1518] The wristband 7108 may include a 3-axis accelerometer to track motion in the X, Y, and Z directions, an altimeter for measuring altitude, and/or a gyroscope to measure orientation and rotation. The accelerometer, altimeter, and/or gyroscope may be operatively coupled to a processing device in the wristband 7108 and may transmit data to the processing device. The processing device may cause a network interface card to transmit the data to the computing device 7102 and the computing device 7102 may use the data representing acceleration, frequency, duration, intensity, and paterns of movement to track steps taken by the user over certain time periods (e.g., days, weeks, etc.). The computing device 7102 may transmit the steps to the computing device 7114 executing a clinical portal 7126. Additionally, in some embodiments, the processing device of the wristband 7108 may determine the steps taken and transmit the steps to the computing device 7102. In some embodiments, the wristband 7108 may use photo plethysmography (PPG) to measure heartrate that detects an amount of red light or green light on the skin of the wrist. For example, blood may absorb green light so when the heart beats, the blood flow may absorb more green light, thereby enabling detecting heartrate. The heartrate may be sent to the computing device 7102 and/or the computing device 7114.

[1519] The computing device 7102 may present the steps taken by the user and/or the heartrate via respective graphical element on the user portal 7118, as discussed further below. The computing device may also use the steps taken and/or the heart rate to control a parameter of operating the electromechanical device 7104. For example, if the heartrate exceeds a target heartrate for a pedaling session, the computing device 7102 may control the electric motor 7122 to reduce resistance being applied to rotation of the radially -adjustable couplings 7124. In another example, if the steps taken are below a step threshold for a day, the treatment plan may increase the amount of time for one or more modes that the user in which the user is to operate the electromechanical device 7104 to ensure the affected body part is geting sufficient movement.

[1520] In some embodiments, the cloud-based computing system 7116 may include one or more servers 7128 that form a distributed computing architecture. Each of the servers 7128 may include one or more processing devices, memory devices, data storage, and/or network interface cards. The servers 7128 may be in communication with one another via any suitable communication protocol. The servers 7128 may store profiles for each of the users that use the electromechanical device 7104. The profiles may include information about the users such as a treatment plan, the affected body part, any procedure the user had performed on the affected body part, health, age, race, blood pressure, measured data from the goniometer 7106, measured data from the wristband 7108, measured data from the pedals 7110, user input received at the user portal 7118 during operation of any of the modes of the treatment plan, a level of discomfort the user experiences before and after any of the modes, before and after session images of the affected body part, and so forth.

[1521] In some embodiments the cloud-based computing system 7116 may include a training engine 7130 that is capable of generating one or more machine learning models 7132. The machine learning models 7132 may be trained to generate treatment plans for the patients in response to receiving various inputs (e.g., a procedure performed on the patient, an affected body part the procedure was performed on, other health characteristics (age, race, fitness level, etc.). The one or more machine learning models 7132 may be generated by the training engine 7130 and may be implemented in computer instructions that are executable by one or more processing device of the training engine 7130 and/or the servers 7128. To generate the one or more machine learning models 7132, the training engine 7130 may train the one or more machine learning models 7132. The training engine 7130 may use a base data set of patient characteristics, treatment plans followed by the patient, and results of the treatment plan followed by the patients. The results may include information indicating whether the treatment plan led to full recovery of the affected body part, partial recover of the affect body part, or lack of recovery of the affected body part. The training engine 7130 may be a rackmount server, a router computer, a personal computer, a portable digital assistant, a smartphone, a laptop computer, a tablet computer, a camera, a video camera, a netbook, a desktop computer, a media center, or any combination of the above. The one or more machine learning models 7132 may refer to model artifacts that are created by the training engine 7130 using training data that includes training inputs and corresponding target outputs. The training engine 7130 may find patterns in the training data that map the training input to the target output, and generate the machine learning models 7132 that capture these patterns. Although depicted separately from the computing device 7102, in some embodiments, the training engine 7130 and/or the machine learning models 7132 may reside on the computing device 7102 and/or the computing device 7114.

[1522] The machine learning models 7132 may include one or more of a neural network, such as an image classifier, recurrent neural network, convolutional network, generative adversarial network, a fully connected neural network, or some combination thereof, for example. In some embodiments, a machine learning model may be supported by one or more data structures, wherein, for example, a data structure may be a data model. For example, a data model may be a structural framework organized according to one or more schemata. A machine learning model may use the data model by applying one or more machine learning techniques to the data model to generate output values or to identify specific data points. In some embodiments, the machine learning models 7132 may be composed of a single level of linear or non-linear operations or may include multiple levels of non-linear operations. For example, the machine learning model may include numerous layers and/or hidden layers that perform calculations (e.g., dot products) using various neurons.

[1523] FIGURE 95A illustrates a perspective view of an example of an exercise and rehabilitation device 7104 according to certain embodiments of this disclosure. The electromechanical device 7104 is shown having pedal 7110 on opposite sides that are adjustably positionable relative to one another on respective radially - adjustable couplings 7124. The depicted device 7104 is configured as a small and portable unit so that it is easily transported to different locations at which rehabilitation or treatment is to be provided, such as at patients’ homes, alternative care facilities, or the like. The patient may sit in a chair proximate the device 7104 to engage the device 7104 with their feet, for example.

[1524] The device 7104 includes a rotary device such as radially -adjustable couplings 7124 or flywheel or the like rotatably mounted such as by a central hub to a frame 200 or other support. The pedals 7110 are configured for interacting with a patient to be rehabilitated and may be configured for use with lower body extremities such as the feet, legs, or upper body extremities, such as the hands, arms, and the like. For example, the pedal 7110 may be a bicycle pedal of the type having a foot support rotatably mounted onto an axle with bearings. The axle may or may not have exposed end threads for engaging a mount on the radially -adjustable coupling 7124 to locate the pedal on the radially -adjustable coupling 7124. The radially -adjustable coupling 7124 may include an actuator configured to radially adjust the location of the pedal to various positions on the radially -adjustable coupling 7124.

[1525] The radially -adjustable coupling 7124 may be configured to have both pedals 7110 on opposite sides of a single coupling 7124. In some embodiments, as depicted, a pair of radially -adjustable couplings 7124 may be spaced apart from one another but interconnected to the electric motor 7122. In the depicted example, the computing device 7102 may be mounted on the frame 200 and may be detachable and held by the user while the user operates the device 7104. The computing device 7102 may present the user portal and control the operation of the electric motor 7122, as described herein. [1526] FIGURE 95B illustrates a perspective view of another example of an exercise and rehabilitation device 7104 according to certain embodiments of this disclosure. The depicted device 7104 takes the form of a traditional exercise/rehabilitation device which is more or less non-portable and remains in a fixed location, such as a rehabilitation clinic or medical practice. The device 7104 in FIGURE 95B may include similar features described in FIG. 95A except the device 7104 in FIGURE 95B includes a seat and is less portable.

[1527] FIGURE 96 illustrates example operations of a method 7300 for controlling an electromechanical device for rehabilitation in various modes according to certain embodiments of this disclosure. The method 7300 may be performed by processing logic that may include hardware (circuitry, dedicated logic, etc.), software, or a combination of both. The method 7300 and/or each of their individual functions, subroutines, or operations may be performed by one or more processors of a control system (e.g., computing device 7102 of FIGURE 94) implementing the method 7300. The method 7300 may be implemented as computer instructions that, when executed by a processing device, execute the user portal 7118. In certain implementations, the method 7300 may be performed by a single processing thread. Alternatively, the method 7300 may be performed by two or more processing threads, each thread implementing one or more individual functions, routines, subroutines, or operations of the methods. Various operations of the method 7300 may be performed by one or more of the cloud-based computing system 7116, the motor controller 7120, the pedals 7110, the goniometer 7106, the wristband 7108, and/or the computing device 7114 of FIGURE 94.

[1528] As discussed above, an electromechanical device may include one or more pedals coupled to one or more radially -adjustable couplings, an electric motor coupled to the one or more pedals via the one or more radially-adjustable couplings, and the control system including one or more processing devices operatively coupled to the electric motor. In some embodiments, the control system (e.g., computing device 7102 and/or motor controller 7120) may store instructions and one or more operations of the control system may be presented via the user portal. In some embodiments the radially-adjustable couplings are configured for translating rotational motion of the electric motor to radial motion of the pedals.

[1529] At block 7302, responsive to a first trigger condition occurring, the processing device may control the electric motor to operate in a passive mode by independently driving the one or more radially-adjustable couplings rotationally coupled to the one or more pedals. “Independently drive” may refer to the electric motor driving the one or more radially-adjustable couplings without the aid of another driving source (e.g., the user). The first trigger condition may include an initiation of a pedaling session via the user interface of the control system, a period of time elapsing, a detected physical condition (e.g., heartrate, oxygen level, blood pressure, etc.) of a user operating the electromechanical device, a request received from the user via the user interface, or a request received via a computing device communicatively coupled to the control system (e.g., a request received from the computing device executing the clinical portal) . The processing device may control the electric motor to independently drive the one or more radially-adjustable couplings rotationally coupled to the one or more pedals at a controlled speed specified in a treatment plan for a user operating the electromechanical device while operating in the passive mode.

[1530] In some embodiments, the electromechanical device may be configured such that the processor controls the electric motor to individually drive the radially-adjustable couplings. For example, the processing device may control the electric motor to individually drive the left or right radially-adjustable coupling, while allowing the user to provide the force to drive the other radially-adjustable coupling. As another example, the processing device may control the electric motor to drive both the left and right radially-adjustable couplings but at different speeds. This granularity of control may be beneficial by controlling the speed at which a healing body part is moved (e.g., rotated, flexed, extended, etc.) to avoid tearing tendons or causing pain to the user. [1531] At block 7304, responsive to a second trigger condition occurring, the processing device may control the electric motor to operate in an active-assisted mode by measuring (block 7306) revolutions per minute of the one or more radially-adjustable couplings, and causing (block 7308) the electric motor to drive the one or more radially-adjustable couplings rotationally coupled to the one or more pedals when the measured revolutions per minute satisfy a threshold condition. The second trigger condition may include an initiation of a pedaling session via the user interface of the control system, a period of time elapsing, a detected physical condition (e.g., heartrate, oxygen level, blood pressure, etc.) of a user operating the electromechanical device, a request received from the user via the user interface, or a request received via a computing device communicatively coupled to the control system (e.g., a request received from the computing device executing the clinical portal). The threshold condition may be satisfied when the measured revolutions per minute are less than a minimum revolutions per minute. In such an instance, the electric motor may begin driving the one or more radially-adjustable couplings to increase the revolutions per minute of the radially-adjustable couplings. [1532] As with the passive mode, the processing device may control the electric motor to individually drive the one or more radially-adjustable couplings in the active-assisted mode. For example, if just a right knee is being rehabilitated, the revolutions per minute of the right radially-adjustable coupling may be measured and the processing device may control the electric motor to individually drive the right radially-adjustable coupling when the measured revolutions per minute is less than the minimum revolutions per minute. In some embodiments, there may be different minimum revolution per minutes set for the left radially-adjustable coupling and the right radially-adjustable coupling, and the processing device may control the electric motor to individually drive the left radially-adjustable coupling and the right radially-adjustable coupling as appropriate to maintain the different minimum revolutions per minute.

[1533] At block 7310, responsive to a third trigger condition occurring, the processing device may control the electric motor to operate in a resistive mode by providing resistance to rotation of the one or more radially-adjustable couplings coupled to the one or more pedals. The third trigger condition may include an initiation of a pedaling session via the user interface of the control system, a period of time elapsing, a detected physical condition (e.g., heartrate, oxygen level, blood pressure, etc.) of a user operating the electromechanical device, a request received from the user via the user interface, or a request received via a computing device communicatively coupled to the control system (e.g., a request received from the computing device executing the clinical portal).

[1534] In some embodiments, responsive to a fourth trigger condition occurring, the processing device is further configured to control the electric motor to operate in an active mode by powering off to enable another source (e.g., the user) to drive the one or more radially-adjustable couplings via the one or more pedals. In the active mode, another source may drive the one or more radially-adjustable couplings via the one or more pedals at any desired speed.

[1535] In some embodiments, the processing device may control the electric motor to operate in each of the passive mode, the active-assisted mode, the resistive mode, and/or the active mode for a respective period of time during a pedaling session based on a treatment plan for a user operating the electromechanical device. In some embodiments, the various modes and the respective periods of time may be selected by a clinician that sets up the treatment plan using the clinical portal. In some embodiments, the various modes and the respective periods of time may be selected by a machine learning model trained to receive parameters (e.g., procedure performed on the user, body part on which the procedure was performed, health of the user) and to output a treatment plan to rehabilitate the affected body part, as described above.

[1536] In some embodiments, the processing device may modify one or more positions of the one or more pedals on the one or more radially -adjustable couplings to change one or more diameters of ranges of motion of the one or more pedals during any of the passive mode, active-assisted mode, the resistive mode, and/or the active mode throughout a pedaling session for a user operating the electromechanical device. The processing device may be further configured to modify the position of one of the one or more pedals on one of the one or more radially-adjustable couplings to change the diameter of the range of motion of the one of the one or more pedals while maintaining another position of another of the one or more pedals on another of the one or more radially-adjustable couplings to maintain another diameter of another range of motion of the another pedal. In some embodiments, the processing device may cause both positions of the pedals to move to change the diameter of the range of motion for both pedals. The amount of movement of the positions of the pedals may be individually controlled in order to provide different diameters of ranges of motions of the pedals as desired. [1537] In some embodiments, the processing device may receive, from the goniometer worn by the user operating the electromechanical device, at least one of an angle of extension of a joint of the user during a pedaling session or an angle of bend of the joint of the user during the pedaling session. In some instances, the joint may be a knee or an elbow. The goniometer may be measuring the angles of bend and/or extension of the joint and continuously or periodically transmitting the angle measurements that are received by the processing device. The processing device may modify the positions of the pedals on the radially-adjustable couplings to change the diameters of the ranges of motion of the pedals based on the at least one of the angle of extension of the joint of the user orthe angle of bend of the joint of the user.

[1538] In some embodiments, the processing device may receive, from the goniometer worn by the user, a set of angles of extension between an upper leg and a lower leg at a knee of the user as the user extends the lower leg away from the upper leg via the knee. In some embodiments, the goniometer may send the set of angles of extension between an upper arm, upper body, etc. and a lower arm, lower body, etc. The processing device may present, on a user interface of the control system, a graphical animation of the upper leg, the lower leg, and the knee of the user as the lower leg is extended away from the upper leg via the knee. The graphical animation may include the set of angles of extension as the set of angles of extension change during the extension. The processing device may store, in a data store of the control system, a lowest value of the set of angles of extension as an extension statistic for an extension session. A set of extension statistics may be stored for a set of extension sessions specified by the treatment plan. The processing device may present progress of the set of extension sessions throughout the treatment plan via a graphical element (e.g., line graph, bar chart, etc.) on the user interface presenting the set of extension statistics.

[1539] In some embodiments, the processing device may receive, from the goniometer worn by the user, a set of angles of bend or flex between an upper leg and a lower leg at a knee of the user as the user retracts the lower leg closer to the upper leg via the knee. In some embodiments, the goniometer may send the set of angles of bend between an upper arm, upper body, etc. and a lower arm, lower body, etc. The processing device may present, on a user interface of the control system, a graphical animation of the upper leg, the lower leg, and the knee of the user as the lower leg is retracted closer to the upper leg via the knee. The graphical animation may include the set of angles of bend as the set of angles of bend change during the bending. The processing device may store, in a data store of the control system, a highest value of the set of angles of bend as a bend statistic for a bend session. A set of bend statistics may be stored for a set of bend sessions specified by the treatment plan. The processing device may present progress of the set of bend sessions throughout the treatment plan via a graphical element (e.g., line graph, bar chart, etc.) on the user interface presenting the set of bend statistics. [1540] In some embodiments, the angles of extension and/or bend of the joint may be transmitted by the goniometer to a computing device executing a clinical portal. A clinician may be operating the computing device executing the clinical portal. The clinical portal may present a graphical animation of the upper leg extending away from the lower leg and/or the upper leg bending closer to the lower leg in real-time during a pedaling session, extension session, and/or a bend session of the user. In some embodiments, the clinician may provide notifications to the computing device to present via the user portal. The notifications may indicate that the user has satisfied a target extension and/or bend angle. Other notifications may indicate that the user has extended or retracted a body part too far and should cease the extension and/or bend session. In some embodiments, the computing device executing the clinical portal may transmit a control signal to the control system to move a position of a pedal on the radially -adjustable coupling based on the angle of extension or angle of bend received from the goniometer. That is, the clinician can increase a diameter of range of motion for a body part of the user in real-time based on the measured angles of extension and/or bend during a pedaling session. This may enable the clinician dynamically control the pedaling session to enhance the rehabilitation results of the pedaling session.

[1541] In some embodiments, the processing device may receive, from a wearable device (e.g., wristband), an amount of steps taken by a user over a certain time period (e.g., day, week, etc.). The processing device may calculate whether the amount of steps satisfies a step threshold of a walking session of a treatment plan for the user. The processing device may present the amount of steps taken by the user on a user interface of the control system and may present an indication of whether the amount of steps satisfies the step threshold. [1542] The wristband may also measure one or more vital statistics of the user, such as a heartrate, oxygen level, blood pressure, glucose level, and the like. The measurements of the vital statistics may be performed at any suitable time, such as during a pedaling session, walking session, extension session, and/or bend session. The measurements of the vital statistics may also be performed at any other suitable time, such as before or after a pedaling session, walking session, extension session, and/or bend session. The wristband may transmit the one or more vital statistics to the control system. The processing device of the control system may use the vital statistics to determine whether to reduce resistance the electric motor is providing to lower one of the vital statistics (e.g., heartrate) when that vital statistic is above a threshold, to determine whether the user is in pain when one of the vital statistics is elevated beyond a threshold, to determine whether to provide a notification indicating the user should take a break or increase the intensity of the appropriate session, and so forth. The processing device of the control system may also use the vital statistics to determine whether previous treatment sessions produced the desired results after the treatment session indicating that treatment parameters in future sessions should be adjusted. The processing device of the control system may also use the vital statistics to determine whether the vital statistics prior to starting a treatment session indicate that the treatment parameters should be adjusted.

[1543] In some embodiments, the processing device may receive a request to stop the one or more pedals from moving. The request may be received by a user selecting a graphical icon representing “stop” on the user portal of the control system. The processing device may cause the electric motor to lock and stop the one or more pedals from moving over a configured period of time (e.g., instantly, over 1 second, 2 seconds, 3 seconds, 5 seconds, 10 seconds, etc.). One benefit of including an electric motor in the electromechanical device is the ability to stop the movement of the pedals as soon as a user desires.

[1544] In some embodiments, the processing device may receive, from one or more force sensors operatively coupled to the one or more pedals and the one or more processing devices, one or more measurements of force on the one or more pedals. The force sensors may be operatively coupled with the one or more processing devices via a wireless connection (e.g., Bluetooth) provided by wireless circuitry of the pedals. The processing device may determine whether the user has fallen from the electromechanical device based on the one or more measurements of force. Responsive to determining that the user has fallen from the electromechanical device, the processing device may lock the electric motor to stop the one or more pedals from moving.

[1545] Additionally or alternatively, the processing device may determine that feet or hands have separated from the pedals based on the one or more measurements of force. In response to determining that the feed or hands have separated from the pedals, the processing device may lock the electric motor to stop the one or more pedals from moving. Also, the processing device may present a notification on a user interface of the control system that instructs the user to place their feet or hands in contact with the pedals.

[1546] In some embodiments, the processing device may receive, from the force sensors operatively coupled to the one or more pedals, the measurements of force exerted by a user on the pedals during a pedaling session. The processing device may present the respective measurements of force on each of the pedals on a separate respective graphical scale on the user interface of the control system while the user pedals during the pedaling session. Various graphical indicators may be presented on the user interface to indicate when the force is below a threshold target range, within the threshold target range, and/or exceeds the threshold target range. Notifications may be presented to encourage the user to apply more force and/or less force to achieve the threshold target range of force. For example, the processing device is to present a first notification on the user interface when the one or more measurements of force satisfy a pressure threshold and present a second notification on the user interface when the one or more measurements do not satisfy the pressure threshold. [1547] In addition, the processing device may provide an indicator to the user based on the one or more measurements of force. The indicator may include at least one of (1) providing haptic feedback in the pedals, handles, and/or seat of the electromechanical device, (2) providing visual feedback on the user interface (e.g., an alert, a light, a sign, etc.), (3) providing audio feedback via an audio subsystem (e.g., speaker) of the electromechanical device, or (4) illuminating a warning light of the electromechanical device.

[1548] In some embodiments, the processing device may receive, from an accelerometer of the control system, motor controller, pedal, or the like, a measurement of acceleration of movement of the electromechanical device. The processing device may determine whether the electromechanical device has moved excessively relative to a vertical axis (e.g., fallen over) based on the measurement of acceleration. Responsive to determining that the electromechanical device has moved excessively relative to the vertical axis based on the measurement of acceleration, the processing device may lock the electric motor to stop the one or more pedals from moving. [1549] After a pedaling session is complete, the processing device may lock the electric motor to prevent the one or more pedals from moving a certain amount of time after the completion of the pedaling session. This may enable healing of the body part being rehabilitated and prevent strain on that body part by excessive movement. Upon expiration of the certain amount of time, the processing device may unlock the electric motor to enable movement of the pedals again.

[1550] The user portal may provide an option to image the body part being rehabilitated. For example, the user may place the body part within an image capture section of the user portal and select an icon to capture an image of the body part. The images may be captured before and after a pedaling session, walking session, extension session, and/or bend session. These images may be sent to the cloud-based computing system to use as training data for the machine learning model to determine the effects of the session. Further, the images may be sent to the computing device executing the clinical portal to enable the clinician to view the results of the sessions and modify the treatment plan if desired and/or provide notifications (e.g., reduce resistance, increase resistance, extend the joint further or less, etc.) to the user if desired.

[1551] In some embodiments, other data may be used to continuously or continually update and train the machine learning model (or machine learning models) to determine the effects of the session. For example, the other data may include heart rate, blood pressure, oxygen level, glucose measurement, goniometer data, steps walked data, temperature, perspiration rate, and/or pain level (as indicated by the user using the user portal). Further, the other data may be sent to the computing device executing the clinical portal to enable the clinician to view the results of the sessions and modify the treatment plan if desired and/or provide notifications (e.g., reduce resistance, increase resistance, extend the joint further or less, etc.) if desired. In some embodiments, the machine learning model may be trained to receive the other data and/or images in real-time or near real-time and output a control instruction that controls operation of the treatment apparatus (e.g., changes a range of motion provided by the pedal configuration, changes a speed of the motor controlling the pedal movement, etc.). [1552] FIGURE 97 illustrates example operations of a method 7400 for controlling an amount of resistance provided by an electromechanical device according to certain embodiments of this disclosure. Method 7400 includes operations performed by processing devices of the control system (e.g., computing device 7102) of FIGURE 94. In some embodiments, one or more operations of the method 7400 are implemented in computer instructions that, when executed by a processing device, execute the control system and/or the user portal. Various operations of the method 7400 may be performed by one or more of the computing device 7114, the cloud-based computing system 7116, the motor controller 7120, the pedal 7110, the goniometer 7106, and/or the wristband 7108. The method 7400 may be performed in the same or a similar manner as described above in regards to method 7300.

[1553] At block 7402, the processing device may receive configuration information for a pedaling session. The configuration information may be received via selection by the user on the user portal executing on the computing device, received from the computing device executing the clinical portal, downloaded from the cloud-based computing system, retrieved from a memory device of the computing device executing the user portal, or some combination thereof. For example, the clinician may select the configuration information for a pedaling session of a patient using the clinical portal and upload the configuration information from the computing device to a server of the cloud-based computing system.

[1554] The configuration information for the pedaling session may specify one or more modes in which the electromechanical device is to operate, and configuration information specific to each of the modes, an amount of time to operate each mode, and the like. For example, for a passive mode, the configuration information may specify a position for the pedal to be in on the radially-adjustable couplings and a speed at which to control the electric motor. For the resistive mode, the configuration information may specify an amount of resistive force the electric motor is to apply to rotation of radially-adjustable couplings during the pedaling session, a maximum pedal force that is desired for the user to exert on each pedal of the electromechanical device during the pedaling session, and/or a revolutions per minute threshold for the radially-adjustable couplings. For the active-assisted mode, the configuration information may specify a minimum pedal force and a maximum pedal force that is desired for the user to exert on each pedal of the electromechanical device, a speed to operate the electric motor at which to drive one or both of the radially-adjustable couplings, and so forth.

[1555] In some embodiments, responsive to receiving the configuration information, the processing device may determine that a trigger condition has occurred. The trigger condition may include receiving a selection of a mode from a user, an amount of time elapsing, receiving a command from the computing device executing the clinical portal, or the like. The processing device may control, based on the trigger condition occurring, the electric motor to operate in a resistive mode by providing a resistance to rotation of the pedals based on the trigger condition.

[1556] At block 7404, the processing device may set a resistance parameter and a maximum pedal force parameter based on the amount of resistive force and the maximum pedal force, respectively, included in the configuration information for the pedaling session. The resistance parameter and the maximum force parameter may be stored in a memory device of the computing device and used to control the electric motor during the pedaling session. For example, the processing device may transmit a control signal along with the resistance parameter and/or the maximum pedal force parameter to the motor controller, and the motor controller may drive the electric motor using at least the resistance parameter during the pedaling session.

[1557] At block 7406, the processing device may measure force applied to pedals of the electromechanical device as a user operates (e.g., pedals) the electromechanical device. The electric motor of the electromechanical device may provide resistance during the pedaling session based on the resistance parameter. A force sensor disposed in each pedal and operatively coupled to the motor controller and/or the computing device executing the user portal may measure the force exerted on each pedal throughout the pedaling session. The force sensors may transmit the measured force to a processing device of the pedals, which in turn causes a communication device to transmit the measured force to the processing device of the motor controller and/or the computing device.

[1558] At block 7408, the processing device may determine whether the measured force exceeds the maximum pedal force parameter. The processing device may compare the measured force to the maximum pedal force parameter to make this determination.

[1559] At block 7410, responsive to determining that the measured force exceeds the maximum pedal force parameter, the processing device may reduce the resistance parameter so the electric motor applies less resistance during the pedaling session to maintain the revolutions per minute threshold specified in the configuration information. Reducing the resistance may enable the user to pedal faster, thereby increasing the revolutions per minute of the radially -adjustable couplings. Maintaining the revolutions per minute threshold may ensure that the patient is exercising the affected body part as rigorously as desired during the mode. In response to determining that the measured force does not exceed the maximum pedal force parameter, the processing device may maintain the same maximum pedal force parameter specified by the configuration information during the pedaling session.

[1560] In some embodiments, the processing device may determine than a second trigger condition has occurred. The second trigger condition may include receiving a selection of a mode from a user via the user portal, an amount of time elapsing, receiving a command from the computing device executing the clinical portal, or the like. The processing device may control, based on the trigger condition occurring, the electric motor to operate in a passive mode by independently driving one or more radially -adjustable couplings coupled to the pedals in a rotational fashion. The electric motor may drive the one or more radially -adjustable couplings at a speed specified in the configuration information without another driving source. Also, the electric motor may drive each of the one or more radially-adjustable couplings individually at different speeds.

[1561] In some embodiments, the processing device may determine that a third trigger condition has occurred. The third trigger condition may be similar to the other trigger conditions described herein. The processing device may control, based on the third trigger condition occurring, the electric motor to operate in an active-assisted mode by measuring revolutions per minute of the one or more radially-adjustable couplings coupled to the pedals and causing the electric motor to drive in a rotational fashion the one or more radially- adjustable couplings coupled to the pedals when the measured revolutions per minute satisfy a threshold condition.

[1562] In some embodiments, the processing device may receive, from a goniometer worn by the user operating the electromechanical device, a set of angles of extension between an upper leg and a lower leg at a knee of the user. The set of angles are measured as the user extends the lower leg away from the upper leg via the knee. In some embodiments, the angles of extension may represent angles between extending a lower arm away from an upper arm at an elbow, angles between upper arm and torso, angles between upper leg and torso, and the like. Such angle measurements may enable treatment of arms, legs, shoulder, neck, and/or hips. Further, the processing device may receive, from the goniometer, a set of angles of bend between the upper leg and the lower leg at the knee of the user. The set of angles of bend are measured as the user retracts the lower leg closer to the upper leg via the knee. In some embodiments, the angles of bend represent angles between bending a lower arm closer to an upper arm at an elbow.

[1563] The processing device may determine whether a range of motion threshold condition is satisfied based on the set of angles of extension and the set of angles of bend. Responsive to determining that the range of motion threshold condition is satisfied, the processing device may modify a position of one of the pedals on one of the radially-adjustable couplings to change a diameter of a range of motion of the one of the pedals. Satisfying the range of motion threshold condition may indicate that the affected body part is strong enough or flexible enough to increase the range of motion allowed by the radially-adjustable couplings.

[1564] FIGURE 98 illustrates example operations of a method 7500 for measuring angles of bend and/or extension of a lower leg relative to an upper leg using a goniometer according to certain embodiments of this disclosure. In some embodiments, one or more operations of the method 7500 are implemented in computer instructions that are executed by the processing devices of the goniometer 7106 of FIGURE 94. The method 7500 may be performed in the same or a similar manner as described above in regards to method 7300.

[1565] At block 7502, the processing device may receive a set of angles from the one or more goniometers. The goniometer may measure angles of extension and/or bend between an upper body part (leg, arm, torso, neck, head, etc.) and a lower body part (leg, arm, torso, neck head, hand, feet, etc.) as the body parts are extended and/or bent during various sessions (e.g., pedaling session, walking session, extension session, bend session, etc.). The set of angles may be received while the user is pedaling one or more pedals of the electromechanical device.

[1566] At block 7504, the processing device may transmit, via one or more network interface cards, the set of angles to a computing device controlling the electromechanical device. The electromechanical device may be operated by a user rehabilitating an affected body part. For example, the user may have recently had surgery to repair a second or third degree sprain of an anterior cruciate ligament (ACL). Accordingly, the goniometer may be seemed proximate to the knee mound the upper and lower leg by the affected ACL.

[1567] In some embodiments, transmitting the set of angles to the computing device controlling the electromechanical device may cause the computing device to adjust a position of one of one or more pedals on a radially-adjustable coupling based on the set of angles satisfying a range of motion threshold condition. The range of motion threshold condition may be set based on configuration information for a treatment plan received from the cloud-based computing system or the computing device executing the clinical portal. The position of the pedal is adjusted to increase a diameter of a range of motion transited by an upper body part (e.g., leg), lower body part (e.g., leg), and a joint (e.g., knee) of the user as the user operates the electromechanical device. In some embodiments, the position of the pedal may be adjusted in real-time while the user is operating the electromechanical device. In some embodiments, the user portal may present a notification to the user indicating that the position of the pedal should be modified, and the user may modify the position of the pedal and resume operating the electromechanical device with the modified pedal position.

[1568] In some embodiments, transmitting the set of angles to the computing device may cause the computing device executing the user portal to present the set of angles in a graphical animation of the lower body part and the upper body part moving in real-time during the extension or the bend. In some embodiments, the set of angles may be transmitted to the computing device executing the clinical portal, and the clinical portal may present the set of angles in a graphical animation of the lower body part and the upper body part moving in real-time during the extension or the bend. In addition, the set of angles may be presented in one or more graphs or charts on the clinical portal and/or the user portal to depict progress of the extension or bend for the user. [1569] FIGURES 99-105 illustrate various detailed views of the components of the rehabilitation system disclosed herein.

[1570] For example, FIGURE 95 illustrates an exploded view of components of the exercise and rehabilitation electromechanical device 7104 according to certain embodiments of this disclosure. The electromechanical device 7104 may include a pedal 7110 that couples to a left radially-adjustable coupling 7124 via a left pedal arm assembly 7600 disposed within a cavity of the left radially-adjustable coupling 7124. The radially-adjustable coupling 7124 maybe disposed in a circular opening of a left outer cover 7601 and the pedal arm assembly 7600 may be secured to a drive sub-assembly 7602. The drive sub-assembly 7602 may include the electric motor 7122 that is operatively coupled to the motor controller 7120. The drive sub-assembly 7602 may include one or more braking mechanisms, such as disk brakes, that enable instantaneously locking the electric motor 7122 or stopping the electric motor 7122 over a period of time. The electric motor 7122 may be any suitable electric motor (e.g., a crystallite electric motor). The drive sub-assembly 7602 may be secured to a frame sub-assembly 7604. A top support sub-assembly 7606 may be seemed on top of the drive sub-assembly 7602.

[1571] A right pedal 7110 couples to a right radially-adjustable coupling 7124 via a right pedal arm assembly 7600 disposed within a cavity of the right radially-adjustable coupling 7124. The right radially- adjustable coupling 7124 may be disposed in a circular opening of a right outer cover 7608 and the right pedal arm assembly 7600 may be secured to the drive sub-assembly 7602. An internal volume may be defined when the left outer cover 7601 and the right outer cover 7608 me secured together mound the frame sub-assembly 7604. The left outer cover 7601 and the right outer cover 7608 may also make up the frame of the device 7104 when secured together. The drive sub-assembly 7602, top support sub-assembly 7606, and pedal arm assemblies 7600 may be disposed within the internal volume upon assembly. A storage compartment 7610 may be secured to the frame.

[1572] Further, a computing device m assembly 7612 may be secured to the frame and a computing device mount assembly 7614 may be secured to an end of the computing device arm assembly 7612. The computing device 7102 may be attached or detached from the computing device mount assembly 7614 as desired during operation of the device 7104.

[1573] FIGURE 100 illustrates an exploded view of a pedal arm assembly 7600 according to certain embodiments of this disclosure. The pedal arm assembly 7600 includes a stepper motor 7700. The stepper motor 7700 may be any suitable stepper motor. The stepper motor 7700 may include multiple coils organized in groups referred to as phases. Each phase may be energized in sequence to rotate the motor one step at a time. The control system may use the stepper motor 7700 to move the position of the pedal on the radially-adjustable coupling.

[1574] The stepper motor 7700 includes a barrel and pin that are inserted through a hole in a motor mount

7702. A shaft coupler 7704 and a bearing 7706 include through holes that receive an end of a first end leadscrew 7708. The leadscrew 7708 is disposed in a lower cavity of a pedal arm 7712. The pin of the electric motor may be inserted in the through holes of the shaft coupler 7704 and the bearing 7706 to secure to the first end of the leadscrew 7708. The motor mount 7702 may be secured to a frame of the pedal arm 7712. Another bearing 7706 may be disposed on another end of the leadscrew 7708. An electric slip ring 7710 may be disposed on the pedal arm 7712.

[1575] A linear rail 7714 is disposed in and secured to an upper cavity of the pedal arm 7712. The linear rail 7714 may be used to move the pedal to different positions as described further below. A number of linear bearing blocks 7716 are disposed onto a top rib and a bottom rib of the linear rail 7714 such that the bearing blocks 7716 can slide on the ribs. A spindle carriage 7718 is secured to each of the bearing blocks 7716. A support bearing 7720 is used to provide support. The lead screw 7708 may be inserted in through hole 7722 of the spindle carriage 7718. A lead screw unit 7721 may be secured at an end of the through hole 7722 to house an end of the lead screw 7708. A spindle 7724 is attached to a hole of the spindle carriage 7718. The end of the spindle 7724 protrudes through a hole of a pedal arm cover 7726 when the pedal arm assembly 7600 is assembled. When the stepper motor 7700 turns on, the lead screw 7708 can be rotated, thereby causing the spindle carriage 7718 to move radially along the linear rail 7714. As a result, the spindle 7724 may radially traverse the opening of the pedal arm cover 7726 as desired.

[1576] FIGURE 101 illustrates an exploded view of a drive sub-assembly 7602 according to certain embodiments of this disclosure. The drive sub-assembly 7602 includes an electric motor 7122. The electric motor 7122 is partially disposed in a crank bracket housing 7800. A side of the electric motor 7122 includes a small molded pulley 7802 secured to it via a small pulley plate 7804 by screws 7806. Also disposed within the crank bracket housing 7800 is a timing belt 7808 and a large molded pulley 7810. The timing belt 7808 may include teeth on an interior side that engage with teeth on the small molded pulley 7802 and the large molded pulley 7810 to cause the large molded pulley 7810 to rotate when the electric motor 7122 operates. The crank bracket housing 7800 includes mounted bearings 7812 on both sides through which cranks 7814 of the large molded pulley 7810 protrude. The cranks 7814 may be operatively coupled to the pedal assemblies.

[1577] FIGURE 102 illustrates an exploded view of a portion of a goniometer according to certain embodiments of this disclosure. The goniometer 7106 includes an upper section 7900 and a lower section 7902. The upper section 7900 and the lower section 7902 are rotatably coupled via a lower leg side brace 7904. A bracket 7903 secures the lower section 7902 to the lower leg brace 7904. A spring 7905 is disposed within an elongated slot in the lower section 7902 and provides loading between parts of the goniometer 7106. A bottom cap 7906 is inserted into a pro traded cavity of the lower leg side brace 7904. In some embodiments the bottom cap 7906 includes a microcontroller 7908. A thrust roller bearing 7910 fits over the protruded cavity of the lower leg side brace, which is inserted into a cavity of the upper section 7900 and secured to the upper section 7900 via a screw 7901 and a washer 7907. Another cavity is located of the upper section 7900 is on a side of the upper section 7900 opposite to the side having the cavity with the inserted protruded cavity. A radial magnet 7912 and a microcontroller (e.g., printed control board) 7914 are disposed in another cavity and a top cap 7916 is placed on top to cover the other cavity. The microcontroller 7908 and/or the microcontroller 7914 may include a network interface card or a radio configured to communicate via a short range wireless protocol (e.g., Bluetooth), a processing device, and a memory device. Further, either or both of the microcontrollers 7908 and 7914 may include a magnetic sensing encoder chip that senses the position of the radial magnet 7912. The position of the radial magnet 7912 may be used to determine an angle of bend or extension of the goniometer 7106 by the processing device(s) of the microcontrollers 7908 and/or 7914. The angles of bend/extension may be transmitted via the radio to the computing device 7102.

[1578] FIGURE 103 illustrates a top view of a wristband 7108 according to certain embodiments of this disclosure. The wristband 7108 includes a strap with a clasp to secure the strap to a wrist of a person. The wristband 7108 may include one or more processing devices, memory devices, network interface cards, and so forth. The wristband 7108 may include a display 71000 configured to present information measured by the wristband 7108. The wristband 7108 may include an accelerometer, gyroscope, and/or an altimeter, as discussed above. The wristband 7108 may also include a light sensor to detect a heartrate of the user wearing the wristband 7108. In some embodiments, the wristband 7108 may also include a light sensor to detect a glucose level of the user wearing the wristband 7108. In some embodiments, the wristband 7108 may include a pulse oximeter to measure an amount of oxygen (oxygen saturation) in the blood by sending infrared light into capillaries and measuring how much light is reflected off the gases. The wristband 7108 may transmit the measurement data to the computing device 7102.

[1579] FIGURE 104 illustrates an exploded view of a pedal 7110 according to certain embodiments of this disclosure. The pedal 7110 includes a molded pedal top 71100 disposed on top of a molded pedal top support plate 71102. The molded pedal top 71100 and the molded pedal top support plate 71102 are seemed to a molded pedal base plate 71104 via screws, for example. The molded pedal base plate 71104 includes one or more strain gauges 71106 configured to measure force exerted on the pedal 7110. The pedal 7110 also includes a molded pedal bottom 71108 where a microcontroller 71110 is disposed. The microcontroller 71110 may include processing devices, memory devices, and/or a network interface card or radio configured to communicate via a short range communication protocol, such as Bluetooth. The one or more strain gauges 71106 me operatively coupled to the microcontroller 71110 and the strain gauges 71106 transmits the measmed force to the microcontroller 71110. The microcontroller 71110 transmits the measured force to the computing device 7102 and/or the motor controller 7120 of the electromechanical device 7104. The molded pedal top 71100, the molded pedal top support plate 71102, the molded pedal base plate 71104 me secured to the molded pedal bottom 71108, which is further secured to a molded pedal bottom cover 71112. The pedal 7110 also includes a spindle 71114 that couples with the pedal arm assembly.

[1580] FIGURE 105 illustrates additional views of the pedal according to certain embodiments of this disclosure. A top view 71200 of the pedal is depicted, a perspective view 71202 of the pedal is depicted, a front view 71204 of the pedal is depicted, and a side view 71206 of the pedal is depicted.

[1581] FIGURES 106-122 illustrate different user interfaces of the user portal 7118. A user may use the computing device 7102, such as a tablet, to execute the user portal 7118. In some embodiments, the user may hold the tablet in their hands and view the user portal 7118 as they perform a pedaling session. Various user interfaces of the user portal 7118 may provide prompts for the user to affirm that they me wearing the goniometer and the wristband, and that their feet are on the pedals.

[1582] FIGURE 106 illustrates an example user interface 71300 of the user portal 7118, the user interface 71300 presenting a treatment plan 71302 for a user according to certain embodiments of this disclosure. The treatment plan 71302 may be received from the computing device 7114 executing the clinical portal 7126 and/or downloaded from the cloud-based computing system 7116. The physician may have generated the treatment plan 71302 using the clinical portal 7126 or the trained machine learning model(s) 7132 may have generated the treatment plan 71302 for the user. As depicted, the treatment plan 71302 presents the type of procedure (“right knee replacement”) that the patient underwent. Further, the treatment plan 71302 presents a pedaling session including a combination of the modes in which to operate the electromechanical device 7104, as well as a respective set period of time for operating each of the modes. For example, the treatment plan 71302 indicates operating the electromechanical device 7104 in a passive mode for 5 minutes, an active-assisted mode for 5 minutes, an active mode for 5 minutes, a resistive mode for 2 minutes, an active mode for 3 minutes, and a passive mode for 2 minutes. The total duration of the pedaling session is 22 minutes and the treatment plan 71302 also specifies that the position of the pedal may be set according to a comfort level of the patient. The user interface 71300 may be displayed as an introductory user interface prior to the user beginning the pedaling session. [1583] FIGURE 107 illustrates an example user interface 71400 of the user portal 7118, the user interface

71400 presenting pedal settings 71402 for a user according to certain embodiments of this disclosure. As depicted graphical representation of feet are presented on the user interface 71400 and two sliders including positions corresponding to portions of the feet. For example, a left slider includes positions LI, L2, L3, L4, and L5. A right slider includes positions Rl, R2, R3, R4, and R5. A button 71404 may be slid up or down on the sliders to automatically adjust the pedal position on the radially -adjustable coupling via the pedal arm assembly. The pedal positions may be automatically populated according to the treatment plan but the user has the option to modify them based on comfort level. The changed positions may be stored locally on the computing device 7102, sent to the computing device 7114 executing the clinical portal 7126, and/or sent to the cloud-based computing system 7116.

[1584] FIGURE 108 illustrates an example user interface 71500 of the user portal 7118, the user interface

71500 presenting a scale 71502 for measuring discomfort of the user at a beginning of a pedaling session according to certain embodiments of this disclosure. The scale 71502 may provide options ranging for no discomfort (e.g., smiley face), mild discomfort, to high discomfort. This discomfort information may be stored locally on the computing device 7102, sent to the computing device 7114 executing the clinical portal 7126, and/or sent to the cloud-based computing system 7116.

[1585] FIGURE 109 illustrates an example user interface 71600 of the user portal 7118, the user interface

71600 presenting that the electromechanical device 7104 is operating in a passive mode 71602 according to certain embodiments of this disclosure. The user interface 71600 presents which pedaling session71604 (session 1) is being performed and how many other pedaling sessions are scheduled for the day. The user interface 71600 also presents an amount of time left in the pedaling session 71604 and an amount of time left in the current mode (passive mode). The full lineup of modes in the pedaling session 71604 are displayed in box 71606. While in the passive mode, the computing device controls the electric motor to independently drive the radially -adjustable couplings so the user does not have to exert any force on the pedals but their affected body part and/or muscles are stretched and warmed up. At any time, if the user so desires, the user may select a stop button 71608, which causes the electric motor to lock and stop the rotation of the radially -adjustable couplings instantaneously or over a set period of time. A descriptive box 71610 may provide instructions related to the current mode to the user.

[1586] FIGURES 1 lOA-110D illustrate an example user interface 71700 of the user portal 7118, the user interface 71700 presenting that the electromechanical device 7104 is operating in active-assisted mode 71702 and the user is applying various amounts of force to the pedals according to certain embodiments of this disclosure. Graphical representations 71704 of feet are presented on the user interface 71700 and the graphical representations may fill up based on the amount of force measured at the pedals. The force sensors (e.g., strain gauge) in the pedal may measure the forces exerted by the user and the microcontroller of the pedal may transmit the force measurements to the computing device 7102. Notifications may be presented when the amount of force is outside of a threshold target force (e.g., either below a range of threshold target force or above the range of threshold target force). For example, in FIGURE 110A, the right foot includes a notification to apply more force with the right foot because the current force measured at the pedal is below the threshold target force.

[1587] A virtual tachometer 71706 is also presented that measures the revolutions per minute of the radially-adjustable couplings and displays the current speed that the user is pedaling. The tachometer 71706 includes areas 71708 (between 0 and 10 revolutions per minute and between 20 and 30 revolutions per minute) that the user should avoid according to their treatment plan. In the depicted example, the treatment plan specifies the user should keep the speed between 10 and 20 revolutions per minute. The electromechanical device 7104 transmits the speed to the computing device 7102 and the needle 71710 moves in real-time as the user operates the pedals. Notifications are presented near the tachometer 71706 that may indicate that the user should keep the speed above a certain threshold revolutions per minute (e.g., 10 RPM). If the computing device 7102 receives a speed from the device 7104 and the speed is below the threshold revolutions per minute, the computing device 7102 may control the electric motor to drive the radially-adjustable couplings to maintain the threshold revolutions per minute.

[1588] FIGURE 110B depicts the example user interface 71700 presenting a graphic 71720 for the tachometer 71706 when the speed is below the threshold revolutions per minute. As depicted, a notification is presented that says “Too slow - speed up”. Also, the user interface 71700 presents an example graphical representation 71721 of the right foot when the pressure exerted at the pedal is below the range of threshold target force. A notification may be presented that reads “Push more withyour right foot.” FIGURE 1 IOC depicts the example user interface 71700 presenting a graphic 71722 for the tachometer 71706 when the speed is within the desired target revolutions per minute. Also, the user interface 71700 presents an example graphical representation 71724 of the right foot when the pressure exerted at the pedal is within the range of threshold target force. FIGURE 110D depicts the example user interface 71700 presenting a graphic 71726 for the tachometer 71706 when the speed is above the desired target revolutions per minute. As depicted, a notification is presented that reads “Too fast - slow down”. Also, the user interface 71700 presents an example graphical representation 71728 of the right foot when the pressure exerted at the pedal is above the range of threshold target force. A notification may be presented that reads “Push less with your right foot.”

[1589] FIGURE 111 illustrates an example user interface 71800 ofthe user portal 7118,theuser interface 71800 presenting a request 71802 to modify pedal position while the electromechanical device 7104 is operating in active-assisted mode 71803 according to certain embodiments of this disclosure. The request 71802 may pop up on a regular interval as specified in the treatment plan. If the user selects the “Adjust Pedals” button, the user portal 7118 may present a screen that allows the user to modify the position of the pedals.

[1590] FIGURE 112 illustrates an example user interface 71900 of the user portal 7118, the user interface 71900 presenting a scale 71902 for measuring discomfort of the user at an end of a pedaling session according to certain embodiments of this disclosure. The scale 71902 may provide options ranging for no discomfort (e.g., smiley face), mild discomfort, to high discomfort. This discomfort information may be stored locally on the computing device 7102, sent to the computing device 7114 executing the clinical portal 7126, and/or sent to the cloud-based computing system 7116.

[1591] FIGURE 113 illustrates an example user interface 72000 of the user portal 7118, the user interface 72000 enabling the user to capture an image of the body part under rehabilitation according to certain embodiments of this disclosure. For example, an image capture zone 72002 is presented on the user interface 72000 and the dotted lines 72004 will populate to show a rough outline of the leg, for example, with a circle to indicate where their kneecap (patella) should be in the image. This enables the patient to line up their leg/knee for the image. The user may select a camera icon 72006 to capture the image. If the user is satisfied with the image, the user can select a save button 72008 to store the image on the computing device 7102 and/or in the cloud-based computing system 7116. Also, the image may be transmitted to the computing device 7114 executing the clinical portal 7126.

[1592] FIGURES 114A-D illustrate an example user interface 72100 of the user portal 7118, the user interface 72100 presenting angles 72102 of extension and bend of a lower leg relative to an upper leg according to certain embodiments of this disclosure. As depicted in FIGURE 114A, the user interface 72100 presents a graphical animation 72104 of the user’s leg extending in real-time. The knee angle in the graphical animation 72104 may match the angle 72102 presented on the user interface 72100. The computing device 7102 may receive the angles 7210 of extension from the goniometer that is worn by the user during an extension session and/or a pedaling session. To that end, although the graphical animation 72104 depicts the user extending their leg during an extension session, it should be understood that the user portal 7118 may be configured to display the angles 72102 in real-time as the user operates the pedals of the electromechanical device 7104 in real-time. [1593] FIGURE 114B illustrates the user interface 72100 with the graphical animation 72104 as the lower leg is extended farther away from the upper leg, and the angle 72102 changed from 84 degrees to 60 degrees of extension. FIGURE 114C illustrates the user interface 72100 with the graphical animation 72104 as the lower leg is extended even farther away from the upper leg. The computing device 7102 may record the lowest angle that the user is able to extend their leg as measured by the goniometer. That angle 72102 may be sent to the computing device 7114 and that lowest angle may be presented on the clinical portal 7126 as an extension statistic for that extension session. Further, a bar 72110 is presented and the bar may fill from left to right over a set amount of time. A notification may indicate that the patient should push down on their knee over the set amount of time. The user interface 72100 in FIGURE 114D is similar to FIGURE 114C but it presents the angle 72102 of bend, measured by the goniometer, as the user retracts their lower leg closer to their upper leg. As depicted, the graphical animation 72104 depicts the angle of the knee matching the angle 72102 presented on the user interface 72100 in real-time. The computing device 7102 may record the highest angle that the user is able to bend their leg as measured by the goniometer. That angle 72102 may be sent to the computing device 7114 and that highest angle may be presented on the clinical portal 7126 as a bend statistic for that bend session.

[1594] FIGURE 115 illustrates an example user interface 72200 of the user portal 7118, the user interface 72200 presenting a progress report 72202 for a user extending the lower leg away from the upper leg according to certain embodiments of this disclosure. The user interface 72200 presents a graph 72204 with the degrees of extension on a y-axis and the days after surgery on the x-axis. The angles depicted in the graph 72204 are the lowest angles achieved each day. The user interface 72202 also depicts the lowest angle the user has achieved for extension and indicates an amount of improvement (83%) in extension since beginning the treatment plan. The user interface 72200 also indicates how many degrees are left before reaching a target extension angle. [1595] FIGURE 116 illustrates an example user interface 72300 of the user portal 7118, the user interface 72300 presenting a progress screen 72302 for a user bending the lower leg toward the upper leg according to certain embodiments of this disclosure. The user interface 72300 presents a graph 72304 with the degrees of bend on a y-axis and the days after surgery on the x-axis. The angles depicted in the graph 72304 are the highest angles of bend achieved each day. The user interface 72202 also depicts the lowest angle the user has achieved for bending and indicates an amount of improvement (95%) in extension since beginning the treatment plan. The user interface 72200 also indicates how many degrees are left before reaching a target bend angle. [1596] FIGURE117 illustrates an example user interface 72400 of the user portal 7118, the user interface

72400 presenting a progress screen 72402 for a discomfort level of the user according to certain embodiments of this disclosure. The user interface 72400 presents a graph 72404 with the discomfort level on a y-axis and the days after surgery on the x-axis. The user interface 72400 also depicts the lowest discomfort level the user has reported and a notification indicating the amount of discomfort level the user has improved throughout the treatment plan.

[1597] FIGURE 118 illustrates an example user interface 72500 of the user portal 7118, the user interface 7118 presenting a progress screen 72502 for a strength of a body part according to certain embodiments of this disclosure. The user interface 72500 presents a graph 72504 with the pounds of force exerted by the patient for both the left leg and the right leg on a y-axis and the days after surgery on the x-axis. The graph 72504 may show an average for left and right leg for a current session. For the number of sessions a user does each day, the average pounds of force for those sessions may be displayed for prior days as well. The user interface 72500 also depicts graphical representations 72506 of the left and right feet and a maximum pound of force the user has exerted for the left and right leg. The maximum pounds of force depicted may be derived from when the electromechanical device is operating in the active mode. The user may select to see statistics for prior days and the average level of active sessions for that day may be presented as well. The user interface 72500 indicates the amount of improvement in strength in the legs and the amount of strength improvement needed to satisfy a target strength goal.

[1598] FIGURE 119 illustrates an example user interface 72600 of the user portal 7118, the user interface 7118 presenting a progress screen 72602 for an amount of steps of the user according to certain embodiments of this disclosure. The user interface 72600 presents a graph 72604 with the number of steps taken by the user on a y-axis and the days after surgery on the x-axis. The user interface 72600 also depicts the highest number of steps the user has taken for amongst all of the days in the treatment plan, the amount the user has improved in steps per day since starting the treatment plan, and the amount of additional steps needed to meet a target step goal. The user may select to view prior days to see their total number of steps they have taken per day.

[1599] FIGURE 120 illustrates an example user interface 72700 of the user portal 7118, the user interface 72700 presenting that the electromechanical device 7104 is operating in a manual mode 72702 according to certain embodiments of this disclosure. During the manual mode 72702, the user may set the speed, resistance, time to exercise, position of pedals, etc. That is, essentially the control system for the electromechanical device 7104 may provide no assistance to operation of the electromechanical device 7104. When the user selects any of the modes in the box 72704, a pedaling session may begin.

[1600] FIGURE 121 illustrates an example user interface 72800 of the user portal 7118, the user interface 72800 presenting an option 72802 to modify a speed of the electromechanical device 7104 operating in the passive mode 72804 according to certain embodiments of this disclosure. The user may slide button 72806 to adjust the speed as desired during the passive mode where the electric motor is providing the driving force of the radially -adjustable couplings.

[1601] FIGURE 122 illustrates an example user interface 72900 of the user portal 7118, the user interface 72900 presenting an option 72902 to modify a minimum speed of the electromechanical device 7104 operating in the active-assisted mode 72904 according to certain embodiments of this disclosure. The user may slide button 72906 to adjust the minimum speed that the user should maintain before the electric motor begins providing driving force.

[1602] FIGURE 123 illustrates an example user interface 73000 of the clinical portal 7118, the user interface 73000 presenting various options available to the clinician/physician according to certain embodiments of this disclosure. The clinical portal 7118 may retrieve a list of patients for a particular physician who logs into the clinical portal 7118. The list of patients may be stored on the computing device 7114 or retrieved from the cloud-based computing system 7116. A first option 73002 may enable the clinician to generate treatment plans for one or more of the patients, as described above. A second option 73004 may enable the clinician to view the number of sessions that each of the patients have completed in 24 horns. This may enable the clinician to determine whether the patients are keeping up with the treatment plan and send notifications to those patients that are not completing the sessions. A third option 73006 may enable the clinician to view the patients who have poor extension (e.g., angle of extension above a target extension for a particular stage in the treatment plan). A fourth option 73008 may enable the clinician to view the patients who have poor flexion (e.g., angle of bend below a target bend for a particular stage in the treatment plan). A fifth option 73010 may enable the clinician to view the patients reporting high pain levels. Regarding any of the options, the clinician can contact the user and inquire as to the status of their lack of participation, extension, flexion, pain level etc. The clinical portal 7126 provides the benefit of direct monitoring of the patients progress by the clinician, which may enable faster and more effective recoveries.

[1603] Further, the clinical portal may include an option to control aspects of operating the electromechanical device 7104. For example, the clinician may use the clinical portal 7126 to adjust a position of a pedal based on angles of extension/bend received from the computing device 7102 and/or the goniometer 7106 in real-time while the user is engaged in a pedaling session or when the user is not engaged in the pedaling session. The clinical portal 7126 may enable the clinician to adjust the amount of resistance provided by the electric motor 7122 in response to determining an amount of force exerted by the user exceeds a target force threshold. The clinical portal 126 may enable the clinician to adjust the speed of the electric motor 7122, and so forth.

[1604] FIGURE 34 illustrates example computer system 73100 which can perform any one or more of the methods described herein, in accordance with one or more aspects of the present disclosure. In one example, computer system 73100 may correspond to the computing device 7102 (e.g., user computing device), the computing device 7114 (e.g., clinician computing device), one or more servers of the cloud-based computing system 7116, the training engine 7130, the servers 7128, the motor controller 7120, the pedals 7110, the goniometer 7106, and/or the wristband 7108 of FIGURE 94. The computer system 73100 may be capable of executing user portal 7118 and/or clinical portal 7126 of FIGURE. 94. The computer system may be connected (e.g., networked) to other computer systems in a LAN, an intranet, an extranet, or the Internet. The computer system may operate in the capacity of a server in a client-server network environment. The computer system may be a personal computer (PC), a tablet computer, a motor controller, a goniometer, a wearable (e.g., wristband), a set-top box (STB), a personal Digital Assistant (PDA), a mobile phone, a camera, a video camera, or any device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that device. Further, while only a single computer system is illustrated, the term “computer” shall also be taken to include any collection of computers that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methods discussed herein.

[1605] The computer system 73100 includes a processing device 73102, a main memory 73104 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM)), a static memory 73106 (e.g., flash memory, static random access memory (SRAM)), and a data storage device 73108, which communicate with each other via a bus 73110.

[1606] Processing device 73102 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device 73102 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing device 73102 may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processing device 73102 is configured to execute instructions for performing any of the operations and steps discussed herein.

[1607] The computer system 73100 may further include a network interface device 73112. The computer system 73100 also may include a video display 73114 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), one or more input devices 73116 (e.g., a keyboard and/or a mouse), and one or more speakers 73118 (e.g., a speaker). In one illustrative example, the video display 73114 and the input device(s) 73116 may be combined into a single component or device (e.g., an LCD touch screen).

[1608] The data storage device 73116 may include a computer-readable medium 73120 on which the instructions 73122 (e.g., implementing control system, user portal, clinical portal, and/or any functions performed by any device and/or component depicted in the FIGURES and described herein) embodying any one or more of the methodologies or functions described herein is stored. The instructions 73122 may also reside, completely or at least partially, within the main memory 73104 and/or within the processing device 73102 during execution thereof by the computer system 73100. As such, the main memory 73104 and the processing device 73102 also constitute computer-readable media. The instructions 73122 may further be transmitted or received over a network via the network interface device 73112.

[1609] While the computer-readable storage medium 73120 is shown in the illustrative examples to be a single medium, the term “computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer-readable storage medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure. The term “computer-readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media.

[1610] None of the description in this application should be read as implying that any particular element, step, or function is an essential element that must be included in the claim scope. The scope of patented subject matter is defined only by the claims. Moreover, none of the claims is intended to invoke 35 U.S.C. § 112(f) unless the exact words “means for” are followed by a participle. [1611] FIGURES 125A-125G illustrate an example rehabilitation system 73200 that utilizes machine learning to generate and monitor a treatment plan of a patient. While one or more embodiments in FIGURES 125A-125G refer to a treatment plan, it is to be understood that these are provided by way of example, and that in practice, the health management server 73202 may generate and recommend any type of health management plan for any type of patient or user. For example, the health management server 73202 may generate a treatment plan for a patient who has undergone surgery or who has a particular illness, injury, condition, or ailment, a prehabilitation plan for an individual who is to undergo surgery or who may have to undergo surgery at a later time period, an exercise plan for an individual trying to improve his or her fitness, and/or the like.

[1612] The rehabilitation system 73200 may include a health management server 73202, the computing device 7102, the electromechanical device 7104, the goniometer 7106, the wristband 7108, and the computing device 7114. The health management server 73202 may be part of the cloud-based computing system 7116, and may include the one or more servers 7128, the training engine 7130, and one or more machine learning models (e.g., the one or more machine learning models 7132).

[1613] FIGURE 125 A illustrates the health management server 73202 receiving training data for training a machine learning model to generate health improvement plans for users (e.g., patients undergoing rehabilitation). For example, the health management server 73202 may receive training data from one or more data storage devices. The training data may be received via an application programming interface (API) and/or another type of communication interface. The training data may include user data for a group of patients who have previously undergone rehabilitation for an injury, condition, or ailment, health improvement plan data for the health improvement plans, sensor data for devices (e.g., electromechanical devices) used for exercises performed as part of the health improvement plans, and/or the like.

[1614] As shown by reference number 73204, the health management server 73202 may receive user data relating to users involved in health improvement plans. For example, the health management server 73202 may receive user data for patients using various electromechanical devices 104 as part of treatment plans for various conditions, injuries, or ailments. A treatment plan may have been completed by a user or the user may be in the process of completing the treatment plan.

[1615] User data for a user may include demographic data relating to one or more demographics of the user, health history data relating to one or more health indicators of the user, and/or the like. The demographic data may specify an age of the user, a race of the user, a sex of the user, an income of the user, and/or the like. The health history data may include data relating to a medical history of the user, data relating to a medical history of one or more family members of the user, data relating to a medical history of one or more individuals with whom the user has been in physical or otherwise proximate contact, data relating to a medical history of one or more physical locations (e.g., hospitals, outpatient clients, doctors’ offices, etc.) where the user has physically been, and/or the like. For example, the health history data may include data that specifies one or more medical conditions of the user, allergies, vital signs recorded over one or more visits with a healthcare professional, notes taken by the healthcare professional, and/or any other information relating to the user’s medical history. The health history data may include information collected before, during, and/or after undergoing a rehabilitation procedure for a condition, injury, or ailment. The notes data may include data relating to a prognosis made by a physician, data relating to a patient description of the condition, injury, or ailment (e.g., symptoms, duration of symptoms, etc.), data relating to a pre-existing condition, injury, or ailment, and/or the like.

[1616] As shown by reference number 73206, the health management server 73202 may receive health improvement plan data relating to health improvement plans of the users. A health improvement plan may include a treatment plan, a rehabilitation plan, a prehabilitation plan, an exercise plan, and/or any other plan capable of improving the health of an individual. For example, a health improvement plan may include an exercise routine that defines exercises a user can complete to strengthen, make more pliable, reduce inflammation and/or swelling in, and/or increase endurance in an area of the body, tasks the user can complete, a start date and end date for the health improvement plan, goals relating to the health improvement plan (e.g., dietary goals, sleep goals, exercise goals, etc.), health improvement plan results, a description and/or identifier of a medical procedure that was performed (or that is to be performed) on the user, and/or the like. The exercises may include a set of pedaling sessions using an electromechanical device 7104, a set of joint extension sessions, a set of flex sessions, a set of walking sessions, a set of heartrates per pedaling session and/or walking session, and/or the like. As will be described further herein, the set of pedaling sessions may be performed using device configurations of an electromechanical device 7104, wherein the device configurations have been optimized for the patient. The device configurations may be optimized to maximize improvements relating to ROM, strength, and/or endurance, optimized to minimize recovery time, and/or optimized to help the patient with any other rehabilitation goals.

[1617] As shown by reference number 73208, the health management server 73202 may receive device data and/or sensor data relating to devices involved in exercise sessions performed as part of the health improvement plan. For example, the health management server 73202 may receive device data from an electromechanical device 7102. The device data may include data relating to a selected exercise routine or session, data relating to a device configuration that corresponds to the selected exercise routine or session, data relating to one or more user-selected preferences, and/or the like.

[1618] Additionally, or alternatively, the health management server 73202 may receive sensor data from one or more monitoring devices (e.g., an electromechanical device 7104, a wristband 7106, a goniometer 7108, a pad, and/or the like). The sensor data may include vital signs data, goniometer data, component data for one or more components of an electromechanical device 7104, and/or the like. For example, a sensor of the electromechanical device 7104 may measure a force exerted by a patient on the pedals during an exercise routine. Additionally, or alternatively, a sensor of the electromechanical device 7104 may measure a distance traveled by the patient during an exercise routine (e g. , based on the number of pedal revolutions completed over an interval).

[1619] Additionally, or alternatively, a wristband 7106 may capture a number of steps taken by a patient over an interval, may measure vital signs of the patient (e.g., heartrate, blood pressure, oxygen level, etc.), and/or the like. Additionally, or alternatively, a goniometer 7108 may measure a range of motion (e.g., angles of extension and/or bend) of a body part to which the goniometer 7108 is attached. Sensor data captured by the one or more monitoring devices may be provided to the health management server 73202.

[1620] In some embodiments, the health management server 73202 may receive one or more other types of training data. For example, the health management server 73202 may receive classification data relating to medical classifications of conditions, injuries, or ailments. The classification data may, for example, include a set of International Classification of Diseases and Related Health Problems (ICD) codes, suchas ICD-10 codes, or Diagnosis-Related Group (DRGs) codes. Additionally, or alternatively, the health management server 73202 may receive feedback data relating to patient feedback of health improvement plans, healthcare professional feedback relating to health improvement plans, and/or the like.

[1621] Additionally, or alternatively, the health management server 73202 may receive safety data relating to a set of constraints approved by one or more healthcare professionals. For example, one or more of the health improvement plans may have been configured to comply with a set of constraints, such as a first constraint relating to one or more maximum permissible ranges of motion on the electromechanical device 7104, a second constraint relating to one or more maximum permissible resistances that can be applied to one or more components of the electromechanical device 7104, a third constraint relating to one or more minimum measures of force permissible to apply to the one or more components of the electromechanical device 7104, and/or the like.

[1622] In some embodiments, the training data may have been stored using one or more cloud storage devices. In some embodiments, the training data may be provided to the health management server 73202 in real-time or near real-time (e g. , provided periodically over a data collection time period) . In some embodiments, the health management server 73202 may receive the training data from one or more cloud storage devices (e.g., rather than needing to be provided the training data in real-time throughout a data collection time period). [1623] In some embodiments, the health management server 73202 may perform one or more pre processing operations to standardize the training data. For example, to use the training data to train a machine learning model, the health management server 73202 may have to perform one or more pre-processing operations to standardize the training data to a uniform format (n.b. a “uniform format” may be referred to as a “canonical format” or a “canonical form,” and the terms as meant as equivalents). As an example, the health management server 73202 may receive training data in multiple formats, multiple file types, and/or the like, and the health management server 73202 may convert one or more types of training data to a uniform format. [1624] The health management server 73202 thus receives the training data that is to be used to train the machine learning model to generate treatment plans for patients.

[1625] FIGURE 125B illustrates the health management server 73202 training a machine learning model to generate treatment plans for patients. While one or more embodiments describe the machine learning model as being trained by the health management server 73202, it is to be understood that this is provided by way of example. In practice, another server or device may train the machine learning model (e.g., a desktop computer of a software developer, etc.) and may provide the trained machine learning model to the health management server 73202 or to another host device that allows the trained machine learning model to be accessed by the health management server 73202 (e.g., using an API or another type of communication interface).

[1626] As shown by reference number 73210, the health management server 3202 may train the machine learning model to generate health improvement plans for users. For example, the health management server 3202 may train the machine learning model to generate health improvement plans optimized for each user. [1627] A machine learning model, as used herein, may refer to a framework able to apply one or more machine learning techniques to analyze input values and to generate output values that are to be used to generate health improvement plans and/or modifications to health improvement plans that are optimal for users. The machine learning model may include a graphical machine learning model, such as a Markov decision process (MDP), a Hidden Markov Model (HMM), a Gaussian Mixture Model (GMM), a model based on a neural network, and/or the like. While one or more embodiments described below refer to the machine learning model as including an MDP, it is to be understood that this is provided by way of example. In practice, the machine learning model may include a neural network, any other type of model driven by machine learning, or any combination of models.

[1628] The one or more machine learning techniques may include one or more supervised machine learning techniques, one or more unsupervised machine learning techniques, one or more reinforcement-driven machine learning techniques, and/or the like. For example, the one or more machine learning techniques may include a classification technique, a regression technique, a clustering technique, and/or any other technique that may be used to train the machine learning model.

[1629] In some embodiments, to train the machine learning model to include an MDP, the health management server 73202 may be configured with (or may generate) a data structure that includes a set of decision states (referred to hereafter as states) and a set of state transitions. An example illustration is provided in FIGURE 125B . The set of states may represent steps or features of health improvement plans, and may include an initial state, sets of intermediary states, and a set of final states. The initial state may include state parameters relating to characteristics of a patient before, during, and/or after surgery. In some embodiments, the initial state parameters may define characteristics of the user before, during, and/or after a trial exercise routine is completed. The state parameters for the initial state may include user data, such as user data relating to demographic information, patient health history (e.g., pre-existing conditions, information impacting overall health, such as whether the user is an athlete, etc.), user vital signs (e.g., a heartrate, a blood pressure level, an oxygen level, and/or the like), physical capabilities of the user (e.g., a range of motion (ROM) of the user, a force the user applied to one or more pedals while exercising on an electromechanical device 104, etc.), and/or the like. [1630] In some embodiments, one or more sets of intermediary states may be used to define steps or features of a health improvement plan. For example, an intermediary state may include state parameters relating to characteristics of steps or features of the health improvement plan. For example, the intermediary state parameters may include a state parameter relating to a duration of an exercise routine, a state parameter relating to a mode in which the electromechanical device 7104 is to engage and/or a duration during which the electromechanical device 7104 is to be engaged in that mode, a state parameter identifying a target heartrate for a patient while performing the exercise routine, and/or the like. Additionally, or alternatively, the intermediary state parameters may include state parameters relating to instructions for aerobic exercises performed off the electromechanical device 7104, such as instructions for a joint extension session, instructions for a flex session, and/or the like.

[1631] The final states may include state parameters identifying specific health improvement plans that can be selected for or presented to a user. For example, if a patient had surgery for an ACL tear, the final states may include health improvement plans with different exercise routines that may be performed while the patient is in rehab. A variety of different exercise routines (and/or a variety of variations to an exercise routine) may be represented by final states. The exercise routines may, for example, vary based on how far along the patient is in the rehabilitation process, how successful a surgery was, the physical fitness of the patient, and/or the like. [1632] In some embodiments, the one or more sets of intermediate states may be segmented into layers. For example, the layers may include a first layer with a subset of states that represent durations of health improvement plans and/or durations of different parts of the health improvement plans, a second layer with a subset of states that represent modes of the electromechanical device 7104 and/or configuration values for one or more configurations that can be implemented during an exercise routine, a third layer with a subset of states that each represent a target number of pedals for the patient to make over an interval, a fourth layer with a subset of states that each represent a target heartrate of the patient over the interval, and/or the like. It is to be understood that this is provided by way of example, and that in practice, the set of states may be segmented into any number of finite layers or related using any number of different data types and/or logical schemes.

[1633] In some embodiments, the data structure (e.g., a data model) supporting the MDP may relate states to each other using a set or sets of state transitions. In some embodiments, a state transition may include a value that represents a probability that transitioning from a source state to a destination state will be an optimal transition (e.g., relative to one or more other transition values relating to the destination state). For example, a set of intermediary states may represent proposed durations of an exercise routine. Each respective state may be initially configured with equal probability values. As will be described, certain input values may cause the probability values to change in order to recommend an optimal health improvement plan for a user. For example, if a user has a history of injuries, exercising for long time periods may increase a likelihood of injury or re injury. In this example, the health management server 73202 may train the machine learning model such that lower probability values are assigned to states with longer exercise routine durations (e.g., based on the longer exercise routine durations being linked to increased risk of injury or re-injury).

[1634] As used herein, a health improvement plan for the user may be an optimal health improvement plan based on the health improvement plan decreasing a likelihood of injury of re-injury (relative to other health improvement plans), decreasing a recovery time for an injury (relative to other health improvement plans), increasing a ROM, strength, and/or endurance of the user (e.g., by an amount that is greater than an increase the user would have using a health improvement plan that is not driven by machine learning), and/or the like. [1635] One or more embodiments herein refer to probabilities or probability values. It is to be understood that this is provided by way of example, and that in practice, the state transitions of the MDP may be implemented using one or more non-parametric (i.e., ranked) means. Further, the probabilities or probability values may, in one or more embodiments herein, represent Bayesian probabilities.

[1636] In some embodiments, the health management server 73202 may train the machine learning model to generate health improvement plans for users. For example, the health management server 73202 may process the training data using one or more machine learning techniques, such that the machine learning model is configured to receive training data values and, based on the training data values, to assign state transition probabilities to state transitions. The health management server 73202 may select a combination of states associated with highest state transition probabilities, where selected states collectively represent a health improvement plan generated for a user. Additionally, the health management server 73202 may compare state data for the selected states with outcome data for known outcomes in order to indicate whether certain health improvement plans were successful, to indicate a degree to which said plans were successful, to indicate a degree to which said plans were optimal for a particular user or characteristic of a user, and/or the like. Based on comparing the state data with the outcome data, the health management server 73202 may update programming used to assign the state transition values. For example, the health management server 73202 may update programming by adjusting threshold values used to assign the state transition values. The state transition values may be used to generate a set of machine learning scores, as described further herein.

[1637] A machine learning score may relate to, without limitation, one or more of each of a risk value (e.g., a risk score), a configuration value (e.g., a configuration score, such as a score for a configuration of the electromechanical device 7104), and/or any other score or value capable of being used to generate a machine learning score, or to one or more of only some of the foregoing values. The risk value and/or the configuration value may be represented as a probability (e.g., a percentage or decimal indicating the likelihood and/or expected value of the occurrence (or the not occurring) of an event or set of events), a confidence interval, a non- probabilistic value (e.g., a non-parametric value or rank order), a numerical value, a summation, an expected value, and/or the like. For example, a machine learning score may relate to a risk score that represents a probability of a change to a health indicator of the user. To provide a specific example, if a device configuration is implemented on the electromechanical device 104 while the user performs the exercise session, a risk score may represent a probability of such performance causing an injury to the user.

[1638] Additionally, or alternatively, and provided as another example, a configuration score may represent a probability that an implemented device configuration is an optimal device configuration for a user or a probability that a modification to the implemented device configuration is an optimal modification. A device configuration may be optimal (or a modification may be optimal) based on a likelihood of the device configuration or modification improving and/or maximizing, given a particular context, a health indicator of a user. For example, depending on the context, a device configuration may be optimal if the device configuration improves or maximizes a recovery time and/or life expectancy of the user, improves or maximizes a ROM of the user, and/or improves or maximizes any other value or metric capable of measuring a health indicator of the user. Context that can affect optimality may include demographic information, medical history, accessibility to medical care, user work ethic, and/or the like. In this context, “improve” or “maximize” may refer to something that is greater (e.g., a strength measurement) or something that is lesser (e.g., probability of dying in the next year) or something that is the same (e.g., homeostasis).

[1639] In some embodiments, the health management server 73202 may train the machine learning model such that the machine learning model is configured to generate real-time modifications to a health improvement plan. For example, the health management server 73202 may receive training data that includes sensor data related to progress users have made in rehabilitation plans. In this example, the health management server 73202 may use one or more machine learning techniques to process the sensor data and to generate, based on processing the sensor data, one or more configuration scores. The one or more configuration scores represent one or more probabilities that an implemented device configuration is an optimal device configuration for a user and/or that represent one or more probabilities that a modification to the implemented device configuration is an optimal modification.

[1640] In some embodiments, a first module of the machine learning model may be trained to generate a health improvement plan and a second module of the machine learning model may be trained to generate real time or near real-time modifications to the health improvement plan. In some embodiments, a first machine learning model may be trained to generate the health improvement plan and a second machine learning model may be trained to generate the real-time modifications to the health improvement plan. Generation or transmission of data may occur in real-time or near real-time. As used herein, real-time may refer to less than 2 seconds, or any other suitable amount of time. Near real-time may refer to 2 or more seconds. For example, near real-time may include a range of 2-5 seconds, 2-10 seconds, or any other suitable amount of time.

[1641] In this way, the health management server 73202 trains the machine learning model to be able to generate health improvement plans for users and/or updates to health improvement plans for the users.

[1642] FIGURE 125C illustrates the health management server 73202 using machine learning to determine a health improvement plan for a user, such as a treatment plan for a patient undergoing rehabilitation. As shown by reference number 73212, the health management server 73202 may receive user data relating to an operator of the electromechanical device 7104. For example, the user may be a patient who has had a surgery performed on a particular body part and who may be taking part in a rehabilitation program that includes exercising on the electromechanical device 7104. The user data may be provided to the health management server 73202 to allow the health management server 73202 to process the user data when generating the rehabilitation plan.

[1643] In some embodiments, the patient may interact with a patient portal to consent to have the patient’s user data provided to the health management server 73202. For example, the patient may have access to a patient portal used to sign up for the rehabilitation program. The patient portal may request that the patient consent to providing user data such that the user data may be processed and used to recommend an optimal rehabilitation plan. In some embodiments, a healthcare professional may interact with a clinical portal to provide the patient’s user data to the health management server 73202. For example, the patient may have provided consent and a healthcare professional may interact with an interface of the clinical portal to provide the patient’ s user data to the health management server 73202.

[1644] In some embodiments, the health management server 73202 may already store the user data (e.g., in a data structure) or may already have access to the user data via any suitable source. In this case, the health management server 73202 may reference the data structure to identify or obtain the user data for further processing from the source.

[1645] While one or more embodiments describe a rehabilitation plan for a patient in a rehabilitation program to recover from surgery, it is to be understood that this is provided by way of example. In practice, the health management server 73202 may generate rehabilitation plans for any number of different health related reasons, such as to heal an injury or ailment, to prevent injury or re-injury, to improve a condition of the user, to improve an overall health status of the user, and/or the like. Furthermore, in some embodiments, the health management server 73202 may generate one or more other types of health improvement plans, such as prehabilitation plans, exercise plans, and/or the like.

[1646] As shown by reference number 73214, the health management server 73202 may use the machine learning model to generate a rehabilitation plan for the user. For example, the health management server 73202 may provide the user data as an input to the machine learning model to cause the machine learning model to generate a set of machine learning scores. The set of machine learning scores may include one or more risk scores relating to probabilities of a change in one or more health indicators of the user, one or more configuration scores relating to different rehabilitation plans being an optimal rehabilitation plan for the user, and/or the like. [1647] As an example, the health management server 73202 may receive health history data for the user that includes data relating to the user having a history of recurring knee problems, data relating to the user having above average physical strength and conditioning, data relating to the user having a history of participating in sports, and/or the like. In this example, the health management server 73202 may provide the user data as an input to the MDP to cause the MDP to generate a set of machine learning scores. The set of machine learning scores may correspond to a set of available rehabilitation plans, where one of the machine learning scores represents a highest probability of a given rehabilitation plan being an optimal rehabilitation plan for the user. [1648] As shown by reference number 73216, the health management server 73202 may provide the rehabilitation plan to the computing device 7116-1. In some embodiments, the computing device 7116-1 may be a device accessible to a healthcare professional. The rehabilitation plan may be provided via a communication interface, such as an API or another type of communication interface.

[1649] As shown by reference number 73218, a medical professional may interact with the computing device 7116-1 to review, modify, and/or approve the rehabilitation plan. In some embodiments, the medical professional may, during a telemedicine session or telehealth session, interact with the interface of the clinical portal. In some embodiments, the medical professional may interact with an interface of the clinical portal to review and approve the rehabilitation. In this case, the interface may display the rehabilitation plan and the medical professional may review and submit the medical professional’s approval of the rehabilitation plan. In some embodiments, the medical professional may interact with the interface of the clinical portal to modify and approve the rehabilitation plan. In this case, the interface may display the rehabilitation plan and the medical professional may interact with the interface by marking up the rehabilitation plan, by selecting one or more modifications from a drop-down menu, by inputting one or more modifications as free-form text, and/or the like.

[1650] In some embodiments, the medical professional may interact with the interface of the clinical portal to reject the rehabilitation. In this case, the medical professional may interact with the interface to input one or more suggested changes for generating a new rehabilitation plan. When the medical professional finalizes the one or more suggested changes, data relating to the suggestions may be provided back to the health management server 73202. The health management server 73202 may then use the one or more suggestions to retrain the machine learning model such that programming used to generate outputs may be updated based on such suggestions.

[1651] As shown by reference number 73220, the computing device 7116-1 may provide to the computing device 7116-2 rehabilitation plan data for an approved rehabilitation plan. The computing device 7116-2 may, for example, be a device accessible to the user. In some embodiments, the computing device 7116- 1 may provide the approved rehabilitation plan to the user portal accessible to the user. Additionally, or alternatively, the computing device 7116-1 may provide the approved rehabilitation plan to the computing device 116-2 as an image in a short message service (SMS) message or a private messenger (e.g., Telegram, Signal, skype, Google Hangouts, Facebook Messenger, WickrPro, WickrMe, WhatsApp, snapchat, Instagram, etc.) message. Additionally, or alternatively, the approved rehabilitation plan may be provided to an e-mail account associated with the user and/or to one or more other accounts associated with the user.

[1652] In this way, the health management server 73202 may generate the rehabilitation plan using machine learning and enable the rehabilitation plan to be provided to a reviewing healthcare professional and to the user. In other situations, the electromechanical device 7104 may generate the rehabilitation plan. For example, a lightweight machine learning model may be hosted or supported by the electromechanical device 7104 (e.g., rather than by the health management server 73202), such that the electromechanical device 7104 may generate the rehabilitation plan. The rehabilitation plan may be generated based on the electromechanical device 104 receiving a request from a device of a healthcare professional, based on a user uploading user data and/or other related data about the user’s health history, and/or via another type of trigger.

[1653] FIGURE 125D illustrates the electromechanical device 7104 implementing a device configuration corresponding to an exercise routine of the approved rehabilitation plan. As shown by reference number 73222, the electromechanical device 7104 may provide, to the health management server 73202, a message identifying an exercise routine that the user selected for an exercise session of the rehabilitation plan. For example, the user may interact with an interface of the electromechanical device that displays exercise routines capable of being performed by the user. In this case, the user may select an exercise routine specified in the rehabilitation plan, such that the message identifying the exercise routine is provided to the health management server 73202.

[1654] As shown by reference number 73224, the health management server 73224 may select the device configuration corresponding to the exercise routine of an exercise session of the rehabilitation plan. For example, the health management server 73202 may use an exercise routine identifier to reference a data structure that associates the exercise routine identifier with a corresponding device configuration.

[1655] The device configuration may include mode data related to one or more modes in which the electromechanical device 7104 is capable of operating during the exercise session. The mode data may include a first component configuration including data related to one or more positions at which to configure one or more components of the electromechanical device 7104, a second component configuration including data related to one or more forces to apply to the one or more components of the electromechanical device 104, a user interface configuration including data related to exercise instructions for the exercise session, wherein the exercise instructions are capable of being provided for display via an interface, and/or the like. The first component configuration may define a position at which to configure a seat of the electromechanical device 104, a position at which to configure one or more pedals of the electromechanical device 7104, and/or the like. [1656] As shown by reference number 73226, the health management server 73226 may provide the device configuration corresponding to the exercise routine of the rehabilitation plan to the electromechanical device 7104. For example, the device configuration may be provided to the electromechanical device 7104 to enable the electromechanical device 7104 to implement the device configuration.

[1657] In some embodiments, the electromechanical device 7104 may implement the device configuration. For example, the electromechanical device 7104 may implement the device configuration to adjust a position of a seat, to adjust a position of one or more pedals, to adjust a position of one or more brake mechanisms, to power on one or more motors (e.g., an electric motor, a stepper motor, and/or the like), to power on one or more sensors, to display exercise instructions for the exercise session on an interface associated with the electromechanical device 7104, and/or the like.

[1658] Additionally, or alternatively, the electromechanical device 7104 may implement the device configuration such that an assisting force may be applied to the one or more pedals. For example, a motor or related component may be configured such that torque is applied to the one or more pedals to assist the user in rotating the pedals. The assisting force may be applied based on a trigger condition being satisfied. For example, the assisting force may be applied while the user is performing the exercise session, while a position of a pedal is at a certain angle (e.g., such that the assisting force is applied for a portion of the total 360-degree rotation of the pedal), based on a user interacting with a user interface to request the assisting force, and/or the like. [1659] Additionally, or alternatively, the electromechanical device 7104 may implement the device configuration such that a resistive force may be applied to the one or more pedals. For example, one or more braking mechanisms may be configured such that a resistive force increases an amount of force needed by the user to rotate the one or more pedals. The resistive force may be applied based on a trigger condition being satisfied.

[1660] Additionally, or alternatively, the electromechanical device 7104 may implement the device configuration such that one or more sensors may be configured to monitor progress of the user while the user is performing the exercise routine. For example, the one or more sensors may be configured to monitor and report vital signs of the user, angles of extension of bend of at least one body part of the user, force the user applies to the one or more pedals, and/or the like.

[1661] In this way, the electromechanical device 7104 is enabled to implement the device configuration.

[1662] FIGURE 125E illustrates one or more sensors capturing and providing sensor data to the health management server 73202. The sensor data may be related to determining the user’s progress in the rehabilitation plan.

[1663] As shown by reference number 73218, the computing device 7102 may provide a first set of sensor data to the health management server 73202. For example, one or more sensors, such as one or more strain gauges, may be configured to measure a force that the user applies to one or more pedals of the electromechanical device 7104. This allows the computing device 7102 to provide the health management server 73202 with a first set of sensor data related to one or more measurements of force that the user applies to the one or more pedals.

[1664] As shown by reference number 73220, the wristband 7106 may provide a second set of sensor data to the health management server 73202. For example, the wristband 7106 may include sensors such as an accelerometer, a gyroscope, an altimeter, a light sensor, a pulse oximeter, and/or the like. The sensors of the wristband 7106 may generate the second set of sensor data by monitoring the user throughout the exercise routine and a processor of the wristband 7106 may provide the second set of sensor data to the health management server 73202.

[1665] As an example, the wristband 7106 may be configured to use the light sensor to detect a heart rate of the user. Additionally, or alternatively, and as provided in another example, the wristband 7106 may be configured to use the pulse oximeter to measure an amount of oxygen in the blood of the user (e.g., by sending infrared light into capillaries and measuring how much light is reflected off the gases). Sensor data (e.g., vital signs data) relating to the heart rate of the user and to the amount of oxygen in the user’s blood may be provided to the health management server 73202.

[1666] As shown by reference number 73222, the goniometer 7108 may provide a third set of sensor data to the health management server 73202. For example, the goniometer 7108 may include a radial magnet and one or more processors with a magnetic sensing encoder chip capable of sensing a position of the radial magnet. The position of the magnet may be measured periodically and used to determine one or more angles of extension or bend. A third set of sensor data relating to the one or more angles of extension or bend may be provided to the health management server 73202.

[1667] In this way, sensor data related to determining the user’s progress in the rehabilitation plan may be provided to the health management server 73202.

[1668] FIGURE 125F illustrates the health management server 73202 performing one or more actions to optimize the exercise routine of the user. Optimizing the exercise routine may include modifying the device configuration to reduce a likelihood that the user is injured, to improve a rate at which the user strengthens an area of the body targeted for rehabilitation, and/or the like.

[1669] As shown by reference number 73224, the health management server 73202 may select a modification to the device configuration based on the sensor data. In some embodiments, the health management server 73202 may provide the sensor data as an input to the machine learning model such that the machine learning model is configured to output a set of machine learning scores. The set of machine learning scores may relate to (e.g., be stored in association with) a set of configuration values capable of being used to modify the device configuration. A machine learning score may represent a confidence that implementing a particular configuration value will optimize the exercise routine for the user (relative to a current device configuration implementation, relative to implementing one or more other configuration values, etc.). For example, a scale of 1-100 may be implemented, wherein a value of one represents a low confidence (e.g., or no confidence) that implementing a particular configuration value will optimize the exercise routine for the user and a value of one hundred represents a high confidence (e.g., or absolute confidence, i.e., certainty) that implementing the particular configuration value will optimize the exercise routine for the user.

[1670] In some embodiments, the health management server 73202 may select, as the modification, a configuration value relating to a highest available machine learning score. In some embodiments, the health management server 73202 may select one or more configuration values based on one or more corresponding machine learning scores satisfying a threshold machine learning score. For example, the health management server 73202 may compare the set of machine learning scores and the threshold machine learning score and may determine that one or more machine learning scores satisfy the threshold machine learning score. In this case, the health management server 73202 may select, as the modification, one or more configuration values corresponding to the one or more machine learning scores.

[1671] As shown by reference number 73226, the health management server 73202 may provide the modification to the device configuration to the motor controller 7120 of the electromechanical device 7104. As shown by reference number 73228, the electromechanical device 7104 may implement the modification to the device configuration. For example, the electromechanical device 7104 may implement the modification by reading the one or more configuration values and adjusting the device configuration based on the one or more configuration values.

[1672] In this way, the health management server 73202 enables the electromechanical device 7104 to implement the modification to the device configuration.

[1673] FIGURE I25G illustrates the health management server 73202 generating and providing a message. As shown by reference number 73230, the health management server 73202 may generate a message (written, spoken, visually displayed, etc.) based on a machine-leaming-driven analysis of the sensor data. For example, the health management server 73202 may provide the sensor data as an input to the machine learning model, such that the machine learning model is configured to output one or more risk scores that represent a probability of change to a health indicator of the user. The health management server 73202 may determine whether the one or more risk scores satisfy a threshold risk score and may generate the message for the user based on the one or more risk scores satisfying the threshold risk score.

[1674] The text of the message may include a warning message that the user is exercising in a manner that may increase a likelihood of injury or delaying the rehabilitation process, a confirmation message indicating that the user is exercising within an optimal ROM or at an optimal speed, a recommendation to modify the device configuration, a recommendation for the user to change form or posture while performing the exercise routine, a recommendation for the user to change an amount of force exerted on one or more pedals of the electromechanical device 104, and/or the like.

[1675] As shown by reference number 73232-1, the health management server 73202 may provide the message to the electromechanical device 7104. For example, the health management server 73202 may provide the message for display via an interface associated with the electromechanical device 7104. The interface may be an interface of the user portal, an interface of an exercise application running on the electromechanical device, and/or the like.

[1676] As shown by reference number 73232-2, the health management server 73202 may provide the message to the computing device 7116. For example, the health management server 73202 may provide the message to the computing device 7116-1 accessible to the healthcare professional, to the computing device 7116-2 accessible to the user, and/or the like.

[1677] As shown by reference number 73234-1, the electromechanical device 7104 may display the message. For example, the message may be displayed such that the user is enabled to view the message while performing the exercise routine. As shown by reference number 73234-2, the computing device 7116 may display the message. For example, the message may be displayed on the computing device 7116-1 associated with the healthcare professional and/or on the computing device 7116-2 associated with the user.

[1678] One or more embodiments described herein may be implemented during a telemedicine or telehealth session with a medical professional. For example, the rehabilitation plans (and/or other rehabilitation or prehabilitation plans not selected) may be presented, during a telemedicine or telehealth session, to a medical professional. The medical professional may select a particular rehabilitation plan for the patient to cause that rehabilitation plan to be transmitted to the patient and/or to control, based on the rehabilitation plan, the electromechanical device 7104. In some embodiments, to facilitate telehealth or telemedicine applications, including remote diagnoses, determination of rehabilitation plans and rehabilitative and/or pharmacologic prescriptions, the health management server 73202 may receive and/or operate distally from the patient and the electromechanical device 7104. In such cases, the recommended rehabilitation plans and/or other rehabilitation or prehabilitation plans may be presented simultaneously with a video of the patient in real-time or near real time during a telemedicine or telehealth session on a user interface of a computing device of a medical professional (e.g., computing device 7116-1).

[1679] By using machine learning to process received data, the health management server 73202 may generate a health improvement plan that is optimal for the user. For example, the health management server 73202 may generate a health improvement plan that includes an exercise session, where the exercise session may be performed by the user when a device configuration is implemented on the electromechanical device 7104. The device configuration allows the exercise session to be performed using an optimal ROM, performed at an optimal strength, and/or performed at an optimal endurance. Additionally, by using machine learning to generate an optimal health improvement plan that accounts for a number of factors that influence optimality (e.g., user demographics, medical history, surgical results, and/or the like), the health management server 73202 reduces a likelihood of injury or re-injury and improves a speed at which the user can recover. This reduces a utilization of resources (e.g., power resources, processing resources, network resources, and/or the like) of the electromechanical device 7104 and related devices relative to using an inferior plan more likely to injure or re injure the user and that will require more time to recover.

[1680] FIGURE 126 illustrates a method 73300 for using machine learning to generate a health improvement plan for a user and for enabling an electromechanical device to implement a device configuration for an exercise session that is part of the health improvement plan. In some embodiments, the method 3300 is implemented on a health management server, such as the health management server 3202 shown in FIGURES 125 A-G. In some embodiments, the health management server may be part of the cloud-based computing system 7116. The method 73300 may include operations implemented in computer instructions stored in a memory and executed by a processor of the health improvement server.

[1681] At block 73302, the method 73300 may include receiving user data for a user capable of operating an electromechanical device. For example, the health improvement server may receive user data for a user capable of operating the electromechanical device. The user data may include health history data related to one or more health indicators of the user.

[1682] At block 73304, the method 73300 may include generating a health improvement plan by using a machine learning model to process the user data, wherein the health improvement plan includes an exercise session to be performed on the electromechanical device. For example, the health improvement server may generate a health improvement plan by using a machine learning model to process the user data, wherein the health improvement plan includes an exercise session to be performed on the electromechanical device.

[1683] At block 73306, the method 73300 may include providing the health improvement plan to one or more user portals. For example, the health improvement server may provide the health improvement plan to one or more user portals, such as a patient portal, a clinical portal, an administrative (admin) portal, a software developer portal, and/or the like.

[1684] At block 73308, the method 73300 may include selecting, for the electromechanical device, a device configuration that corresponds to the health improvement plan. For example, the health improvement server may select, for the electromechanical device, a device configuration that corresponds to the health improvement plan. The device configuration may include mode data related to one or more modes the electromechanical device is capable of operating during the exercise session.

[1685] At block 73310, the method 73300 may include providing the device configuration to the electromechanical device such that the electromechanical device is enabled to implement the device configuration. For example, the health improvement server may provide the device configuration to the electromechanical device such that the electromechanical device is enabled to implement the device configuration.

[1686] FIGURE. 127 shows an example embodiment of a method 73400 for receiving a selection of an optimal treatment plan and controlling, based on the optimal treatment plan, a treatment apparatus (e.g., the electromechanical device 7104) while the patient uses the treatment apparatus according to the present disclosure. Method 73400 includes operations performed by processors of a computing device (e.g., any component of FIG. 94, such as server 7128 executing the training engine 7130). In some embodiments, one or more operations of the method 73400 are implemented in computer instructions stored on a memory device and executed by a processing device. The method 73400 may be performed in the same or a similar manner as described above in regard to method 73300. The operations of the method 73400 may be performed in some combination with any of the operations of any of the methods described herein.

[1687] Prior to the method 73400 being executed, various optimal treatment plans may be generated by one or more trained machine learning models 7132 of the training engine 7130. For example, based on a set of treatment plans pertaining to a medical condition of a patient, the one or more trained machine learning models 7132 may generate the optimal treatment plans. The various treatment plans may be transmitted to one or computing devices of a patient and/or medical professional.

[1688] At 73402 of the method 73400, the processing device may receive a selection of an optimal treatment plan from the optimal treatment plans. The selection may have been entered on a user interface presenting the optimal treatment plans on the patient interface and/or the assistant interface.

[1689] At 73404, the processing device may control, based on the selected optimal treatment plan, the treatment apparatus while the patient uses the treatment apparatus. In some embodiments, the controlling is performed distally by the server 7128. If the selection is made using a patient interface, one or more control signals may be transmitted from the patient interface to the treatment apparatus to configure, according to the selected treatment plan, a setting of the treatment apparatus to control operation of the treatment apparatus. Further, if the selection is made using an assistant interface, one or more control signals may be transmitted from the assistant interface to the treatment apparatus to configure, according to the selected treatment plan, a setting of the treatment apparatus to control operation of the treatment apparatus.

[1690] It should be noted, that as the patient uses the treatment apparatus, sensors may transmit measurement data to a processing device. The processing device may dynamically control, according to the treatment plan, the treatment apparatus by modifying, based on the sensor measurements, a setting of the treatment apparatus. For example, if the force measured by the sensors indicates the user is not applying enough force to a pedal, the treatment plan may indicate to reduce the required amount of force for an exercise.

[1691] It should be noted, that as the patient uses the treatment apparatus, the user may use the patient interface to enter input pertaining to a pain level experienced by the patient as the patient performs the treatment plan. For example, the user may enter a high degree of pain while pedaling with the pedals set to a certain range of motion on the treatment apparatus. The pain level may cause the range of motion to be dynamically adjusted based on the treatment plan. For example, the treatment plan may specify alternative range of motion settings if a certain pain level is indicated when the user is performing an exercise at a certain range of motion.

[1692] Clause 1.6. A method for using machine learning to control an electromechanical device, comprising: receiving user data for a user capable of operating the electromechanical device, wherein the user data comprises health history data related to one or more health indicators of the user; generating a health improvement plan by using a machine learning model to process the user data, wherein the health improvement plan includes an exercise session to be performed on the electromechanical device; providing the health improvement plan to one or more user portals; selecting, for the electromechanical device, a device configuration that corresponds to the health improvement plan, wherein the device configuration comprises mode data related to one or more modes the electromechanical device is capable of operating during the exercise session; and providing the device configuration to the electromechanical device such that the electromechanical device is enabled to implement the device configuration.

[1693] Clause 2.6. The method of any clause herein, wherein the mode data comprises at least one of: a first component configuration comprising data related to one or more positions at which to configure one or more components of the electromechanical device, a second component configuration comprising data related to one or more forces to apply to the one or more components of the electromechanical device, a third component configuration comprising data related to one or more speeds to apply to the one or more components of the electromechanical device, and” a user interface configuration comprising data related to exercise instructions for the exercise session, wherein the exercise instructions are capable of being provided for display via an interface.

[1694] Clause 3.6. The method of any clause herein, further comprising: receiving sensor data comprising one or more values related to determining the user’ s progress in the rehabilitation plan; providing the sensor data as an input to the machine learning model such that the machine learning model is configured to output a set of machine learning scores, wherein the set of machine learning scores relates to a set of configuration values capable of being used to modify the device configuration; selecting one or more configuration values from the set of configuration values, based on the one or more configuration values relating to a machine learning score that satisfies a threshold machine learning score; and providing a modification to the electromechanical device, such modification comprising the one or more configuration values, wherein providing the electromechanical device with the modification enables the electromechanical device to implement the modification.

[1695] Clause 4.6. The method of any clause herein, wherein the sensor data comprises at least one of: vital sign data related to one or more measured vital signs of the user during the exercise session, goniometer data related to one or more measured angles of extension or bend of at least one body part of the user, and component data related to one or more measurements of force that the user applies to components of the electromechanical device.

[1696] Clause 5.6. The method of any clause herein, further comprising: receiving sensor data comprising one or more values related to determining the user’ s progress in the rehabilitation plan; providing the sensor data as an input to the machine learning model such that the machine learning model is configured to output one or more risk scores, wherein such one or more risk scores represent a probability of a change to a health indicator that is one of the one or more health indicators of the user; determining that the one or more risk scores satisfy a threshold risk score; and providing a message to a user portal that is one of the one or more user portals.

[1697] Clause 6.6. The method of any clause herein, wherein the sensor data comprises at least one of: vital sign data related to one or more vital signs of the user during the exercise session, goniometer data related to one or more angles of extension or bend of at least one body part of the user, and component data related to one or more measurements of force applied by the user to one or more components of the electromechanical device.

[1698] Clause 7.6. The method of any clause herein, wherein the message is configured to notify the user of the probability of the change to the health indicator.

[1699] Clause 8.6. The method of any clause herein, wherein the message comprises instructions describing an adjustment the user is to make to the device configuration to reduce the probability of the change to the health indicator.

[1700] Clause 9.6. The method of any clause herein, further comprising: receiving sensor data comprising one or more values related to the user’s progress in the rehabilitation plan; providing the sensor data as an input to the machine learning model such that the machine learning model is configured to output one or more risk scores, wherein such one or more risk scores represent a probability of a change to a health indicator that is one of the one or more health indicators of the user; determining that the one or more risk scores satisfy a threshold risk score; responsive to determining that the one or more risk scores satisfy the threshold risk score, generating a recommendation based on the one or more risk scores; and providing the recommendation to the one or more user portals.

[1701] Clause 10.6. The method of any clause herein, wherein the machine learning model is trained on historical data comprising safety data related to a set of constraints approved by a healthcare professional; and wherein generating the health improvement plan comprises: generating the health improvement plan such that the health improvement plan is configured to comply with the set of constraints.

[1702] Clause 11.6. The method of any clause herein, wherein the set of constraints comprises at least one of: a first constraint comprising one or more maximum permissible ranges of motion, a second constraint comprising one or more maximum permissible resistances, and a third constraint comprising one or more minimum measures of force permissible to apply to one or more components of the electromechanical device.

[1703] Clause 12.6. The method of any clause herein, wherein the machine learning model has been trained to generate the health improvement plan such that, using an optimal ROM, the user is enabled to perform the exercise session, wherein the optimal ROM is correlated with successful outcomes for users with characteristics comprising health indicators or demographics similar to corresponding characteristics of the user [1704] Clause 13.6. The method of any clause herein, wherein providing the device configuration to the electromechanical device comprises: providing the device configuration to a processor of the electromechanical device such that the processor is configured to: use the mode data of the device configuration to identify a mode, out of the one or more modes, that the electromechanical device is to operate, and control one or more of an electric motor and a brake to operate in the mode.

[1705] Clause 14.6. The method of any clause herein, wherein the device configuration is provided to the electromechanical device such that a processor of the electromechanical device is enabled to display exercise instructions via an interface associated with the electromechanical device.

[1706] Clause 15.6. The method of any clause herein, further comprising: receiving sensor data comprising one or more values related to determining the user’ s progress in the rehabilitation plan; and providing the sensor data to a clinical portal that is one of the one or more user portals, such that a healthcare professional is enabled to remotely monitor the user.

[1707] Clause 16.6. The method of any clause herein, further comprising: receiving sensor data comprising one or more values related to determining the user’ s progress in the rehabilitation plan; providing the sensor data as input to the machine learning model such that the machine learning model is configured to output one or more risk scores indicating a likelihood of a change to at least one health indicator that is one of the one or more health indicators of the user; generating a recommendation related to the one or more risk scores; and providing recommendation data for the recommendation to a clinical portal that is one of the one or more user portals

[1708] Clause 17.6. The method of any clause herein, wherein the electromechanical device is a prehabilitation device.

Claims

CLAIMS What is claimed is:

1. A computer-implemented system, comprising: a treatment device configured to be manipulated by a user while the user performs a treatment plan; a patient interface comprising an output device configured to present telemedicine information associated with a telemedicine session; and a processing device configured to: receive a treatment plan for a patient; during the telemedicine session, use the treatment plan to generate at least one parameter; and responsive to at least one trigger condition occurring, control at least one operation of the treatment device.

2. The system of claim 1, wherein the treatment device comprises a sensor for detecting data associated with the at least one operation.

3. The system of claim 2, wherein the processing device is configured to receive the data from the sensor in real-time or near real-time.

4. The system of claim 3, wherein, to determine the at least one trigger condition, the one or more processing devices are configured to use at least one of the data, the at least one parameter, and a patient input.

5. The system of claim 1, wherein the controlling of the at least one operation of the device comprises causing the device to modify at least one of a volume, a pressure, a resistance, an angle, a speed, an angular or rotational velocity, and a time period.

6. The system of claim 1, wherein the at least one parameter is at least one of a force parameter, a resistance parameter, a range of motion parameter, a temperature parameter, a pain level parameter, an exercise session parameter, a vital sign parameter, and a time parameter.

7. A system for enabling a remote adjustment of a device, comprising: a control system comprising one or more processing devices operatively coupled to the device, wherein the one or more processing devices are configured to: receive a treatment plan for a patient; use the treatment plan to generate at least one parameter; and responsive to at least one trigger condition occurring, control at least one operation of the device.

8. The system of claim 7, wherein the device comprises a sensor for detecting data associated with the at least one operation.

9. The system of claim 8, wherein the one or more processing devices are configured to receive the data from the sensor in real-time or near real-time.

10. The system of claim 9, wherein, to determine the at least one trigger condition, the one or more processing devices are configured to use at least one of the data, the at least one parameter, and a patient input.

11. The system of claim 7, wherein the controlling of the at least one operation of the device comprises causing the device to modify at least one of a volume, a pressure, a resistance, an angle, a speed, an angular or rotational velocity, and a time period.

12. The system of claim 7, wherein the at least one parameter is at least one of a force parameter, a resistance parameter, a range of motion parameter, a temperature parameter, a pain level parameter, an exercise session parameter, a vital sign parameter, and a time parameter.

13. The system of claim 7, wherein the one or more processing devices are configured to receive the treatment plan from a clinical portal.

14. The system of claim 7, wherein the one or more processing devices are further configured to: transmit a notification to a clinical portal in real-time or near real-time; receive at least one adjusted parameter in real-time or near real-time; and using the at least one adjusted parameter, control the at least one operation of the device in real-time or near real-time.

15. The system of claim 7, wherein the one or more processing devices are further configured to: transmit a notification to a clinical portal; receive at least one adjusted parameter; and using the at least one adjusted parameter, control the at least one operation of the device at a time subsequent to receiving the at least one adjusted parameter.

16. The system of claim 7, wherein the device comprises at least one of a physical therapy device, a brace, a cap, a mat, and a wrap.

17. A method for enabling a remote adjustment of a device, comprising: receiving a treatment plan for a patient; using the treatment plan to generate at least one parameter; and responsive to at least one trigger condition occurring, controlling at least one operation of the device.

18. The method of claim 17, wherein the device comprises a sensor for detecting data associated with the at least one operation.

19. The method of claim 18, wherein the data is received from the sensor in real-time or near real time.

20. The method of claim 19, further comprising: to determine the at least one trigger condition, using at least one of the data, the at least one parameter, and a patient input.

21. The method of claim 17, wherein the controlling of the at least one operation of the device comprises causing the device to modify at least one of a volume, a pressure, a resistance, an angle, a speed, an angular or rotational velocity, and a time period.

22. The method of claim 17, wherein the at least one parameter is at least one of a force parameter, a resistance parameter, a range of motion parameter, a temperature parameter, a pain level parameter, an exercise session parameter, a vital sign parameter, and a time parameter.

23. The method of claim 17, wherein the treatment plan is received from a clinical portal.

24. The method of claim 17, further comprising: transmitting a notification to a clinical portal in real-time or near real-time; receiving at least one adjusted parameter in real-time or near real-time; and using the adjusted parameter to control the at least one operation of the device in real-time or near real time.

25. The method of claim 17, further comprising: transmitting a notification to a clinical portal; receiving at least one adjusted parameter; and using the at least one adjusted parameter to control the at least one operation of the device at a time subsequent to receiving the at least one adjusted parameter.

26. The method of claim 17, wherein the device comprises at least one of a physical therapy device, a brace, a cap, a mat, and a wrap.

27. A tangible, non-transitory computer-readable storage medium storing instructions that, when executed, cause a processor to: receive a treatment plan for a patient; use the treatment plan to generate at least one parameter; and responsive to at least one trigger condition occurring, control at least one operation of a device.

28. The tangible, non-transitory computer-readable storage medium of claim 27, wherein the treatment plan is received from a clinical portal.

29. The tangible, non-transitory computer-readable storage medium of claim 27, wherein the instructions further cause the processor to: transmit a notification to a clinical portal in real-time or near real-time; receive at least one adjusted parameter in real-time or near real-time; and using the at least one adjusted parameter, control the at least one operation of the device in real-time or near real-time.

30. The tangible, non-transitory computer-readable storage medium of claim 27, wherein the instructions further cause the processor to: transmit a notification to a clinical portal; receive at least one adjusted parameter; and using the at least one adjusted parameter, control the at least one operation of the device at a time subsequent to receiving the at least one adjusted parameter.