WO2022155260A1 - Procédé et système d'implémentation d'environnements de traitement dynamiques selon des informations de patients - Google Patents

Procédé et système d'implémentation d'environnements de traitement dynamiques selon des informations de patients Download PDF

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Publication number
WO2022155260A1
WO2022155260A1 PCT/US2022/012199 US2022012199W WO2022155260A1 WO 2022155260 A1 WO2022155260 A1 WO 2022155260A1 US 2022012199 W US2022012199 W US 2022012199W WO 2022155260 A1 WO2022155260 A1 WO 2022155260A1
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WO
WIPO (PCT)
Prior art keywords
patient
treatment
user
treatment apparatus
interface
Prior art date
Application number
PCT/US2022/012199
Other languages
English (en)
Inventor
Steven Mason
Daniel Posnack
Peter ARN
Wendy Para
S. Adam Hacking
Micheal Mueller
Joseph GUANERI
Jonathan Greene
Original Assignee
Rom Technologies, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US17/147,232 external-priority patent/US20210134432A1/en
Priority claimed from US17/148,047 external-priority patent/US20210128080A1/en
Application filed by Rom Technologies, Inc. filed Critical Rom Technologies, Inc.
Publication of WO2022155260A1 publication Critical patent/WO2022155260A1/fr

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Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/70—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
    • A61H1/00—Apparatus for passive exercising; Vibrating apparatus; Chiropractic devices, e.g. body impacting devices, external devices for briefly extending or aligning unbroken bones
    • A61H1/02—Stretching or bending or torsioning apparatus for exercising
    • A61H1/0214—Stretching or bending or torsioning apparatus for exercising by rotating cycling movement
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
    • A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
    • A61H2201/12—Driving means
    • A61H2201/1207—Driving means with electric or magnetic drive
    • A61H2201/1215—Rotary drive
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
    • A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
    • A61H2201/50—Control means thereof
    • A61H2201/5007—Control means thereof computer controlled
    • A61H2201/501—Control means thereof computer controlled connected to external computer devices or networks
    • A61H2201/5012—Control means thereof computer controlled connected to external computer devices or networks using the internet
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
    • A61H2203/00—Additional characteristics concerning the patient
    • A61H2203/04—Position of the patient
    • A61H2203/0425—Sitting on the buttocks
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
    • A61H2230/00—Measuring physical parameters of the user

Definitions

  • Remote medical assistance also referred to, inter alia, as remote medicine, telemedicine, telemed, telmed, tel-med, or telehealth
  • a healthcare provider or providers such as a physician or a physical therapist
  • audio and/or audiovisual and/or other sensorial or perceptive e.g., without limitation, gesture recognition, gesture control, touchless user interfaces (TUIs), kinetic user interfaces (KUIs), tangible user interfaces, wired gloves, depth-aware cameras, stereo cameras, and gesture-based controllers, tactile, gustatory, haptic, pressure-sensing-based or electromagnetic (e.g., neurostimulation) 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.
  • 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.
  • a system that comprises a memory device storing instructions, and a processing device communicatively coupled to the memory device.
  • the processing device executes the instructions to: receive user data obtained from records associated with a user; generate a modified treatment plan based on the user data; and send, to a treatment apparatus accessible to the user, the modified treatment plan, wherein the modified treatment plan causes the treatment apparatus to update at least one operational aspect of the treatment apparatus, and update at least one operational aspect of at least one other device communicatively coupled to the treatment apparatus.
  • a method includes receiving user data obtained from electronic or physical records associated with a user.
  • the method includes generating a modified treatment plan based on the user data.
  • the method further includes sending, to a treatment apparatus accessible to the user, the modified treatment plan, wherein the modified treatment plan causes the treatment apparatus to: update at least one operational aspect of the treatment apparatus, and update at least one operational aspect of at least one other device communicatively coupled to the treatment apparatus.
  • a tangible, non-transitory computer-readable medium stores instructions that, when executed, cause a processing device to perform any of the methods, operations, or steps described herein.
  • FIG. 1 generally illustrates a block diagram of an embodiment of a computer implemented system for managing a treatment plan according to the present disclosure
  • FIG. 2 generally illustrates a perspective view of an embodiment of a treatment apparatus according to the present disclosure
  • FIG. 3 generally illustrates a perspective view of a pedal of the treatment apparatus of FIG. 2 according to the present disclosure
  • FIG. 4 generally illustrates a perspective view of a person using the treatment apparatus of FIG.
  • FIG. 5 generally illustrates an example embodiment of an overview display of an assistant interface according to the present disclosure
  • FIG. 6 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 present disclosure
  • FIG. 7 generally illustrates a block diagram of a system for implementing dynamic treatment environments based on patient information, according to some embodiments
  • FIGS. 8A-8H generally illustrate conceptual diagrams for implementing a dynamic treatment environment based on a patient’s information, according to some embodiments
  • FIG. 9 generally illustrates an example embodiment of a method for implementing dynamic treatment environments, according to some embodiments.
  • FIG. 10 generally illustrates an example embodiment of another method for implementing dynamic treatment environments, according to some embodiments.
  • FIG. 11 generally illustrates an example computer system according to the present disclosure.
  • FIG. 12 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
  • FIG. 13 generally illustrates an embodiment of the overview display of the assistant interface presenting, in near 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
  • FIG. 14 generally illustrates 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 principles of the present disclosure
  • FIG. 15 generally illustrates an example embodiment of a method for presenting, during a telemedicine session, the recommended treatment plan to a healthcare professional according to the principles of the present disclosure.
  • 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.
  • phrases “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.
  • “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.
  • 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.
  • spatially relative terms such as “inner,” “outer,” “beneath,” “below,” “lower,” “above,” “upper,” “top,” “bottom,” “inside,” “outside,” “contained within,” “superimposing upon,” 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 feature(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.
  • 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.
  • 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 apparatus, 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 terms telemedicine, telehealth, telemed, teletherapeutic, etc. may be used interchangeably herein.
  • the term “optimal treatment plan” may refer to optimizing a treatment plan based on a certain parameter or factors or combinations of more than one parameter or factor, such as, but not limited to, a measure of benefit which one or more exercise regimens provide to users, one or more probabilities of users complying with 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, information pertaining to a microbiome from one or more locations on or in the user (e.g., skin, scalp, digestive tract, vascular system, etc.), or some combination thereof.
  • a measure of benefit which one or more exercise regimens provide to users
  • the term healthcare professional may include a healthcare 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).
  • 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.
  • 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 preferably but not determinatively be less than 10 seconds but greater than 2 seconds.
  • 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, post-surgical recovery, 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 weight 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.
  • 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.
  • a mastectomy may require prehabilitation to strengthen muscles or muscle groups affected directly or indirectly by the mastectomy.
  • 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.
  • 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.
  • phrase, and all permutations of the phrase, “respective measure of benefit with which one or more exercise regimens may provide the user” may refer to one or more measures of benefit with which one or more exercise regimens may provide the user.
  • enhanced reality or “enhanced environment” may include a user experience comprising one or more of an interaction with a computer, 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).
  • 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.
  • 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.
  • 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.
  • 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 ortotal immersion), in the simulated interactive experience.
  • a specific extent possible e.g., partial immersion ortotal immersion
  • 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.
  • 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, one or more leg or foot engaging mechanisms, one or more arm or hand engaging mechanisms, one or more neck or head engaging mechanisms, other suitable hardware components, or a combination thereof.
  • 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.
  • Determining a treatment plan for a patient having certain characteristics may be a technically challenging problem.
  • 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.
  • 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.
  • 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’s location.
  • a healthcare provider or other healthcare 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 healthcare professional may refer to a doctor, physician assistant, nurse, chiropractor, dentist, physical therapist, acupuncturist, physical trainer, 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.
  • the physical therapist or other healthcare professional Since the physical therapist or other healthcare professional is located in a location different from the patient and the treatment apparatus, it may be technically challenging for the physical therapist or other healthcare professional 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the treatment apparatus may be collected before, during, and/or after the treatment plan is performed.
  • 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).
  • desired results e.g., improved muscle strength, range of motion, etc.
  • diminishing returns e.g., continuing to exercise after 3 minutes actually delays or harms recovery.
  • 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.
  • 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.
  • an artificial intelligence engine may include one or more machine learning models that are trained using the cohorts.
  • 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.
  • 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 or any reasonably proximate difference between two different times.
  • the term “results” may referto medical results or medical outcomes. Results and outcomes may referto responses to medical actions.
  • 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.
  • 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.
  • the treatment plans and/or excluded treatment plans may be presented, during a telemedicine or telehealth session, to a healthcare professional.
  • the healthcare 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.
  • the artificial intelligence engine may receive and/or operate distally from the patient and the treatment apparatus.
  • the recommended treatment plans and/or excluded treatment plans may be presented simultaneously with a video of the patient in realtime or near real-time during a telemedicine or telehealth session on a user interface of a computing device of a healthcare 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 realtime 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 suitable 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 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 healthcare professional’s experience using the computing device and may encourage the healthcare professional to reuse the user interface.
  • Such a technique may also reduce computing resources (e.g., processing, memory, network) because the healthcare 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.
  • 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.
  • 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.
  • a healthcare 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.
  • FIG. 1 shows a block diagram of a computer-implemented system 10, 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.
  • the system 10 also includes a server 30 configured to store and to provide data related to managing the treatment plan.
  • the server 30 may include one or more computers and may take the form of a distributed and/or virtualized computer or computers.
  • the server 30 also includes a first communication interface 32 configured to communicate with the clinician interface 20 via a first network 34.1n some embodiments, the first network 34 may include wired and/or wireless network connections such as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC), cellular data network, etc.
  • the server 30 includes a first processor 36 and a first machine-readable storage memory 38, which may be called a “memory” for short, holding first instructions 40 for performing the various actions of the server 30 for execution by the first processor 36.
  • the server 30 is configured to store data regarding the treatment plan.
  • the memory 38 includes a system data store 42 configured to hold system data, such as data pertaining to treatment plans for treating one or more patients.
  • the server 30 is also configured to store data regarding performance by a patient in following a treatment plan.
  • the memory 38 includes a patient data store 44 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.
  • all or a portion of the data described throughout this disclosure can be stored on / provided by a data source 15 with which the server 30 is communicably coupled.
  • the data source 15 can store patient data that can be retrieved and utilized by the server 30.
  • the data source 15 can provide access to data obtained from electronic medical records systems, insurance provider systems, and the like.
  • 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 44.
  • 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.
  • 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.
  • 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 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.
  • 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.
  • the server 30 may execute an artificial intelligence (Al) engine 11 that uses one or more machine learning models 13 to perform at least one of the embodiments disclosed herein.
  • the server 30 may include a training engine 9 capable of generating the one or more machine learning models 13.
  • the machine learning models 13 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 70, among other things.
  • the one or more machine learning models 13 may be generated by the training engine 9 and may be implemented in computer instructions executable by one or more processing devices of the training engine 9 and/or the servers 30. To generate the one or more machine learning models 13, the training engine 9 may train the one or more machine learning models 13.
  • the one or more machine learning models 13 may be used by the artificial intelligence engine 11.
  • the training engine 9 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 (loT) 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.
  • the training engine 9 may use a training data set of a corpus of the characteristics of the people that used the treatment apparatus 70 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 70 throughout each step of the treatment plan, etc.) of the treatment plans performed by the people using the treatment apparatus 70, and the results of the treatment plans performed by the people.
  • the one or more machine learning models 13 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 13 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 13 may also be trained to control, based on the treatment plan, the machine learning apparatus 70.
  • Different machine learning models 13 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.
  • the one or more machine learning models 13 may refer to model artifacts created by the training engine 9.
  • 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 13 that capture these patterns.
  • the artificial intelligence engine 11, the database 33, and/or the training engine 9 may reside on another component (e.g., assistant interface 94, clinician interface 20, etc.) depicted in FIG. 1.
  • the one or more machine learning models 13 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 13 may be a deep network, i.e., a machine learning model comprising multiple levels of non-linear operations.
  • 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).
  • the machine learning model may include numerous layers and/or hidden layers that perform calculations (e.g., dot products) using various neurons.
  • the system 10 also includes a patient interface 50 configured to communicate information to a patient and to receive feedback from the patient.
  • the patient interface includes an input device 52 and an output device 54, which may be collectively called a patient user interface 52, 54.
  • 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 54 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 54 may include other hardware and/or software components such as a projector, virtual reality capability, augmented reality capability, etc.
  • the output device 54 may incorporate various different visual, audio, or other presentation technologies.
  • the output device 54 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.).
  • the patient interface 50 includes a second communication interface 56, which may also be called a remote communication interface configured to communicate with the server 30 and/or the clinician interface 20 via a second network 58.
  • the second network 58 may include a local area network (LAN), such as an Ethernet network.
  • the second network 58 may include the Internet, and communications between the patient interface 50 and the server 30 and/or the clinician interface 20 may be secured via encryption, such as, for example, by using a virtual private network (VPN).
  • the second network 58 may include wired and/or wireless network connections such as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC), cellular data network, etc.
  • the second network 58 may be the same as and/or operationally coupled to the first network 34.
  • the patient interface 50 includes a second processor 60 and a second machine-readable storage memory 62 holding second instructions 64 for execution by the second processor 60 for performing various actions of patient interface 50.
  • the second machine-readable storage memory 62 also includes a local data store 66 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 50 also includes a local communication interface 68 configured to communicate with various devices for use by the patient in the vicinity of the patient interface 50.
  • the local communication interface 68 may include wired and/or wireless communications.
  • the local communication interface 68 may include a local wireless network such as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC), cellular data network, etc.
  • the system 10 also includes a treatment apparatus 70 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.
  • the treatment apparatus 70 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 70 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 70 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.
  • the treatment apparatus 70 includes a controller 72, which may include one or more processors, computer memory, and/or other components.
  • the treatment apparatus 70 also includes a fourth communication interface 74 configured to communicate with the patient interface 50 via the local communication interface 68.
  • the treatment apparatus 70 also includes one or more internal sensors 76 and an actuator 78, such as a motor.
  • the actuator 78 may be used, for example, for moving the patient’s body part and/or for resisting forces by the patient.
  • the internal sensor 76, or an external sensor, may also be referred to as, and interchangeable with, an apparatus sensor.
  • the internal sensors 76 may measure one or more operating characteristics of the treatment apparatus 70 such as, for example, a force a position, a speed, and /or a velocity.
  • the internal sensors 76 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 and/or a position of the internal sensor 76.
  • an internal sensor 76 in the form of a position sensor may measure a distance that the patient is able to move a part of the treatment apparatus 70, where such distance may correspond to a range of motion that the patient’s body part is able to achieve.
  • the internal sensors 76 may include a force sensor configured to measure a force applied by the patient.
  • an internal sensor 76 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 70.
  • the system 10 shown in FIG. 1 also includes an ambulation, or user, sensor 82, which communicates with the server 30 via the local communication interface 68 of the patient interface 50.
  • the ambulation sensor 82 may track and store a number of steps taken by the patient.
  • the ambulation sensor 82 may take the form of a wristband, wristwatch, or smart watch.
  • the ambulation sensor 82 may be integrated within a phone, such as a smartphone.
  • the ambulation, or user, sensor 82 may measure one or more operating characteristics of the user such as, for example, a force, a position, a speed, and /or a velocity.
  • the ambulation sensor 82 may include a plurality of ambulation sensors 82.
  • the ambulation sensor 82, or ambulation sensors may be configured to, or communicate with the local communication interface to, measure at least one of a linear motion or an angular motion of a body part of the patient, and/or a position of the ambulation sensor 82, or ambulation sensors.
  • the ambulation sensors 82 may communicate with the local communication interface cooperating with the server to track a location of at least one ambulation sensor 82, and in turn, a location of the user.
  • the system 10 shown in FIG. 1 also includes a goniometer 84, which communicates with the server 30 via the local communication interface 68 of the patient interface 50.
  • the goniometer 84 measures an angle of the patient’s body part.
  • the goniometer 84 may measure the angle of flex of a patient’s knee or elbow or shoulder.
  • the goniometer 84 may include one or more treatment sensors (not shown in the FIGS.), configured to measure one or more operating characteristics of the user, such as, for example, a force, a position, a speed, and /or a velocity, of the goniometer 84.
  • the treatment sensor may include a plurality of treatment sensors.
  • the treatment sensor may be configured to, or communicate with the local communication interface to, measure at least one of a linear motion, an angular motion of a body part of the patient, and/or a position of the treatment sensor.
  • the treatment sensor may communicate with the local communication interface cooperating with the server to track a location of the plurality of treatment sensors, and in turn a location of the goniometer 84.
  • the system 10 shown in FIG. 1 also includes a pressure sensor 86, which communicates with the server 30 via the local communication interface 68 of the patient interface 50.
  • the pressure sensor 86 measures an amount of pressure or weight applied by a body part of the patient.
  • pressure sensor 86 may measure an amount of force applied by a patient’s foot when pedaling a stationary bike.
  • the system 10 shown in FIG. 1 also includes a supervisory interface 90 which may be similar or identical to the clinician interface 20. In some embodiments, the supervisory interface 90 may have enhanced functionality beyond what is provided on the clinician interface 20.
  • the supervisory interface 90 may be configured for use by a person having responsibility for the treatment plan, such as an orthopedic surgeon.
  • the system 10 shown in FIG. 1 also includes a reporting interface 92 which may be similar or identical to the clinician interface 20.
  • the reporting interface 92 may have less functionality from what is provided on the clinician interface 20.
  • the reporting interface 92 may not have the ability to modify a treatment plan.
  • Such a reporting interface 92 may be used, for example, by a biller to determine the use of the system 10 for billing purposes.
  • the reporting interface 92 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.
  • the system 10 includes an assistant interface 94 for an assistant, such as a doctor, a nurse, a physical therapist, or a technician, to remotely communicate with the patient interface 50 and/or the treatment apparatus 70.
  • an assistant such as a doctor, a nurse, a physical therapist, or a technician
  • Such remote communications may enable the assistant to provide assistance or guidance to a patient using the system 10.
  • the assistant interface 94 is configured to communicate a telemedicine signal 96, 97, 98a, 98b, 99a, 99b with the patient interface 50 via a network connection such as, for example, via the first network 34 and/or the second network 58.
  • the telemedicine signal 96, 97, 98a, 98b, 99a, 99b comprises one of an audio signal 96, an audiovisual signal 97, an interface control signal 98a for controlling a function of the patient interface 50, an interface monitor signal 98b for monitoring a status of the patient interface 50, an apparatus control signal 99a for changing an operating parameter of the treatment apparatus 70, and/or an apparatus monitor signal 99b for monitoring a status of the treatment apparatus 70.
  • each of the control signals 98a, 99a may be unidirectional, conveying commands from the assistant interface 94 to the patient interface 50.
  • an acknowledgement message may be sent from the patient interface 50 to the assistant interface 94.
  • each of the monitor signals 98b, 99b may be unidirectional, status-information commands from the patient interface 50 to the assistant interface 94.
  • an acknowledgement message may be sent from the assistant interface 94 to the patient interface 50 in response to successfully receiving one of the monitor signals 98b, 99b.
  • the patient interface 50 may be configured as a pass-through for the apparatus control signals 99a and the apparatus monitor signals 99b between the treatment apparatus 70 and one or more other devices, such as the assistant interface 94 and/or the server 30.
  • the patient interface 50 may be configured to transmit an apparatus control signal 99a in response to an apparatus control signal 99a within the telemedicine signal 96, 97, 98a, 98b, 99a, 99b from the assistant interface 94.
  • the assistant interface 94 may be presented on a shared physical device as the clinician interface 20.
  • the clinician interface 20 may include one or more screens that implement the assistant interface 94.
  • the clinician interface 20 may include additional hardware components, such as a video camera, a speaker, and/or a microphone, to implement aspects of the assistant interface 94.
  • one or more portions of the telemedicine signal 96, 97, 98a, 98b, 99a, 99b may be generated from a prerecorded source (e.g., an audio recording, a video recording, or an animation) for presentation by the output device 54 of the patient interface 50.
  • a prerecorded source e.g., an audio recording, a video recording, or an animation
  • a tutorial video may be streamed from the server 30 and presented upon the patient interface 50.
  • Content from the prerecorded source may be requested by the patient via the patient interface 50.
  • the assistant via a control on the assistant interface 94, the assistant may cause content from the prerecorded source to be played on the patient interface 50.
  • the assistant interface 94 includes an assistant input device 22 and an assistant display 24, which may be collectively called an assistant user interface 22, 24.
  • the assistant input device 22 may include one or more of a telephone, a keyboard, a mouse, a trackpad, or a touch screen, for example.
  • the assistant input device 22 may include one or more microphones.
  • 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 50.
  • assistant input device 22 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 22 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 22 may include other hardware and/or software components.
  • the assistant input device 22 may include one or more general purpose devices and/or special -purpose devices.
  • the assistant display 24 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 24 may include other hardware and/or software components such as projectors, virtual reality capabilities, or augmented reality capabilities, etc.
  • the assistant display 24 may incorporate various different visual, audio, or other presentation technologies.
  • the assistant display 24 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 24 may comprise one or more different display screens presenting various data and/or interfaces or controls for use by the assistant.
  • the assistant display 24 may include graphics, which may be presented by a web-based interface and/or by a computer program or application (App.).
  • the system 10 may provide computer translation of language from the assistant interface 94 to the patient interface 50 and/or vice-versa.
  • the computer translation of language may include computer translation of spoken language and/or computer translation of text.
  • the system 10 may provide voice recognition and/or spoken pronunciation of text.
  • the system 10 may convert spoken words to printed text and/or the system 10 may audibly speak language from printed text.
  • the system 10 may be configured to recognize spoken words by any or all of the patient, the clinician, and/or the assistant.
  • the system 10 may be configured to recognize and react to spoken requests or commands by the patient.
  • the system 10 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).
  • the server 30 may generate aspects of the assistant display 24 for presentation by the assistant interface 94.
  • the server 30 may include a web server configured to generate the display screens for presentation upon the assistant display 24.
  • the artificial intelligence engine 11 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 24 of the assistant interface 94.
  • the assistant display 24 may be configured to present a virtualized desktop hosted by the server 30.
  • the server 30 may be configured to communicate with the assistant interface 94 via the first network 34.
  • the first network 34 may include a local area network (LAN), such as an Ethernet network.
  • LAN local area network
  • the first network 34 may include the Internet, and communications between the server 30 and the assistant interface 94 may be secured via privacy enhancing technologies, such as, for example, by using encryption over a virtual private network (VPN).
  • the server 30 may be configured to communicate with the assistant interface 94 via one or more networks independent of the first network 34 and/or other communication means, such as a direct wired or wireless communication channel.
  • the patient interface 50 and the treatment apparatus 70 may each operate from a patient location geographically separate from a location of the assistant interface 94.
  • the patient interface 50 and the treatment apparatus 70 may be used as part of an in-home rehabilitation system, which may be aided remotely by using the assistant interface 94 at a centralized location, such as a clinic or a call center.
  • the assistant interface 94 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 94 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.
  • FIGS. 2-3 show an embodiment of a treatment apparatus 70. More specifically, FIG. 2 shows a treatment apparatus 70 in the form of a stationary cycling machine 100, which may be called a stationary bike, for short.
  • the stationary cycling machine 100 includes a set of pedals 102 each attached to a pedal arm 104 for rotation about an axle 106.
  • the pedals 102 are movable on the pedal arms 104 in order to adjust a range of motion used by the patient in pedaling.
  • the pedals being located inwardly toward the axle 106 corresponds to a smaller range of motion than when the pedals are located outwardly away from the axle 106.
  • a pressure sensor 86 is attached to or embedded within one of the pedals 102 for measuring an amount of force applied by the patient on the pedal 102.
  • the pressure sensor 86 may communicate wirelessly to the treatment apparatus 70 and/or to the patient interface 50.
  • FIG. 4 shows a person (a patient) using the treatment apparatus of FIG. 2 and showing sensors and various data parameters connected to a patient interface 50.
  • the example patient interface 50 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.
  • the patient interface 50 may be embedded within or attached to the treatment apparatus 70.
  • FIG. 4 shows the patient wearing the ambulation sensor 82 on his wrist, with a note showing “STEPS TODAY 1355”, indicating that the ambulation sensor 82 has recorded and transmitted that step count to the patient interface 50.
  • FIG. 4 shows the patient wearing the ambulation sensor 82 on his wrist, with a note showing “STEPS TODAY 1355”, indicating that the ambulation sensor 82 has recorded and transmitted that step count to the patient interface 50.
  • FIG. 4 also shows the patient wearing the goniometer 84 on his right knee, with a note showing “KNEE ANGLE 72°”, indicating that the goniometer 84 is measuring and transmitting that knee angle to the patient interface 50.
  • FIG. 4 also shows a right side of one of the pedals 102 with a pressure sensor 86 showing “FORCE 12.5 lbs.,” indicating that the right pedal pressure sensor 86 is measuring and transmitting that force measurement to the patient interface 50.
  • FIG. 4 also shows a left side of one of the pedals 102 with a pressure sensor 86 showing “FORCE 27 lbs.”, indicating that the left pedal pressure sensor 86 is measuring and transmitting that force measurement to the patient interface 50.
  • FIG. 4 is an example embodiment of an overview display 120 of the assistant interface 94. Specifically, the overview display 120 presents several different controls and interfaces for the assistant to remotely assist a patient with using the patient interface 50 and/or the treatment apparatus 70. This remote assistance functionality may also be called telemedicine or telehealth.
  • the overview display 120 includes a patient profile display 130 presenting biographical information regarding a patient using the treatment apparatus 70.
  • the patient profile display 130 may take the form of a portion or region of the overview display 120, as shown in FIG. 5, although the patient profile display 130 may take other forms, such as a separate screen or a popup window.
  • the patient profile display 130 may include a limited subset of the patient’s biographical information. More specifically, the data presented upon the patient profile display 130 may depend upon the assistant’s need for that information.
  • a healthcare 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 70 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 130 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.
  • 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”.
  • HIPAA Health Insurance Portability and Accountability Act
  • GDPR General Data Protection Regulation
  • the patient profile display 130 may present information regarding the treatment plan for the patient to follow in using the treatment apparatus 70.
  • Such treatment plan information may be limited to an assistant who is a healthcare professional, such as a doctor or physical therapist.
  • a healthcare 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 70 may not be provided with any information regarding the patient’s treatment plan.
  • one or more recommended treatment plans and/or excluded treatment plans may be presented in the patient profile display 130 to the assistant.
  • the one or more recommended treatment plans and/or excluded treatment plans may be generated by the artificial intelligence engine 11 of the server 30 and received from the server 30 in real-time during, inter aha, 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. 7.
  • the example overview display 120 shown in FIG. 5 also includes a patient status display 134 presenting status information regarding a patient using the treatment apparatus.
  • the patient status display 134 may take the form of a portion or region of the overview display 120, as shown in FIG.
  • the patient status display 134 may take other forms, such as a separate screen or a popup window.
  • the patient status display 134 includes sensor data 136 from one or more of the external sensors 82, 84, 86, and/or from one or more internal sensors 76 of the treatment apparatus 70.
  • the patient status display 134 may present other data 138 regarding the patient, such as last reported pain level, or progress within a treatment plan.
  • 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 20, 50, 90, 92, 94 of the system 10.
  • user access controls may be employed to control what information is available to any given person using the system 10.
  • data presented on the assistant interface 94 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.
  • the example overview display 120 shown in FIG. 5 also includes a help data display 140 presenting information for the assistant to use in assisting the patient.
  • the help data display 140 may take the form of a portion or region of the overview display 120, as shown in FIG. 5.
  • the help data display 140 may take other forms, such as a separate screen or a popup window.
  • the help data display 140 may include, for example, presenting answers to frequently asked questions regarding use of the patient interface 50 and/or the treatment apparatus 70.
  • the help data display 140 may also include research data or best practices.
  • the help data display 140 may present scripts for answers or explanations in response to patient questions.
  • the help data display 140 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.
  • the assistant interface 94 may present two or more help data displays 140, 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.
  • the second help data display may automatically populate with script information.
  • the example overview display 120 shown in FIG. 5 also includes a patient interface control 150 presenting information regarding the patient interface 50, and/or to modify one or more settings of the patient interface 50.
  • the patient interface control 150 may take the form of a portion or region of the overview display 120, as shown in FIG. 5.
  • the patient interface control 150 may take other forms, such as a separate screen or a popup window.
  • the patient interface control 150 may present information communicated to the assistant interface 94 via one or more of the interface monitor signals 98b.
  • the patient interface control 150 includes a display feed 152 of the display presented by the patient interface 50.
  • the display feed 152 may include a live copy of the display screen currently being presented to the patient by the patient interface 50.
  • the display feed 152 may present an image of what is presented on a display screen of the patient interface 50.
  • the display feed 152 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 150 may include a patient interface setting control 154 for the assistant to adjust or to control one or more settings or aspects of the patient interface 50.
  • the patient interface setting control 154 may cause the assistant interface 94 to generate and/or to transmit an interface control signal 98 for controlling a function or a setting of the patient interface 50.
  • the patient interface setting control 154 may include collaborative browsing or co-browsing capability for the assistant to remotely view and/or control the patient interface 50.
  • the patient interface setting control 154 may enable the assistant to remotely enter text to one or more text entry fields on the patient interface 50 and/or to remotely control a cursor on the patient interface 50 using a mouse or touchscreen of the assistant interface 94.
  • the patient interface setting control 154 may allow the assistant to change a setting that cannot be changed by the patient.
  • the patient interface 50 may be precluded from accessing a language setting to prevent a patient from inadvertently switching, on the patient interface 50, the language used for the displays, whereas the patient interface setting control 154 may enable the assistant to change the language setting of the patient interface 50.
  • the patient interface 50 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 50 such that the display would become illegible to the patient, whereas the patient interface setting control 154 may provide for the assistant to change the font size setting of the patient interface 50.
  • the example overview display 120 shown in FIG. 5 also includes an interface communications display 156 showing the status of communications between the patient interface 50 and one or more other devices 70, 82, 84, such as the treatment apparatus 70, the ambulation sensor 82, and/or the goniometer 84.
  • the interface communications display 156 may take the form of a portion or region of the overview display 120, as shown in FIG. 5.
  • the interface communications display 156 may take other forms, such as a separate screen or a popup window.
  • the interface communications display 156 may include controls for the assistant to remotely modify communications with one or more of the other devices 70, 82, 84.
  • the assistant may remotely command the patient interface 50 to reset communications with one of the other devices 70, 82, 84, or to establish communications with a new one of the other devices 70, 82, 84.
  • This functionality may be used, for example, where the patient has a problem with one of the other devices 70, 82, 84, or where the patient receives a new or a replacement one of the other devices 70, 82, 84.
  • the example overview display 120 shown in FIG. 5 also includes an apparatus control 160 for the assistant to view and/or to control information regarding the treatment apparatus 70.
  • the apparatus control 160 may take the form of a portion or region of the overview display 120, as shown in FIG. 5.
  • the apparatus control 160 may take other forms, such as a separate screen or a popup window.
  • the apparatus control 160 may include an apparatus status display 162 with information regarding the current status of the apparatus.
  • the apparatus status display 162 may present information communicated to the assistant interface 94 via one or more of the apparatus monitor signals 99b.
  • the apparatus status display 162 may indicate whether the treatment apparatus 70 is currently communicating with the patient interface 50.
  • the apparatus status display 162 may present other current and/or historical information regarding the status of the treatment apparatus 70.
  • the apparatus control 160 may include an apparatus setting control 164 for the assistant to adjust or control one or more aspects of the treatment apparatus 70.
  • the apparatus setting control 164 may cause the assistant interface 94 to generate and/or to transmit an apparatus control signal 99 for changing an operating parameter of the treatment apparatus 70, (e.g., a pedal radius setting, a resistance setting, a target RPM, etc.).
  • the apparatus setting control 164 may include a mode button 166 and a position control 168, which may be used in conjunction for the assistant to place an actuator 78 of the treatment apparatus 70 in a manual mode, after which a setting, such as a position or a speed of the actuator 78, can be changed using the position control 168.
  • the mode button 166 may provide for a setting, such as a position, to be toggled between automatic and manual modes.
  • one or more settings may be adjustable at any time, and without having an associated auto/manual mode.
  • the assistant may change an operating parameter of the treatment apparatus 70, such as a pedal radius setting, while the patient is actively using the treatment apparatus 70. Such “on the fly” adjustment may or may not be available to the patient using the patient interface 50.
  • the apparatus setting control 164 may allow the assistant to change a setting that cannot be changed by the patient using the patient interface 50.
  • the example overview display 120 shown in FIG. 5 also includes a patient communications control 170 for controlling an audio or an audiovisual communications session with the patient interface 50.
  • the communications session with the patient interface 50 may comprise a live feed from the assistant interface 94 for presentation by the output device of the patient interface 50.
  • the live feed may take the form of an audio feed and/or a video feed.
  • the patient interface 50 may be configured to provide two-way audio or audiovisual communications with a person using the assistant interface 94.
  • the communications session with the patient interface 50 may include bidirectional (two-way) video or audiovisual feeds, with each of the patient interface 50 and the assistant interface 94 presenting video of the other one.
  • the patient interface 50 may present video from the assistant interface 94, while the assistant interface 94 presents only audio or the assistant interface 94 presents no live audio or visual signal from the patient interface 50.
  • the assistant interface 94 may present video from the patient interface 50, while the patient interface 50 presents only audio or the patient interface 50 presents no live audio or visual signal from the assistant interface 94.
  • the audio or an audiovisual communications session with the patient interface 50 may take place, at least in part, while the patient is performing the rehabilitation regimen upon the body part.
  • the patient communications control 170 may take the form of a portion or region of the overview display 120, as shown in FIG. 5.
  • the patient communications control 170 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 94 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 94.
  • the audio and/or audiovisual communications may include communications with a third party.
  • the system 10 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 healthcare professional or a specialist.
  • the example patient communications control 170 shown in FIG. 5 includes call controls 172 for the assistant to use in managing various aspects of the audio or audiovisual communications with the patient.
  • the call controls 172 include a disconnect button 174 for the assistant to end the audio or audiovisual communications session.
  • the call controls 172 also include a mute button 176 to temporarily silence an audio or audiovisual signal from the assistant interface 94.
  • the call controls 172 may include other features, such as a hold button (not shown).
  • the call controls 172 also include one or more record/playback controls 178, 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 172 also include a video feed display 180 for presenting still and/or video images from the patient interface 50, and a self-video display 182 showing the current image of the assistant using the assistant interface.
  • the self-video display 182 may be presented as a picture-in-picture format, within a section of the video feed display 180, as shown in FIG.
  • the self-video display 182 may be presented separately and/or independently from the video feed display 180.
  • the example overview display 120 shown in FIG. 5 also includes a third-party communications control 190 for use in conducting audio and/or audiovisual communications with a third party.
  • the third- party communications control 190 may take the form of a portion or region of the overview display 120, as shown in FIG. 5.
  • the third-party communications control 190 may take other forms, such as a display on a separate screen or a popup window.
  • the third-party communications control 190 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 healthcare professional or a specialist.
  • the third-party communications control 190 may include conference calling capability for the third party to simultaneously communicate with both the assistant via the assistant interface 94, and with the patient via the patient interface 50.
  • the system 10 may provide for the assistant to initiate a 3 -way conversation with the patient and the third party.
  • FIG. 6 shows an example block diagram of training a machine learning model 13 to output, based on data 600 pertaining to the patient, a treatment plan 602 for the patient according to the present disclosure.
  • Data pertaining to other patients may be received by the server 30.
  • 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.).
  • 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.
  • 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 week for 3 weeks, wherein values for the 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).
  • Cohort A and cohort B may be included in a training dataset used to train the machine learning model 13.
  • the machine learning model 13 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 600 for a new patient is input into the trained machine learning model 13, the trained machine learning model 13 may match the characteristics included in the data 600 with characteristics in either cohort A or cohort B and output the appropriate treatment plan 602. In some embodiments, the machine learning model 13 may be trained to output one or more excluded treatment plans that should not be performed by the new patient.
  • FIG. 7 illustrates a block diagram of a system 700 for implementing dynamic treatment environments based on patient information, according to some embodiments. As shown in FIG.
  • the system 700 may include a data source 15, a server 30, a patient interface 50, a treatment apparatus 70, and local devices 750, 760. Notwithstanding the specific illustrations in FIG. 7, the number and/or organization of the various computing devices illustrated in FIG. 7 is not meant to be limiting. To the contrary, the system 700 may be adapted to omit and/or combine a subset of the devices illustrated in FIG. 7, or to include additional devices not illustrated in FIG. 7.
  • the data source 15 illustrated in FIG. 7 may represent the data source 15 illustrated in FIG. 1 or may represent any other data source(s) from which patient records may be obtained.
  • the data source 15 may be configured to store information for various patients — e.g., the patient data 44 illustrated in FIG. 1 — which is represented in FIG. 7 as patient records 702.
  • the patient records 702 may include, for each patient, occupational characteristics of the patient, health-related characteristics of the patient, demographic characteristics of the patient, psychographic characteristics of the patient, any other characteristics or attributes of the patient and the like.
  • the occupational characteristics for a given patient may include historical information about the patient’s employment experiences, travel experiences, social interactions, and the like.
  • the employment experience information may include the patient’s job roles, deployment history, rankings, and the like.
  • the health-related characteristics of the patent may include historical information about the patient’s health, including a history of the patient’s interactions with healthcare professionals, diagnoses received, prescriptions received, surgical procedures undertaken, past and/or ongoing medical conditions, dietary needs and/or habits, and the like.
  • the patient records 702 for a given patient may indicate that the patient has, e.g., ongoing endocrinological issues, where such issues affect the patient’s overall psychological wellbeing.
  • the demographic characteristics for a given patient may include information pertaining to the age, sex, ethnicity, weight, height, etc., of the patient.
  • the patient records 702 for a given patient may indicate that the patient is a thirty-seven-year-old female of Asian descent.
  • the psychographic characteristics of the patient characteristics for a given patient may include information relating to the attitudes, interests, opinions, beliefs, activities, overt behaviors, motivating behaviors, etc., of the patient.
  • the patient records 702 for a given patient may indicate that the patient has suffered from social anxiety disorder for the past five years.
  • patient records occupational, health-related, demographic, psychographic, etc.
  • any type of patient record — such as those previously herein — may be stored by the data source 15 consistent with the scope of this disclosure.
  • the server 30 illustrated in FIG. 7 may represent the server 30 illustrated in FIG. 1 or may represent another server device configured to implement the different techniques set forth herein.
  • the server 30 may generate modified treatment plans 720 by using the various machine-learning functionalities described herein.
  • the server may utilize the Al engine 11, the ML models 13, the training engine 9, etc. — which are collectively represented in FIG. 7 as an assessment utility 712 — to generate the modified treatment plans 720.
  • the assessment utility 712 may be configured to receive data pertaining to patients who have performed modified treatment plans 720 using different treatment apparatuses — e.g., the patient interface 50, the treatment apparatus 70, the local devices 750/760, and the like.
  • the data may include characteristics of the patients (e.g., patient records 702), the details of the modified treatment plans 720 performed by the patients, the results of performing the modified treatment plans 720, and the like.
  • the results may include, for example, the feedback 780/782 received from the patient interface 50 and the treatment apparatus 70, feedback received from other devices (e.g., one or more of the clinician interface 20, the supervisory interface 90, the reporting interface 92, and the assistant interface 94), and the like.
  • the foregoing feedback sources are not meant to be limiting; further, the assessment utility 712 may receive feedback from any conceivable source / individual consistent with the scope of this disclosure.
  • the feedback may include changes to the modified treatment plans 720 requested by the patients (e.g., in relation to performing the customized treatment plans 720), survey answers provided by the patients regarding their overall experience related to the customized treatment plans 720, information related to the patient’s psychological and/or physical state during the treatment session (e.g., collected by sensors, by the patient, by a healthcare professional, etc.), and the like.
  • changes to the modified treatment plans 720 requested by the patients e.g., in relation to performing the customized treatment plans 720
  • survey answers provided by the patients regarding their overall experience related to the customized treatment plans 720 e.g., information related to the patient’s psychological and/or physical state during the treatment session (e.g., collected by sensors, by the patient, by a healthcare professional, etc.), and the like.
  • information related to the patient’s psychological and/or physical state during the treatment session e.g., collected by sensors, by the patient, by a healthcare professional, etc.
  • the assessment utility 712 may utilize the machine-learning techniques described herein to generate a modified treatment plan 720 for a given patient.
  • a modified treatment plan 720 may include lighting parameters 722, sound parameters 724, notification parameters 726, augmented reality parameters 728, and other parameters 729.
  • the foregoing parameters are exemplary and not meant to be limiting.
  • the modified treatment plan 720 may include any information necessary to facilitate a treatment session as described herein, e.g., connectivity information, pre-recorded content, interactive content, overarching treatment plan information (associated with the modified treatment plan 720), and so on.
  • the patient interface 50 and the treatment apparatus 70 may be configured to implement treatment utilities 730 and 740, respectively, to enable the patient interface 50 and the treatment apparatus 70 to self-configure.
  • one or more of the local devices 750/760 to which the patient interface 50 and the treatment apparatus 70 are communicatively coupled may implement respective treatment utilities that enable the local devices 750/760 to self-configure. This may not be required, however, in scenarios based on the modified treatment plan 720 in which one or more of the patient interface 50 and the treatment apparatus 70 possess the ability to adjust the configurations of one or more of the local devices 750/760.
  • the lighting parameters 722 may specify the manner in which one or more light sources should be configured in order to enhance the patient’s overall experience. More specifically, the lighting parameters 722 may enable one or more devices on which the modified treatment plan 720 is being implemented — e.g., the patient interface 50, the treatment apparatus 70, the local devices 750/760, etc. (hereinafter, “the recipient devices”) — to identify light sources, if any, that are relevant to (i.e., nearby) the user and are at least partially configurable according to the lighting parameters 722.
  • the configurational aspects may include, for example, the overall brightness of a light source, the color tone of a light source, and the like.
  • a patient may have installed one or more smart lights for light sources, e.g., Phillips Hue smart lights, Lutron Caseta smart lights, etc., in a room in which the patient typically conducts the treatment sessions, where the brightness, the color tone, etc. of the smart lights may be dynamically modified by commands.
  • a patient may have installed one or more traditional lights (e.g., incandescent, light emitting diode (LED), etc.) linked to a controller that can affect the brightness, color tone, etc. output by the one or more traditional lights.
  • the recipient device can be configured to adjust identified light sources in accordance with the lighting parameters 722.
  • the sound parameters 724 may specify the manner in which one or more sound sources may be configured to enhance the patient’s overall experience. More specifically, the sound parameters 724 may enable one or more of the recipient devices to identify speakers (and/or amplifiers to which one or more speakers are connected), if any, wherein both are nearby the user and at least partially configurable according to the sound parameters 724.
  • the configurational aspects may include, for example, an audio file and/or stream to play back, a volume at which to play back the audio file and/or stream, sound settings (e.g., bass, treble, balance, etc.), and the like.
  • one of the recipient devices may be linked to one or more wired or wireless speakers, headphones, etc. located in a room in which the patient typically conducts the treatment sessions.
  • the notification parameters 726 may specify the manner in which one or more nearby computing devices may be configured to enhance the patient’s overall experience. More specifically, the notification parameters 726 may enable one or more of the recipient devices to adjust their own (or other devices’) notification settings. In one example, this may include updating configurations to suppress at least one of audible, visual, haptic, or physical alerts, to minimize distractions to the patient during the treatment session. This may also include updating a configuration to cause one or more of the recipient devices to transmit all electronic communications directly to an alternative target comprising one of voicemail, text, email, or other alternative electronic receiver.
  • the augmented reality parameters 728 may specify the manner in which one or more of the recipient devices are configured to provide an augmented reality experience to the patient. This may include, for example, updating a virtual background displayed on a display device communicatively coupled to one or more of the recipient devices.
  • the techniques set forth herein are not limited to augmented reality but may also apply to virtual (or other) reality implementations.
  • the augmented reality parameters 728 may include information enabling a patient to participate in a treatment session using a virtual reality headset configured in accordance with one or more of the lighting parameters 722, sound parameters 724, notification parameters 726, augmented reality parameters 728, or other parameters 729. Further, any suitable immersive reality shall be deemed to be within the scope of the disclosure.
  • the other parameters 729 may represent any other conceivable parameters that may be used to adjust the patient’s environment.
  • the other parameters 729 may include, for example, configuration parameters for exercise equipment, which are described below in greater detail in relation to FIGS. 8G- 8H.
  • one or more of the recipient devices may take snapshots of their own (or other devices’) existing configurations prior to adjusting said devices. In this manner, the one or more recipient devices may restore the configurations at the conclusion of the treatment session, thereby improving the patient’s overall experience.
  • FIGS. 8A-8H illustrate conceptual diagrams for implementing a dynamic treatment environment based on a patient’s information, according to some embodiments.
  • FIG. 8 A illustrates an example scenario in which the patient interface 50 receives a modified treatment plan 720 — which, as described above, may be provided by the server 30 using the manual and/or automated (e.g., machine-learning) techniques described herein.
  • the patient interface 50 in response to receiving the modified treatment plan 720, may output a treatment utility interface 802 (e.g., on a display communicably coupled to the patient interface 50).
  • a treatment utility interface 802 e.g., on a display communicably coupled to the patient interface 50.
  • the patient interface 50 may seek to discover nearby devices in response to identifying that the modified treatment plan 720 includes parameters (e.g., lighting, sound, notification, etc.) intended to modify the configuration settings of nearby devices. As shown in FIG. 8 A, a patient operating the patient interface 50 may authorize the discovery of nearby devices.
  • parameters e.g., lighting, sound, notification, etc.
  • the patient interface 50 may limit the discovery process. For example, when only lighting parameters 722 (and not the other parameters described herein) are included in the modified treatment plan 720, the patient interface 50 may search for light sources only. The patient interface 50 may also limit its discovery only to devices nearby the patient’s known or likely location. For example, the patient interface 50 may reliably assume that devices coupled to the patient interface 50 via low-energy communications (e.g., Bluetooth, Near Field Communication, etc.) are nearby. In another example, the patient interface 50 may identify devices nearby based on names, tags, etc. assigned to the devices.
  • low-energy communications e.g., Bluetooth, Near Field Communication, etc.
  • the patient interface 50 may prompt the patient to indicate the name of the room in which the patient is currently sitting (e.g., “Home Office”), and, in turn, discover nearby devices based on the name of the room.
  • machine-learning techniques may be implemented to reliably predict the room in which the patient is located when the treatment session is about to begin. For example, the patient interface 50 may identify that, during virtually every prior treatment session, the patient was located in the “Home Office.” In this manner, the patient interface 50 may automatically limit its search for devices in that room prior to starting each treatment session. Additionally, the patient interface 50 may be configured to forego the discovery process after identifying that the same devices are consistently utilized over a threshold number of treatment sessions. [0133] FIG.
  • FIG. 8B illustrates an example outcome of the patient interface 50 presents nearby devices (as established in FIG. 8A) associated with the patient interface 50.
  • the patient interface 50 indicates, by way of the treatment utility interface 802, that the patient interface 50 has discovered office lights 830 (four different light sources under the name “Office Lights”), an office speaker 832 (under the name “Office Speaker”), and a tablet 834 (under the name “Tablet”).
  • office lights 830 four different light sources under the name “Office Lights”
  • Office Speaker under the name “Office Speaker”
  • Tablet 834 under the name “Tablet”.
  • the treatment utility interface 802 may enable the patient to add other devices not discovered by the patient interface 50 when performing the search.
  • adding other devices may involve enabling the patient to select from a list of devices filtered out during discovery (e.g., per the techniques described in the foregoing paragraph).
  • Adding other devices may also involve enabling the patient to enter information necessary to discover and/or connect to other devices, such as device names, device addresses, device authentication information, and the like.
  • the treatment utility interface 802 may enable the patient to modify the devices discovered by the patient interface 50.
  • the patient may select the respective “Modify” button located next to a given group of discovered devices to add, modify, or remove devices from the group.
  • the treatment utility interface 802 may also enable the patient to instruct the patient interface 50 to forget one or more groups of devices, both in a temporary capacity (e.g., for the current session only) or in a more permanent capacity (e.g., until the patient removes the group from a list of forgotten devices). Such modifications may be communicated back to the server 30 in the form of feedback that may be used to improve the overall accuracy of the machine-learning techniques described herein.
  • the patient may verify the accuracy of the list of nearby devices presented in the treatment utility interface 802.
  • the treatment utility interface 802 may indicate to the patient the recommended settings for the various devices when implementing the modified treatment plan 720, which is illustrated in FIG. 8C and described below in greater detail.
  • the various parameters included in the modified treatment plan 720 may be applied to the devices discovered (as established in FIGS. 8A-8B).
  • the lighting parameters 722 of the modified treatment plan 720 may involve setting the office lights 830 to a 50% brightness level and a color tone of 2700K.
  • the sound parameters 724 of the modified treatment plan 720 may involve setting the office speaker 832 to play, e.g., a Mozart composition, at a volume level of 50 dB.
  • one or more audio files may be included in the sound parameters 724 to enable the office speaker 832 to play back audio designed to accompany the modified treatment plan 720.
  • instructions for obtaining audio data may be included in the sound parameters 724, e.g., a web address, credentials, etc. to stream audio designed to accompany the modified treatment plan 720.
  • the notification parameters 726 of the modified treatment plan 720 may involve suppressing all alerts on the patient interface 50 and the tablet 834 such that, during the treatment session, the patient is not disturbed or distracted.
  • the augmented reality parameters 728 of the modified treatment plan 720 may involve applying a fixed/live ocean background to a video session that comprises the treatment session (e.g., wherein a clinician is superimposed over the live ocean background). This background may be visible, for example, on a display device communicatively coupled to the patient interface 50 (or other device with which the patient interface 50 is in communication).
  • the other parameters 729 of the modified treatment plan 720 may be used to apply any other additional settings to other recipient devices.
  • the treatment utility interface 802 may enable the patient to disable or modify the suggested settings listed for the various devices.
  • the patient opts to modify the suggested settings listed for the office speaker 832, which is described below in greater detail in relation to FIG. 8D.
  • Such modifications may be applied in a temporary capacity (e.g., for the current session only) or in a more permanent capacity (e.g., until the patient indicates it is acceptable to utilize the respective device as suggested by the modified treatment plan 720).
  • modifications may be communicated back to the server 30 in the form of feedback that may be used to improve the overall accuracy of the machine-learning techniques described herein.
  • the treatment utility interface 802 may enable the patient to adjust the type and volume of the audio track that will be played back by the office speaker 832.
  • the patient may select alternative music (e.g., a Beethoven composition or, alternatively, e.g., a jazz, a pop, or a Reggae composition) if the patient does not like Mozart’s music.
  • the patient may also select a different volume at which to output the music, e.g., a lower or higher volume than the volume recommended by the modified treatment plan 720.
  • the patient is not limited, however, to modifying the parameters illustrated in FIG. 8D.
  • the treatment utility interface 802 may enable the patient to select other desired music from other desired sources (e.g., a local music library, streaming music services, etc.), to select from different playlists, and so on, consistent with the scope of this disclosure.
  • FIG. 8D illustrates the patient modifies the sound parameters 724 by selecting a soundtrack of Beethoven compositions (instead of Mozart compositions) and selecting a volume of 45 dB (instead of 50 dB).
  • FIG. 8E illustrates the treatment utility interface 802 after the patient has requested the changes (as established in FIG. 8D). At this juncture, the patient confirms that the recommended parameters are acceptable by selecting “YES”. In turn, and as illustrated in FIG. 8F, the treatment utility interface 802 causes the different devices to reflect the settings illustrated in FIG. 8E.
  • the office lights 830 are configured to output light at a 50% brightness level and a 2700K color tone.
  • the office speaker 832 (illustrated as the office speaker 832’ due to its adjusted settings) begins playing a Beethoven composition at 45 dB.
  • the tablet 834 (illustrated as the tablet 834’ due to its adjusted settings) has entered into a silent mode.
  • the patient interface 50 (illustrated as the patient interface 50’ due to its adjusted settings) has entered into a silent mode and is displaying a soothing live ocean background as an augmented reality. At this juncture, the training session may begin.
  • FIG. 8G illustrates an example scenario involving the incorporation of an exercise session into a treatment session (e.g., as a continuation of the treatment session established in FIGS. 8A- 8F, as a new / different treatment session, etc.).
  • the exercise session may involve the patient interface 50 discovering nearby exercise devices. To identify the types of exercise devices compatible with the exercise session, this may involve, for example, referencing other parameters 729 included in the modified treatment plan 720.
  • the patient interface 50 discovers a cycling trainer 840 named “Jim’s Cycling Trainer” (based on, for example, the other parameters 729 of the modified treatment plan 720 indicating that cycling trainers are acceptable).
  • the cycling trainer 840 may represent the treatment apparatus 70 described in FIGS. 1-4 or may represent a different cycling trainer. As shown in FIG. 8G, the cycling trainer 840 may include one or more adjustable pedals 842 modifiable to establish a range of motion 844. The cycling trainer 840 may also include a resistor 846 modifiable to establish a resistance 848 against the rotational motion of the one or more pedals 842.
  • the patient may confirm that the discovery of the cycling trainer 840 is accurate. Alternatively, the patient may attempt to add other exercise trainers by utilizing the same approaches described in FIG. 8A for discovering other devices.
  • the treatment utility interface 802 may display recommended settings (e.g., defined by the other parameters 729 of the modified treatment plan 720) for different components included on the cycling trainer 840. Again, the treatment utility interface 802 also permits the patient to modify / disable different settings (e.g., in a manner similar to that described in FIGS. 8C-8D).
  • the patient interface 50 may cause the recommended settings to be applied to the cycling trainer 840. This may include, for example, changing the range of motion of the pedals 842 to four inches to establish a range of motion 844’. This may also include changing the resistor 846 to 35% to establish a resistance 848’ against the pedals 842. This may further involve setting the workout duration to 7.5 minutes (e.g., using an internal clock on the cycling trainer 840 that causes the cycling trainer 840 to adjust its operation after 7.5 minutes have lapsed).
  • cycling trainer 840 The components and configurable aspects of the cycling trainer 840 are exemplary; further, any cycling trainer may be utilized consistent with the scope of this disclosure. It is also noted that the embodiments set forth herein are not limited to cycling trainers and that all forms of exercise equipment, having varying adjustments and capabilities at any level of granularity, may be utilized consistent with the scope of this disclosure.
  • the modified treatment plan 720 may include information that enables one or more of the settings to change in response to conditions being satisfied.
  • Such conditions may include, for example, an amount of time lapsing (e.g., five minutes after the treatment session starts), a milestone being hit (e.g., clinician / patient indicating a meditation period is been completed), an achievement being made (e.g., a low resting heart rate being hit), and the like.
  • FIG. 9 shows an example embodiment of a method 900 for implementing dynamic treatment environments, according to some embodiments.
  • Method 900 includes operations performed by processors of a computing device (e.g., any component of FIG. 1, such as the server 30).
  • processors of a computing device e.g., any component of FIG. 1, such as the server 30.
  • one or more operations of the method 900 are implemented in computer instructions stored on a memory device and executed by a processing device.
  • the operations of the method 900 may be performed in some combination with any of the operations of any of the methods described herein.
  • the processing device e.g., the server 30 — receives user data obtained from electronic or physical records associated with a user.
  • the server 30 generates a modified treatment plan based on the user data obtained from electronic or physical records associated with the user.
  • the server 30 provides the modified treatment plan to a treatment apparatus accessible to the user.
  • the modified treatment plan causes the treatment apparatus to, based on the modified treatment plan: (1) update at least one operational aspect of the treatment apparatus, and (2) update at least one operational aspect of at least one other device communicatively coupled to the treatment apparatus.
  • FIG. 10 shows an example embodiment of another method 1000 for implementing dynamic treatment environments, according to some embodiments.
  • Method 1000 includes operations performed by processors of a computing device (e.g., any component of FIG. 1, such as the patient interface 50, the treatment apparatus 70, and the like).
  • processors of a computing device e.g., any component of FIG. 1, such as the patient interface 50, the treatment apparatus 70, and the like.
  • one or more operations of the method 1000 are implemented in computer instructions stored on a memory device and executed by a processing device .
  • the operations of the method 1000 may be performed in some combination with any of the operations of any of the methods described herein.
  • the processing device receives, from a server device (e.g., the server 30), a treatment plan modified based on user data obtained from electronic or physical records associated with a user.
  • the processing device updates at least one operational aspect of the treatment apparatus based on the modified treatment plan.
  • the processing device updates at least one operational aspect of at least one other device communicably coupled to the treatment apparatus.
  • FIG. 11 shows an example computer system 1100 which can perform any one or more of the methods described herein, in accordance with one or more aspects of the present disclosure.
  • computer system 1100 may include a computing device and correspond to the assistance interface 94, the reporting interface 92, the supervisory interface 90, the clinician interface 20, the server 30 (including the Al engine 11), the patient interface 50, the ambulatory sensor 82, the goniometer 84, the treatment apparatus 70, the pressure sensor 86, or any suitable component of FIG. 1.
  • the computer system 1100 may be capable of executing instructions implementing the one or more machine learning models 13 of the artificial intelligence engine 11 ofFIG. l.
  • 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 (loT) device, or any device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that device.
  • PC personal computer
  • PDA personal Digital Assistant
  • a mobile phone a camera, a video camera, an Internet of Things (loT) device, or any device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that device.
  • the computer system 1100 includes a processing device 1102, a main memory 1104 (e.g., readonly memory (ROM), flash memory, solid state drives (SSDs), dynamic random-access memory (DRAM) such as synchronous DRAM (SDRAM)), a static memory 1106 (e.g., flash memory, solid state drives (SSDs), static random-access memory (SRAM)), and a data storage device 1108, which communicate with each other via a bus 1110.
  • main memory 1104 e.g., readonly memory (ROM), flash memory, solid state drives (SSDs), dynamic random-access memory (DRAM) such as synchronous DRAM (SDRAM)
  • DRAM dynamic random-access memory
  • SDRAM synchronous DRAM
  • static memory 1106 e.g., flash memory, solid state drives (SSDs), static random-access memory (SRAM)
  • SRAM static random-access memory
  • Processing device 1102 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device 1102 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.
  • ASIC application specific integrated circuit
  • FPGA field programmable gate array
  • DSP digital signal processor
  • network processor or the like.
  • the processing device 1402 is configured to execute instructions for performing any of the operations and steps discussed herein.
  • the computer system 1100 may further include a network interface device 1112.
  • the computer system 1100 also may include a video display 1114 (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 1116 (e.g., a keyboard and/or a mouse or a gaming-like control), and one or more speakers 1118 (e.g., a speaker).
  • the video display 1114 and the input device(s) 1116 may be combined into a single component or device (e.g., an LCD touch screen).
  • the data storage device 1116 may include a computer-readable medium 1120 on which the instructions 1122 embodying any one or more of the methods, operations, or functions described herein is stored.
  • the instructions 1122 may also reside, completely or at least partially, within the main memory 1104 and/or within the processing device 1102 during execution thereof by the computer system 1100. As such, the main memory 1104 and the processing device 1102 also constitute computer-readable media.
  • the instructions 1122 may further be transmitted or received over a network via the network interface device 1112.
  • computer-readable storage medium 1120 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.
  • a method for implementing dynamic treatment environments comprising, at a server device: receiving user data obtained from records associated with a user; generating, based on the user data, a modified treatment; and sending, to a treatment apparatus accessible to the user, the modified treatment plan, wherein the modified treatment plan causes the treatment apparatus to: update at least one operational aspect of the treatment apparatus, and update at least one operational aspect of at least one other device communicatively coupled to the treatment apparatus.
  • updating the at least one operational aspect of the treatment apparatus comprises: updating a virtual background displayed on a display device communicatively coupled to the treatment apparatus, and updating notification settings on the treatment apparatus.
  • updating the notification settings comprises: causing the treatment apparatus to suppress at least one of audible, visual, haptic, or physical alerts, and causing the treatment apparatus to send all electronic communications directly to an alternative target comprising one of voicemail, text, email, or other alternative electronic receiver.
  • Clause 5 The method of any clause herein, wherein: the at least one other device comprises at least one light source, and updating the at least one operational aspect of the at least one light source comprises modifying one or more of a brightness or a color tone exhibited by at least one light source.
  • Clause 6 The method of any clause herein, wherein: the at least one other device comprises at least one audio component, and updating the at least one operational aspect of the at least one audio component comprises modifying one or more of an output volume or an audio stream played back by the at least one audio component.
  • Clause 7 The method of any clause herein, wherein the at least one audio component comprises at least one speaker or at least one amplifier communicably coupled to at least one speaker.
  • Clause 8 The method of any clause, wherein: the at least one other device comprises at least one other computing device, and updating the at least one operational aspect of the at least one other computing device comprises updating notification settings at the at least one other computing device.
  • the at least one other device comprises a training device that includes at least one pedal or handle and at least one component that exerts resistance against a rotational motion of the at least one pedal or handle; and updating the at least one operational aspect of the training device comprises: adjusting a range of motion of the at least one pedal, and by way of the at least one component, adjusting an amount of resistance against the rotational motion of the at least one pedal or handle.
  • Clause 10 The method of any clause herein, wherein, prior to updating the at least one operational aspect of the at least one other device, the treatment apparatus discovers the at least one other device on an authorized network to which the treatment apparatus and the at least one other device are communicably coupled.
  • a tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to: receive user data obtained from records associated with a user; generate a modified treatment plan based on the user data; and sending, to a treatment apparatus accessible to the user, the modified treatment plan, wherein the modified treatment plan causes the treatment apparatus to: update at least one operational aspect of the treatment apparatus, and update at least one operational aspect of at least one other device communicatively coupled to the treatment apparatus.
  • updating the at least one operational aspect of the treatment apparatus comprises: updating a virtual background displayed on a display device communicatively coupled to the treatment apparatus, and updating notification settings on the treatment apparatus.
  • updating the notification settings comprises: causing the treatment apparatus to suppress at least one of audible, visual, haptic, or physical alerts, and causing the treatment apparatus to send all electronic communications directly to an alternative target comprising one of voicemail, text, email, or other alternative electronic receiver.
  • Clause 15 The tangible, non-transitory computer-readable medium of any clause herein, wherein: the at least one other device comprises at least one light source, and updating the at least one operational aspect of the at least one light source comprises modifying one or more of a brightness or a color tone exhibited by at least one light source.
  • Clause 16 The tangible, non-transitory computer-readable medium of any clause herein, wherein: the at least one other device comprises at least one audio component, and updating the at least one operational aspect of the at least one audio component comprises modifying one or more of an output volume or an audio stream played back by the at least one audio component.
  • the at least one other device comprises a training device that includes at least one pedal or handle and at least one component that exerts resistance against a rotational motion of the at least one pedal or handle; and updating the at least one operational aspect of the training device comprises: adjusting a range of motion of the at least one pedal, and by way of the at least one component, adjusting an amount of resistance against the rotational motion of the at least one pedal or handle.
  • a system comprising: a memory device storing instructions; and a processing device communicatively coupled to the memory device, wherein the processing device executes the instructions to: receive user data obtained from records associated with a user; generate a modified treatment plan based on the user data; and sending, to a treatment apparatus accessible to the user, the modified treatment plan, wherein the modified treatment plan causes the treatment apparatus to: update at least one operational aspect of the treatment apparatus, and update at least one operational aspect of at least one other device communicatively coupled to the treatment apparatus.
  • Clause 19 The system of any clause herein, wherein the records contain one or more of: occupational characteristics of the user; health-related characteristics of the user; demographic characteristics of the user; or psychographic characteristics of the user.
  • updating the at least one operational aspect of the treatment apparatus comprises: updating a virtual background displayed on a display device communicatively coupled to the treatment apparatus, and updating notification settings on the treatment apparatus.
  • Clause 22 The system of any clause herein, wherein the at least one other device comprises at least one light source, and the at least one light source having at least one operational aspect.
  • Clause 23 The system of any clause herein, further comprising updating the at least one operational aspect of the at least one light source.
  • Clause 24 The system of any clause herein, further comprising modifying one or more of a brightness or a color tone exhibited by the at least one light source.
  • Clause 25 The system of any clause herein, wherein the at least one other device comprises at least one audio component having at least one operation aspect.
  • Clause 26 The system of any clause herein, further comprising updating the at least one operational aspect of the at least one audio component.
  • Clause 27 The system of any clause herein, further comprising modifying one or more of an output volume or an audio stream played back by the at least one audio component.
  • Clause 28 The system of any clause herein, wherein the at least one other device comprises a training device that includes at least one rotatable pedal or handle and at least one component that exerts resistance against a rotational motion of the at least one pedal or handle.
  • Clause 29 The system of any clause herein, wherein the training device has at least one operational aspect.
  • Clause 30 The system of any clause herein, wherein the at least one operational aspect of the training device comprises: adjusting a range of motion of the at least one rotational pedal, and adjusting an amount of the exerted resistance against the rotational motion of the at least one pedal or handle.
  • FIG. 12 shows an embodiment of an overview display 120 of the assistant interface 94 presenting recommended treatment plans and excluded treatment plans in real-time during a telemedicine session according to the present disclosure.
  • the overview display 120 just includes sections for the patient profile 130 and the video feed display 1130, including the self-video display 182. Any suitable configuration of controls and interfaces of the overview display 120 described with reference to FIG. 5 may be presented in addition to or instead of the patient profile 130, the video feed display 1130, and the self-video display 182.
  • the assistant e.g., healthcare professional
  • using the assistant interface 94 e.g., computing device
  • the video feed display 1130 may also include a graphical user interface (GUI) object 1200 (e.g., a button) that enables the healthcare professional to share on the patient interface 50, in real-time or near real-time during the telemedicine session, the recommended treatment plans and/or the excluded treatment plan with the patient.
  • the healthcare professional may select the GUI object 1200 to share the recommended treatment plans and/or the excluded treatment plans.
  • another portion of the overview display 120 includes the patient profde display 130.
  • the patient profde display 130 is presenting two example recommended treatment plans 600 and one example excluded treatment plan 602.
  • the treatment plans may be recommended in view of characteristics of the patient being treated.
  • 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 120 to perform a treatment plan may be matched by one or more machine learning models 13 of the artificial intelligence engine 11.
  • Each of the recommended treatment plans may be generated based on different desired results.
  • the patient profile display 130 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 130 presents recommended treatment plans from cohort A, and each treatment plan provides different results.
  • 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%.
  • 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.
  • a recommended medication e.g., medication Z
  • 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.
  • the patient profile display 130 may also present the excluded treatment plans 602. These types of treatment plans are shown to the assistant using the assistant interface 94 to alert the assistant not to recommend certain portions of a treatment plan to the patient.
  • 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).
  • 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.
  • the assistant may select the treatment plan for the patient on the overview display 120.
  • the assistant may use an input peripheral (e.g., mouse, touchscreen, microphone, keyboard, etc.) to select from the treatment plans 600 for the patient.
  • the assistant may discuss the pros and cons of the recommended treatment plans 600 with the patient.
  • 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 50 for presentation.
  • the patient may view the selected treatment plan on the patient interface 50.
  • the assistant and the patient may discuss during the telemedicine session the details (e.g., treatment protocol using treatment apparatus 120, diet regimen, medication regimen, etc.) in real-time or in near real-time.
  • the server 30 may control, based on the selected treatment plan and during the telemedicine session, the treatment apparatus 120 as the user uses the treatment apparatus 120.
  • FIG. 13 shows an embodiment of the overview display 120 of the assistant interface 94 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.
  • the treatment apparatus 120 and/or any computing device may transmit data while the patient uses the treatment apparatus 120 to perform a treatment plan.
  • the data may include updated characteristics of the patient.
  • 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 120, a range of motion achieved by the patient, a force exerted on a portion of the treatment apparatus 120, a heartrate of the patient, a blood pressure of the patient, a respiratory rate of the patient, and so forth.
  • the data received at the server 30 may be input into the trained machine learning model 13, 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 13 to adjust a parameter of the treatment apparatus 120. The adjustment may be based on a next step of the treatment plan to further improve the performance of the patient.
  • the data received at the server 30 may be input into the trained machine learning model 13, 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 oris 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 13 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 13 may reassign the patient to another cohort that includes qualifying characteristics the patient’s characteristics. As such, the trained machine learning model 13 may select a new treatment plan from the new cohort and control, based on the new treatment plan, the treatment apparatus 120.
  • the server 30 may provide the new treatment plan 1300 to the assistant interface 94 for presentation in the patient profde 130.
  • the patient profde 130 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.”
  • the patient profde 130 presents the new treatment plan 1300 (“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 healthcare professional
  • the server 30 may receive the selection.
  • the server 30 may control the treatment apparatus 120 based on the new treatment plan 1300.
  • the new treatment plan 1300 may be transmitted to the patient interface 50 such that the patient may view the details of the new treatment plan 1300.
  • FIG. 14 shows an example embodiment of a method 1400 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 1400 is performed by processing logic that may include hardware (circuitry, dedicated logic, etc.), software (such as is run on a general-purpose computer system or a dedicated machine), or a combination of both.
  • the method 1400 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 1, such as server 30 executing the artificial intelligence engine 11).
  • the method 1400 may be performed by a single processing thread.
  • the method 800 may be performed by two or more processing threads, each thread implementing one or more individual functions, routines, subroutines, or operations of the methods.
  • the method 1400 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 1400 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 1400 in accordance with the disclosed subject matter. In addition, those skilled in the art will understand and appreciate that the method 1400 could alternatively be represented as a series of interrelated states via a state diagram or events. [0209] At 1402, the processing device may receive first data pertaining to a first user that uses a treatment apparatus 70 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the measurement information may include a vital sign, a respiration rate, a heartrate, a temperature, or some combination thereof.
  • 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.
  • 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.
  • 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.
  • 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.
  • the treatment plan may include a treatment protocol that specifies using the treatment apparatus 70 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 70 for each of the exercises.
  • a two-week treatment protocol for a person having certain characteristics e.g., respiration, weight, age, injury, current range of motion, heartrate, etc.
  • 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.
  • the processing device may control, based on the treatment plan, the treatment apparatus 70 while the second user uses the treatment apparatus.
  • the controlling may be performed by the server 30 distal from the treatment apparatus 70 (e.g., during a telemedicine session).
  • Controlling the treatment apparatus 70 distally may include the server 30 transmitting, based on the treatment plan, a control instruction to change a parameter of the treatment apparatus 70 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.
  • 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.
  • the server 30, executing one or more machine learning models 13 may transmit a control signal to the treatment apparatus 70 to cause the treatment apparatus 70 to change a parameter (e.g., slow down, stop, etc.).
  • 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.
  • the processing device may continue to receive data while the second user uses the treatment apparatus 70 to perform the treatment plan. The data received may include characteristics of the second user while the second user uses the treatment apparatus 70 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 70, speed of actuating a portion of the treatment apparatus 70, 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.
  • the processing device may adjust, via a trained machine learning model 13, based on the data and the treatment plan, a parameter of the treatment apparatus 70.
  • the data may indicate the second user is pedaling a portion of the treatment apparatus 70 for 3 minutes at a certain speed.
  • 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.
  • the processing device may receive, from the treatment apparatus 70, data pertaining to second characteristics of the second user while the second user uses the treatment apparatus 70 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 70, speed of actuating a portion of the treatment apparatus 70, etc.) of the second user as the second user uses the treatment apparatus 70 to perform the treatment plan.
  • 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.
  • 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 70 may be dynamically adjusted, in real-time while the user is using the treatment apparatus 70, 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 70 while the second user uses the treatment apparatus.
  • 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 70 while the second user uses the treatment apparatus.
  • FIG. 15 shows an example embodiment of a method 1500 for presenting, during a telemedicine session, the recommended treatment plan to a healthcare professional according to the present disclosure.
  • Method 1500 includes operations performed by processors of a computing device (e.g., any component of FIG. 1, such as server 30 executing the artificial intelligence engine 11).
  • processors of a computing device e.g., any component of FIG. 1, such as server 30 executing the artificial intelligence engine 11.
  • one or more operations of the method 1500 are implemented in computer instructions stored on a memory device and executed by a processing device.
  • the method 1500 may be performed in the same or a similar manner as described above in regard to method 1400.
  • the operations of the method 1500 may be performed in some combination with any of the operations of any of the methods described herein.
  • the method 1500 may occur after 1410 and prior to 1412 in the method 1400 depicted in FIG. 14. That is, the method 1500 may occur prior to the server 30 executing the one or more machine learning models 13 controlling the treatment apparatus 70.
  • 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 94) of a healthcare 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).
  • the processing device may receive, from the computing device of the healthcare professional, a selection of the treatment plan.
  • the healthcare 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 30, which receives the selection.
  • Each of the treatment plans recommended may provide different results and the healthcare professional may consult, during the telemedicine session, with the user to discuss which result the user desires.
  • the recommended treatment plans may only be presented on the computing device of the healthcare professional and not on the computing device of the user (patient interface 50).
  • the healthcare 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 50 for presentation.
  • the healthcare 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.
  • the processing device may control, based on the selected treatment plan, the treatment apparatus while the second user uses the treatment apparatus 70.
  • the processor, or processing device, 36 may generate an enhanced environment representative of an environment.
  • the enhanced environment may be displayed in the patient interface 50.
  • the patient interface 50 may be one of an augmented reality device, a virtual reality device, a mixed reality device, and an immersive reality device configured to present the enhanced environment.
  • the enhanced environment may be any of a selected or predetermined environment, e.g., a template living room or rehabilitation center.
  • the enhanced environment may be selected from a menu displayed in the patient interface 50 and displaying one or more option for the enhanced environment.
  • the processor 36 may receive, from the apparatus sensors 76, apparatus data representative of a location of the apparatus in the environment.
  • the apparatus sensors 76 may communicate with the server 30, and in turn, the processors 36, the data associated with a location of the apparatus sensors 76, and in turn, the apparatus 70 in the environment.
  • the processors 36 may also generate, from the apparatus data, an apparatus avatar in the enhanced environment.
  • the processor 36 may also receive, from the user sensors 82, user data representative of a location of the user in the environment.
  • the user sensors 82 may communicate with the server 30.
  • the processors 36 may receive, from the server 30, the data associated with a location of the user sensors 82, and in turn, the apparatus 70 in the environment.
  • the processors 36 may generate, from the user data, a user avatar in the enhanced environment.
  • the processor 36 may also receive, from the treatment sensors (not illustrated), treatment data representative of one or more locations of the treatment sensors in the environment.
  • the user sensors 82 may communicate with the server 30, and in turn, the processors 36, the data associated with a location of the user sensors 82, and in turn, the apparatus 70 in the environment.
  • the processors 36 may generate, from the treatment data, treatment sensor avatars in the enhanced environment.
  • the processor 36 may calculate, based on one or more of the apparatus data and the user data, a treatment location for each treatment sensor, wherein the treatment location is associated with an anatomical structure of a user.
  • the processor 36 may further generate, based on the treatment location and treatment data, instruction data representing an instruction for positioning the treatment sensors at the treatment location.
  • the instruction data may represent instructions capable of altering the location of the treatment sensor to be one of adjacent to, at, and near the treatment location.
  • the instruction data may represent instruction confirming that the location of the treatment sensor is one of adjacent to, at, and near the treatment location. For example, when the goniometer is located at an ideal location on the user, the instruction data represents instruction confirming the goniometer is in an optimal position on the user for rehabilitation.
  • the instruction data represents instruction confirming the goniometer is out of place and needs to be moved.
  • the instruction may represent one or more of arrows, a color-coded indicator, an audible indication and a textual message.
  • the instruction can be any medium suitable for communicating with a user.
  • the instruction may be one or more of a color-coded indication, audible indication, a textual message, and/or other sensorial or perceptive (e.g., tactile, gustatory, haptic, pressure-sensing-based or electromagnetic (e.g., neurostimulation) communication indication.
  • the processor 36 may output, to the patient (or user) interface 50 and based on the environment data, an image representative of the enhanced environment. Further, the processors 36 may output, to the patient interface 50 (or any other interface, such as: the assistance interface 94, reporting interface 92, supervisory interface 140, clinician interface 20), an image representing the user, apparatus and treatment sensor avatars.
  • the avatars can be of any shape or form sufficient to communicate with a user the relative location of object in the enhanced environment.
  • the processor 36 may also generate, based on the treatment location, a treatment location avatar.
  • the processors 36 may output, to the patient interface 50, an image representing the treatment location avatar.
  • the treatment location avatar may be one of overlaid on and transposed with a portion of the user avatar.
  • the treatment location avatar would display as if on the user avatar and represent a location where the goniometer is to be placed.
  • the processors 36 is may output, to the patient interface 5050, the treatment location avatar in a frequency, or pattern, configured to cause the avatar to flash and wherein the frequency of flashing is one or more of: variable (e.g. the treatment location avatar blinks); static (e.g., the treatment avatar does not blink but presented as a shaded object or an object outlined by a solid line); and based on the instruction data.
  • the processor 36 may output, to the patient interface 50, instructions confirming that the location of the treatment sensor is one of adjacent to, at, and near the treatment location.
  • the processor 36 may output, to the patient interface 50, instructions to move the location of the treatment sensor to the treatment location.
  • the instruction can be any one of the instruction discussed above, or in any other form sufficient to communicate with a user.
  • the processor 36 may receive, during a telemedicine-enabled appointment between the user and a healthcare professional, medical instruction data representative of instructions from the healthcare professional.
  • the processor 36 may output, to the user interface 50 and based on the medical instruction data, instructions from the healthcare professional.
  • the medical instruction data refers to a recommended treatment location.
  • a system for positioning one or more sensors on a user comprising: an apparatus configured to be manipulated by the user to perform an exercise; user sensors associated with the user; apparatus sensors associated with the apparatus; treatment sensors; a processing device; a memory communicatively coupled to the processing device and including computer readable instructions, that when executed by the processing device, cause the processing device to: generate an enhanced environment representative of an environment; receive, from the apparatus sensors, apparatus data representative of a location of the apparatus in the environment; generate, from the apparatus data, an apparatus avatar in the enhanced environment; receive, from the user sensors, user data representative of a location of the user in the environment; generate, from the user data, a user avatar in the enhanced environment; receive, from the treatment sensors, treatment data representative of one or more locations of the treatment sensors in the environment; generate, from the treatment data, treatment sensor avatars in the enhanced environment; calculate, based on one or more of the apparatus data and the user data, a treatment location for each treatment sensor, wherein the treatment location is associated with an anatomical structure of a user; and generate
  • Clause 32 The system of any clause herein, further comprising an interface and wherein the processing device is further configured to: output, to the interface and based on the environment data, an image representative of the enhanced environment; and output, to the interface, an image representing the user, apparatus and treatment sensor avatars.
  • Clause 33 The system of any clause herein, wherein the processing device is further configured to: generate, based on the treatment location, a treatment location avatar; output, to the interface, an image representing the treatment location avatar.
  • Clause 34 The system of any clause herein, wherein the treatment location avatar is one of overlaid on and transposed with a portion of the user avatar.
  • Clause 35 The system of any clause herein, wherein the processing device is further configured to output, to the interface, the treatment location avatar in a frequency configured to cause the avatar to flash and wherein the frequency of flashing is one or more of: variable; static; and based on the instruction data.
  • Clause 37 The system of any clause herein, wherein the processing device is further configured to output, to the interface, instructions confirming that the location of the treatment sensor is one of adjacent to, at, and near the treatment location.
  • a method for positioning one or more sensors on a user comprising: generating an enhanced environment associated with an environment; receiving, from apparatus sensors, apparatus data representative of a location of an apparatus in the environment; generating, from the apparatus data, an apparatus avatar in the enhanced environment; receiving, from user sensors, user data representative of a location of the user in the environment; generating, from the user data, a user avatar in the enhanced environment; receiving, from treatment sensors, treatment data representative of a location the treatment sensors in the environment; generating, from the treatment data, treatment sensor avatars in the enhanced environment; calculating, based on one or more of the environment data, the apparatus data, and the user data, a treatment location for each treatment sensor; and generating, based on the treatment location and the treatment data, instruction data representative of the treatment sensors relative to the user.
  • Clause 47 The method of any clause herein, further comprising: displaying, in an interface and based on the environment data, an image representative of the enhanced environment; and displaying, with the interface, an image representative of the user, apparatus and treatment sensor avatars.
  • Clause 48 The method of any clause herein, further comprising: generating, based on the treatment location, a treatment location avatar; displaying, with the interface, an image representative of the treatment location avatar.
  • Clause 50 The method of any clause herein, further comprising displaying, with the interface, the treatment location avatar in a flashing pattern wherein, the flashing pattern is based on the instruction data.
  • Clause 52 The method of any clause herein, further comprised of displaying, with the interface, instructions confirming that the location of the treatment sensor is one of adjacent to, at, and near the treatment location.
  • Clause 53 The method of any clause herein, wherein the instructions are one or more of a color- coded indication, audible indication, and a textual message.
  • the instruction data represents instructions to alter the location of the treatment sensor to be one of adjacent to, at, and near the treatment location.
  • Clause 55 The method of any clause herein, further comprising displaying, with the interface, instructions to move the location of the treatment sensor to the treatment location.
  • Clause 57 The method of any clause herein, further comprising receiving, during a telemedicine-enabled appointment between the user and a healthcare professional, medical instruction data representative of instructions from the healthcare professional.
  • Clause 58 The method of any clause herein, wherein the processing device is further configured to output, to the interface and based on the medical instruction data, instruction from the healthcare professional.
  • Clause 60 The method of any clause herein, further comprising displaying, with a medical interface remote from the user and associated with the healthcare professional, a user response to the medical instruction.

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Abstract

L'invention concerne un système comprenant un dispositif de mémoire stockant des instructions et un dispositif de traitement, couplé en communication au dispositif de mémoire. Le dispositif de traitement exécute les instructions : pour recevoir des données d'utilisateur obtenues à partir d'enregistrements associés à un utilisateur ; pour générer un plan modifié de traitement selon les données d'utilisateur ; et pour envoyer, à un appareil de traitement accessible à l'utilisateur, le plan modifié de traitement qui amène l'appareil de traitement à mettre à jour au moins un de ses aspects opérationnels et à mettre à jour au moins un aspect opérationnel d'au moins un autre dispositif couplé en communication à l'appareil de traitement.
PCT/US2022/012199 2021-01-12 2022-01-12 Procédé et système d'implémentation d'environnements de traitement dynamiques selon des informations de patients WO2022155260A1 (fr)

Applications Claiming Priority (8)

Application Number Priority Date Filing Date Title
US17/147,232 2021-01-12
US17/147,232 US20210134432A1 (en) 2019-10-03 2021-01-12 Method and system for implementing dynamic treatment environments based on patient information
US17/148,047 US20210128080A1 (en) 2019-10-03 2021-01-13 Augmented reality placement of goniometer or other sensors
US17/148,047 2021-01-13
US17/379,681 US11445985B2 (en) 2019-10-03 2021-07-19 Augmented reality placement of goniometer or other sensors
US17/379,661 2021-07-19
US17/379,661 US11410768B2 (en) 2019-10-03 2021-07-19 Method and system for implementing dynamic treatment environments based on patient information
US17/379,681 2021-07-19

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