EP3930827A2

EP3930827A2 - Computer systems and methods for enhancing neurorehabilitation

Info

Publication number: EP3930827A2
Application number: EP20712806.7A
Authority: EP
Inventors: Kimberly Lynn SKINNER; Richard William SABOURIN; John Alexander Paul REYNISH; Michael Stuart BAILEY; Kai Christopher LEDDY; Adam Nicholas PARKER; Samuel Dominic CONNOLLY; Richard Ian LONG
Original assignee: Helius Medical Inc
Current assignee: Helius Medical Inc
Priority date: 2019-02-28
Filing date: 2020-02-26
Publication date: 2022-01-05
Also published as: WO2020176594A2; CN113728393A; GB202113774D0; WO2020176594A3; GB2596678B; IL285901A; AU2020228618A1; GB2596678A; CA3131684A1

Abstract

A computerized method of processing data including receiving, from a portable neurostimulation device having a controller, portable neurostimulation device identifying information; receiving, from a healthcare professional, patient identifying information for a patient; creating an electronic record for the patient, the electronic record pairing the portable neurostimulation device identifying information with the patient identifying information; receiving, via a healthcare professional, first input specifying a first treatment regimen for the patient; processing the first input specifying the first treatment regimen, thereby creating a device-ready first treatment regimen; sending, to the portable neurostimulation device, the device-ready first treatment regimen; receiving, from the portable neurostimulation device, first treatment regimen data for the patient; and generating, based on the first treatment regimen data, a first treatment regimen data set for the patient.

Description

COMPUTER SYSTEMS AND METHODS FOR

ENHANCING NEUROREHABILITATION

CROSS REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit of and priority to U.S. Provisional Patent Application No. 62/812,185 filed February 28, 2019, the entire content of which is owned by the assignee of the instant application and incorporated herein by reference in its entirety.

TECHNICAL FIELD

[0002] This application relates generally to systems, methods and apparatuses, including computer programs, for enhancing neurorehabilitation. More specifically, this application relates to software tools for handling information of patients undergoing neurorehabilitation treatment.

BACKGROUND

[0003] Neurorehabilitation is an emerging field in medical science in which patients suffering from damage to, or impairment of, all or a portion of their central nervous system (CNS) are treated to rehabilitate neural pathways, and/or establish new neural pathways, to at least partially compensate for the damage/impairment. Examples of neurorehabilitation devices are described in prior issued patents, e.g., U.S. Patent No. 9,072,889 to Guarraia et al, U.S. Patent No. 9,227,051 to Fisk et al, U.S. Patent No. 9,415,209 to Fisk et al, and U.S. Patent No. 9,616,222 to Guarraia et al, all of which are hereby incorporated by reference in their entireties.

[0004] Neurorehabilitation is commonly effected by non-invasive methods such as physical therapy, occupational therapy, or speech therapy, which involve the use of exercise to attempt to increase a patient’s abilities. For example, one suffering from a spinal cord injury might exercise an affected area of the body to increase coordination and range of motion. However, these methods suffer from the disadvantage of being time-consuming, difficult and exhausting for the patient. Invasive methods also exist, such as electrostimulation, wherein electrodes are implanted to deliver electricity at or near neural pathways to enhance neural function, and/or to counter erroneous neural function. For example, deep brain stimulation (DBS) may be used for treatment of Parkinson’s Disease and depression, left vagal nerve stimulation (LVNS) may be used for treatment of epilepsy, or sub-dural implantable stimulators may be used to assist with stroke recovery. These invasive methods are risky and expensive and thus are generally used as a last resort when all other therapeutic interventions have failed.

[0005] Although methods and devices for neurorehabilitation that are noninvasive or minimally invasive are available, what would be beneficial are more sophisticated tools (e.g., software) for tracking and analyzing a specific patient’s progress during neurorehabilitation therapy to better aid neurorehabilitation treatment.

SUMMARY

[0006] Accordingly, the invention provides a novel framework, including a computing system and associated computing methods and modules, for storing, processing, transferring, analyzing, and displaying information collected from patients undergoing neurorehabilitation treatment. A portable neurostimulation device entrusted to a patient can collect patient data during a therapy session and provide the data to a personal computer of a healthcare professional (HCP) during a therapy visit. The HCP can have installed on his or her personal computer a data management application (DMA) for receiving, processing, analyzing, and/or displaying the data on the personal computer, and/or uploading the data to a remote server having a counterpart DMA cloud service installed. The DMA cloud service can receive data for the patients of one or more HCPs and can store some or all patient data in a remote DMA database. The systems and methods described herein can also aid in monitoring a specific activity of a patient during a therapy session, e.g., by receiving data recorded by an accelerometer of the patient’s portable neurostimulation device and applying an algorithm to the data to classify the patient’s activity during a therapy session.

[0007] In one aspect, the invention features a computerized method of processing data. The computerized method includes receiving, by a computing device, from a portable

neurostimulation device having a controller, portable neurostimulation device identifying information. The computerized method also includes receiving, by the computing device, from a healthcare professional, patient identifying information for a patient. The computerized method also includes creating, by the computing device, an electronic record for the patient, the electronic record pairing the portable neurostimulation device identifying information with the patient identifying information. The computerized method also includes receiving, by the computing device, via a healthcare professional, first input specifying a first treatment regimen for the patient. The computerized method also includes processing, by the computing device, the first input specifying the first treatment regimen, thereby creating a device-ready first treatment regimen. The computerized method also includes sending, by the computing device, to the portable neurostimulation device, the device-ready first treatment regimen. The computerized method also includes receiving, by the computing device, from the portable neurostimulation device, first treatment regimen data for the patient. The computerized method also includes generating, by the computing device, based on the first treatment regimen data, a first treatment regimen data set for the patient.

[0008] In some embodiments, the method includes displaying, by the computing device, the first treatment regimen data set for the healthcare professional. In some embodiments, the method includes sending, by the computing device, via an electronic communications network, the first treatment regimen data set to a remote server. In some embodiments, the method includes storing, by the remote server, the first treatment regimen data set in a database in electronic communication with the remote server. In some embodiments, the method includes receiving, by the computing device, input specifying a second treatment regimen for the patient via the healthcare professional. In some embodiments, the method includes processing, by the computing device, the input specifying the second treatment regimen, thereby creating a device ready second treatment regimen. In some embodiments, the method includes sending, by the computing device, to the portable neurostimulation device, the device-ready second treatment regimen. In some embodiments, the method includes receiving, by the computing device, from the portable neurostimulation device, second treatment regimen data for the patient. In some embodiments, the method includes generating, by the computing device, based on the second treatment regimen data, a second treatment regimen data set for the patient.

[0009] In some embodiments, the method includes displaying, by the computing device, the second treatment regimen data set for the healthcare professional. In some embodiments, the method includes sending, by the computing device, via an electronic communication medium, the second treatment regimen data set to a remote server. In some embodiments, the method includes storing, by the remote server, the second treatment regimen data set in a database in electronic communication with the remote server. In some embodiments, the patient identifying information includes a full name, a date of birth, a time zone, and a patient reference code for the patient. In some embodiments, the input specifying the first treatment regimen includes (i) a number of therapy sessions, (ii) a type of periodic activity for each of the therapy sessions, and (iii) a type of device feedback to signal an end of each of the therapy sessions. In some embodiments, the input specifying the first treatment regimen includes an expiration date. In some embodiments, the expiration date is set to occur after a subsequent patient visit to a healthcare professional. In some embodiments, the method includes generating, by the computing device, based on the first treatment regimen data set, at least one of a summary page, an activity page, and/or a report for the treatment regimen. In some embodiments, the first device-ready treatment regimen includes one or more commands. In some embodiments, each command includes multiple ASCII characters representing a payload start, a payload, a payload end, a checksum, and a message end. In some embodiments, the first treatment regimen data for the patient includes one or more responses. In some embodiments, each response includes multiple ASCII characters representing a payload start, a payload, a payload end, a checksum, and a message end.

[0010] In another aspect, the invention features a computerized system including a computing device for processing patient data. The computing device is configured to receive, from a portable neurostimulation device having a controller, portable neurostimulation device identifying information. The computing device is also configured to receive, from a healthcare professional, patient identifying information for a patient. The computing device is also configured to create an electronic record for the patient, the electronic record pairing the portable neurostimulation device identifying information with the patient identifying information. The computing device is also configured to receive, via a healthcare professional, first input specifying a first treatment regimen for the patient. The computing device is also configured to process the first input specifying the first treatment regimen, thereby creating a device-ready first treatment regimen. The computing device is also configured to send, to the portable

neurostimulation device, the device-ready first treatment regimen. The computing device is also configured to receive, from the portable neurostimulation device, first treatment regimen data for the patient. The computing device is also configured to generate, based on the first treatment regimen data, a first treatment regimen data set for the patient.

[0011] In some embodiments, the system includes a portable neurostimulation device having a controller and an interface for communicating electronically with the computing device. In some embodiments, the system includes a remote server in electronic communication with the computing device over an electronic communications network. In some embodiments, the system includes a database in electronic communication with the remote server.

[0012] In another aspect, the invention features a computerized method of determining a patient activity during a time interval. The method includes receiving, by a computing system, from a portable neurostimulation device having an accelerometer, acceleration data measured for the time interval corresponding to movement of a patient during the time interval, the acceleration data reflecting a set of acceleration values. The method also includes parsing, by the computing system, the acceleration data into subsets corresponding to multiple discrete periods within the time interval. The method also includes determining, by the computing system, for each of one or more threshold magnitudes, a number of discrete periods for which any acceleration value within a discrete period exceeds the threshold magnitude. The method also includes

determining, by the computing system, for each of the one or more threshold magnitudes, for the number of discrete periods, a subset of the number of discrete periods for which an immediately preceding discrete period also includes an acceleration value exceeding the threshold magnitude. The method also includes calculating, by the computing system, for each of the one or more threshold magnitudes, an activity fraction by dividing the number of the subset of discrete periods by the number of discrete periods. The method also includes classifying, by the computing system, based on at least one of the activity fractions, an activity of the patient during the time interval.

[0013] In some embodiments, the computing system includes a healthcare professional personal computing device and a portable neurostimulation device having a controller. In some embodiments, the portable neurostimulation device determines, and/or provides to the healthcare professional computing device, the activity fractions and/or the number of discrete periods. In some embodiments, the healthcare computing device classifies the patient activity based on the activity fractions and the number of discrete periods. In some embodiments, classifying an activity of the patient during the time interval includes determining an activity according to activity fraction within the time interval, as follows: (i) a first activity fraction of greater than or equal to 0.9 denotes walking; (ii) a second activity fraction of greater than or equal to 0.85 denotes balancing; (iii) a third activity fraction of greater than or equal to 0.8 denotes breathing; and (iv) a third activity fraction of less than 0.8 denotes inactivity. In some embodiments, other numerical thresholds are used to separate out and/or identify these activities and/or other activities or combinations of activities.

[0014] In some embodiments, the method includes displaying, by the computing device, a fraction of patient activities constituting walking, breathing and awareness training, balancing, and/or inactivity. In some embodiments, balancing and breathing and awareness training are included in the same fraction (e.g., added, packaged, and/or displayed together). In some embodiments, the threshold magnitudes of acceleration are O. lg, O.Olg, and 0.005g. In some embodiments, a sample rate of the accelerometer is at least 50Hz. In some embodiments, the length of each discrete period is 1 second. In some embodiments, a length of the time interval is at least 120 seconds. In some embodiments, the method includes generating, by the computing device, a report summarizing a patient activity determined for the time interval.

BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The advantages of the invention described above, together with further advantages, may be better understood by referring to the following description taken in conjunction with the accompanying drawings. The drawings are not necessarily to scale; emphasis is instead generally placed upon illustrating the principles of the invention.

[0016] FIG. 1 A is a high level schematic diagram of a system for aiding neurorehabilitation of a patient, according to an illustrative embodiment of the invention.

[0017] FIG. IB is a schematic diagram of a system for aiding neurorehabilitation of a patient, according to an illustrative embodiment of the invention.

[0018] FIG. 2A is a schematic diagram of a computer architecture implementing a data management application for handling neurorehabilitation patient information, according to an illustrative embodiment of the invention.

[0019] FIG. 2B is a schematic diagram of a computer architecture of a DMA cloud service for handling neurorehabilitation patient information, according to an illustrative embodiment of the invention.

[0020] FIG. 3 is a sequence diagram of computerized steps that occur when a controller is connected to a HCP computing device, according to an illustrative embodiment of the invention.

[0021] FIG. 4 is a sequence diagram of computerized steps that occur when a HCP uploads patient data to a cloud platform, according to an illustrative embodiment of the invention.

[0022] FIG. 5 is a sequence diagram of computerized steps that occur when a HCP sets a regime for implementation by a portable neurostimulation device, according to an illustrative embodiment of the invention.

[0023] FIGS. 6A-6N show multiple illustrations of screenshots of the DMA in use by a HCP, according to an illustrative embodiment of the invention.

[0024] FIGS. 7A-7G show multiple illustrations of screenshots of a back-end cloud interface configured for use by an administrator, according to an illustrative embodiment of the invention.

[0025] FIG. 8 shows a flowchart of an activity monitoring algorithm for determining an activity of a patient during a therapy session, according to an illustrative embodiment of the invention.

[0026] FIGS. 9A and 9B show exemplary graphs of acceleration vs. time measured by an accelerometer of a patient’s portable neurostimulation device, according to an illustrative embodiment of the invention.

[0027] FIG. 10 shows a flowchart displaying mathematical details of an activity monitoring algorithm for determining an activity of a patient during a therapy session, according to an illustrative embodiment of the invention.

[0028] FIG. 11 is a method flow chart of a method of processing data, according to an illustrative embodiment of the invention.

[0029] FIG. 12 is a method flow chart of a computerized method of determining a patient activity for a time interval, according to an illustrative embodiment of the invention.

DETAILED DESCRIPTION

[0030] FIG. 1 A is a high level schematic diagram of a system 100 for aiding neurorehabilitation of a patient, according to an illustrative embodiment of the invention. The system 100 includes a portable neurostimulation device 102 having a controller, an HCP computing device 104 having an installed data management application (DMA), a remote server computing device 106 having an installed DMA cloud service, and a remote DMA database 108. The DMA and the DMA cloud service can be collectively referred to as the DMA software 110. The portable

neurostimulation device 102 can be a PoNS^® device having a PoNS^® controller, provided by Helius Medical, Inc. The HCP computing device 104 can be a personal computer or tablet in a therapy clinic. The remote server computing device 106 and the remote DMA database 108 can be hosted by a third party cloud service, e.g., Amazon Web Services (AWS).

[0031] The portable neurostimulation device 102 provides electrical stimulation to a patient’s tongue (e.g., upon command of the patient) in accordance with stimulation parameters (e.g., duration) that are pre-defmed by the HCP. The portable neurostimulation device 102 can be used in conjunction with one or more physical therapy exercises, e.g., with the goal of improving balance and/or gait disorders resulting from traumatic brain injury (TBI) or enhancing neurorehabilitation of the patient to various other ends. The portable neurostimulation device 102 can connect to the HCP computing device 104 electronically, e.g., by using a standard USB device interface. The HCP computing device can host the DMA, which includes software used by the HCP for various purposes, e.g., to (i) record the number of expected daily activity sessions a patient is expected to perform, (ii) view the patient’s usage data after a period of treatment and (iii) to configure the neurostimulation device 102 via its controller (e.g., with one or more personalized therapy regimens).

[0032] The HCP computing device 104 is in electronic communication with the remote server computing device 106, e.g., via the Internet. The remote server computing device 106 hosts the DMA cloud service, which can receive data from the DMA, sort and analyze patient data, and send data via electronic communication to the DMA database 108, e.g., for storage and retrieval. Multiple HCP computing devices can connect to the DMA cloud service, and data for multiple patients (including protected health information or“PHI”) can be stored in the DMA database 108. In some embodiments, the DMA software is not intended for use by patients; it is a tool to be used by HCPs, who can access and use it only after receiving suitable training on its functionality. A HCP user must log in to access the DMA using unique account information (e.g., a username and password).

[0033] FIG. IB is a schematic diagram of a system 120 for aiding neurorehabilitation of a patient, according to an illustrative embodiment of the invention. The system 120 includes the components shown in FIG. 1 A but shows the portable neurostimulation device 102 in greater schematic detail. In particular, the portable neurostimulation device 102 includes a controller 122 and a mouthpiece 124 in electrical communication with the controller 122. The controller 122 includes a primary user interface and drive electronics. Through the primary user interface stimulation can be started and stopped; the intensity of stimulation can be adjusted; and the system can be powered on and off. The primary user interface includes a visual display, audio feedback and vibration feedback. The mouthpiece 124 includes a cable to connect it to the controller 122. The mouthpiece 124 is placed in the patient’s mouth and houses the electrodes that contact the patient’s tongue to deliver the stimulation current to the patient.

[0034] The portable neurostimulation device 102 also includes a mouthpiece retainer case 126, a carry case 128, and a charger 130. The mouthpiece retainer case 126 provides a sanitary and durable protective case for the mouthpiece. The carry case 126 provides a protective case to store and transport the controller 122 and mouthpiece 124 when they are not in use. The charger 128 includes a main power adapter that connects to the controller 122, e.g., via a standard USB connector and provides a low voltage power supply to recharge the controller battery.

Stimulation can be disabled while the charger 130 is connected to the controller 122.

[0035] The system 100 shown and described above in FIGS. 1 A-1B can deliver an electrical neuromodulation waveform to specific areas of a patient’s tongue. This waveform can be used in conjunction with specific physical therapy, which together can comprise a treatment regimen. The treatment regimen can be used for patients, e.g., with chronic balance and/or gait disorders resulting from Traumatic Brain Injury (TBI). The HCP can configure the controller 122 with a treatment regimen specific to the patient and their stage of treatment using the DMA. The portable neurostimulation device 102 can then be used by the patient, potentially several times per day, over a period of days to suit the prescribed treatment. The treatment may be self- managed by the patient during the period or may be supervised by the HCP. After treatment, the HCP can download and view results and alerts from the controller 122 to the HCP computing device 104 using the DMA.

[0036] FIG. 2A is a schematic diagram of a computer architecture 200 implementing a data management application (DMA) for handling neurorehabilitation patient information, according to an illustrative embodiment of the invention. As described above, the DMA software can be divided into two parts: (i) a desktop-based DMA (client application, as shown) and a cloud-based DMA cloud service (DMA server, as shown). The client application can be used by HCPs to (i) retrieve and view patient activity session data from connected PoNS^® controllers; (ii) set new patient prescriptions on connected PoNS^® controllers; and/or (iii) create patient usage summary PDF reports. The client application can send its patient activity session data to the DMA server. This cloud service can be responsible for: (i) storing PoNS^® patient activity session data; (ii) retrieving PoNS^® patient activity session data for display by the desktop application; (iii) storing patient prescription history; and/or (iv) associating patients with PoNS^® controllers. In addition to these responsibilities, the DMA software can include access control, auditing and user account management responsibilities (e.g., as described in greater detail herein).

[0037] FIG. 2B is a schematic diagram of a computer architecture 250 of a DMA cloud service for handling neurorehabilitation patient information, according to an illustrative embodiment of the invention. The DMA cloud service can interact with a database for storing patient data. Figure 2B demonstrates how these components can be deployed multiple times for redundancy across a cloud infrastructure provided by AWS and can be supported by ancillary internal and AWS provided services. Cloud service components can include: (i) a DMA HTTP web service (shown as“DMA”), which responds to incoming requests from the DMA Client using data stored in the database; (ii) an AWS Relational Database Service (shown as“RDS Master” and “RDS Standby”), which stores all patient, device and practitioner data; (iii) an AWS

CloudWatch Logs Service (shown as AWS CloudWatch), which encrypts and stores the web service application and audit log streams; (iv) an AWS Elastic Load Balancer (shown as“Load Balancer”), which, as the holder of the cloud’s web domain certificate, receives incoming encrypted client requests and shares them amongst the multiple DMA HTTP web services; and (v) an AWS Simple Email Service (Shown as“SES password reset”), which is used to send password reset and account setup emails to users.

[0038] FIG. 3 is a sequence diagram 300 of computerized steps that occur when a controller is connected to a HCP computing device, according to an illustrative embodiment of the invention. Once connected, the client application’s desktop application requests data from the device to verify it is suitable to be used with the current DMA version before sending a notification to the web application. This process continues by checking device status in the cloud, e.g., by asking whether it is registered on the system and whether it has a patient assigned. Once this process is completed, the web application is notified of the device’s full status and the user is presented with a list of options to continue.

[0039] FIG. 4 is a sequence diagram 400 of computerized steps that occur when a HCP uploads patient data to a cloud platform, according to an illustrative embodiment of the invention. If a device with activity records is connected, the HCP is prompted to upload these records to the cloud platform. This diagram shows the data flow once the user has started the upload process. The desktop app then sends all of the records to the cloud platform in a batch operation. Once the cloud platform has received the data, it begins a process of verifying the data received. Once complete, it updates the database with the correct records and sends a response detailing the result. The user is then notified of the result and the records are removed from the device.

[0040] FIG. 5 is a sequence diagram 500 of computerized steps that occur when a HCP sets a regime for implementation by a portable neurostimulation device, according to an illustrative embodiment of the invention. Setting a regime starts with the healthcare professional creating a regime in the web application. When they choose to continue, the regime is sent to the cloud, where it is validated and returned. The web application then requests the server time, which is required to update the internal clock on the controller. Once all of these steps have been completed, the desktop application then writes the new configuration to the controller via a series of requests.

[0041] FIGS. 6A-6N show multiple illustrations of screenshots of the DMA in use by a HCP, according to an illustrative embodiment of the invention. FIG. 6A shows an initial account creation screen 604. A HCP or user will receive a“create account” email including a link to begin downloading the DMA software application (e.g., the PoNS^® Software). Once the download has completed, the HCP opens the downloaded file and follows the instructions within the setup wizard to complete the setup. The PoNS^® Software should start automatically after it has been installed. (If the PoNS^® Software does not start automatically, the HCP can click on the PoNS^® Software icon on his or her desktop to start the application.) Once the PoNS^® Software is open, the HCP clicks a second link in the“create account” email and follows the instructions on screen to create a password for the new account. Once the password is entered, the user clicks“SAVE PASSWORD” to complete his or her account setup.

[0042] FIG. 6B shows an account sign in screen 608. A HCP or user clicks on the PoNS^® Software icon on his or her desktop to start the PoNS^® Software, ensuring that s/he has a working internet connection before attempting to sign in. The user then enters his or her email address and password and clicks“LOG IN” to continue. FIG. 6C shows the next screen 612 that the user encounters, which is a prompt to connect a new PoNS^® controller to the computer using the USB cable provided. After the user connects the new PoNS^® controller, the PoNS^® software will automatically detect that a new controller has been connected to the computer.

[0043] FIG. 6D shows the next screen 616 that the user encounters, which includes a choice of two options for proceeding that correspond to two different possible scenarios: (1)“This is a new patient with a new device;” or (2)“This is a return patient with a replacement device.” FIGS. 6E-6L below show the sequence of screens if option 1 is selected; FIGS. 6M-6N show the sequence of screens encountered if option 2 is selected. FIG. 6E shows the next screen 620 encountered if option (1) is selected, which is a screen for adding new patient information. The screen 620 can include fields for the HCP to enter the patient’s first name, last name, date of birth, time zone, and patient reference number. The patient reference number can be a unique code assigned to each patient. In some embodiments, it may be helpful to use the same reference used in the HCP’s patient records (as this will be shown on reports generated by the PoNS^® software). When this information has been entered, the user can click“ADD” to continue.

[0044] FIG. 6F shows the next screen 624 the HCP sees, which prompts the HCP to create a treatment regimen. A daily treatment regimen can be created for the patient by completing the following steps. First, the HCP selects one or more daily activity sessions, which are to be completed by the patient each day. In some embodiments, the patient may perform up to a total of 10 activity sessions each day split across the following activities: gait, balance, and breathing and awareness training (BAT). In some embodiments, each session lasts for 20 minutes.

Second, the user selects the type of device feedback the patient will receive to signal the end of a session. In some embodiments, this is an audible beep and/or a vibration. Third, the user selects the appropriate device language to be displayed on the patient’s device. When this information has been entered, the user clicks“SET DAILY REGIMEN” to continue.

[0045] FIG. 6G shows the next screen 628 the HCP sees, which prompts the HCP to disconnect the device. Before disconnecting the device, the user should check the device to ensure that it will not expire before the next patient visit. If needed, the user can extend the device expiration date by clicking“EXTEND DEVICE EXPIRATION.” Then, the user disconnects the device by removing the USB cable from the computer. The PoNS^® software will confirm the device has been disconnected. Once the device has been disconnected, it is ready for use by the patient for his or her treatment sessions. The controller records the patient’s use of the PoNS^®, e.g., the number of sessions attempted and the duration of each session.

[0046] The next time the device is connected to the user’s computer, the PoNS^® software can display screens prompting the user to sign into his or her account and connect the patient’s device, similar to what was described above. After the device is connected, the PoNS^® software can display a screen to confirm the patient’s date of birth for the device, as shown by screen 632 in FIG. 6H. After the patient’s date of birth is confirmed, the PoNS^® software will display the screen 636 shown in FIG. 61, which shows the number of activity sessions stored on the controller. The screen 636 also includes a button for the user to click,“UPLOAD DATA,” to begin retrieving session data from the patient’s controller. The controller should not be disconnected while the data is uploading. When the upload has confirmed, screen 640 as shown in FIG. 6J will be displayed. The screen 640 has a prompt for the user to click“VIEW

SESSION DATA” to review the patient session’s data.

[0047] FIG. 6K shows a screen 644 having a sample summary page of the patient’s data. The summary page provides an overview of the patient’s session data. In some embodiments, it is split into the following sections: (i) Showing; (ii) Daily Regimen; (iii) Attempted Sessions; (iv) Sessions by duration; (v) Activities (Gait, BAT & Balance); and/or (vi) Sessions over time. “Showing” displays the period of time to which the summary applies. This section can show the latest data, e.g., up until the most recent treatment regimen change or up to a maximum of 98 days. In some embodiments, patients are expected to perform their daily regimen six days per week.“Daily regimen” displays the activities that make up the patient’s current daily treatment regimen.“Attempted sessions” displays the percentage of sessions attempted against expected number of sessions.“Sessions by duration” displays a breakdown of recorded sessions based on the duration of each session performed. In some embodiments, the sessions are graded as follows: Good (17-20 minutes); OK (14-17 minutes); Poor (1-14 minutes). Activities (Gait, BAT & Balance) displays the percentage of sessions attempted against the expected number of sessions, separated by activity type. This section also displays percentages of Good, OK and Poor sessions.“Sessions over time” displays which activity sessions took place on each day, graded by duration: Good, OK and Poor. In some embodiments, each column represents a single day, with each session represented by a block. In some embodiments, the HCP can export a detailed report in a variety of formats (e.g., Microsoft Word or Excel, or Adobe PDF) and/or can produce a historical report in a similar format.

[0048] FIG. 6L shows a screen 648 of a patient’s activity page. To navigate to the activity page, the user can click“GAIT” or“BAT & BALANCE” in the navigation bar. These pages provide an overview of patient activity for a specific activity type. The activity page can be split into the following sections: (i) Attempted sessions; (ii) Sessions by duration; (iii) Sessions over time; (iv) Sessions by day; (v) Sessions by hour; and/or (vi) Create report. The“Attempted Sessions” portion shows the percentage of sessions attempted against expected number of sessions. The “Sessions by duration” displays a breakdown of recorded sessions based on the duration of each session performed. In some embodiments, the sessions are graded as follows: Good (17-20 minutes); OK (14-17 minutes); Poor (1-14 minutes). The“Sessions over time” displays which activity sessions took place on each day, graded by duration: Good, OK and Poor. Each column represents a single day, with each session represented by a block. The“Sessions by day” displays recorded sessions based on the day they were recorded. The“Sessions by hour” displays recorded sessions based on the 2 hour block in which they were recorded. These are adjusted to the patient’s time zone setting. The“Create Report” enables the HCP to create a report, change the patient’s daily regimen or extend the device expiration. The HCP can click “REPORTS” and follow the on-screen instructions to save a copy of the patient’s summary, e.g., as a PDF document.

[0049] The screen 644 in FIG. 6K also corresponds to the next screen in the sequence that enables the user to set next steps. In screen 644, the user can set a new daily treatment regimen, e.g., by clicking“CHANGE REGIMEN” to change the patient’s daily treatment regimen. This will open up the“Create regimen” page where a patient’s daily regimen can be modified. The user can also extend device expiration on this page. When the user has finished, s/he can disconnect the controller by removing the USB cable from the computer.

[0050] FIG. 6M shows a screen 652 corresponding to choosing option (2) at the screen shown above in FIG. 6D. In that case, the user selects“This is a return patient with a replacement device” to continue and is taken to the screen 656 of FIG. 6N, which is a screen permitting the user to link an existing patient to a new controller. The user can search for an existing patient by entering the patient’s first name, last name and date of birth and then clicking“SEARCH.”

Search results will display, and the user can click the“LINK” button next to the correct patient in the list. This will open up a create regimen page, e.g., as shown above.

[0051] FIGS. 7A-7G show multiple illustrations of screenshots of a back-end cloud interface configured for use by an administrator, according to an illustrative embodiment of the invention. The administrator (or“super-user) of the back-end cloud interface can access multiple administrator functions (e.g., via the Internet using a web interface) that support the PoNS^® software application used by clinical end users. Such functions may include: (1) managing the list of users by adding and/or removing users as they complete training or no longer use PoNS^® treatment for their patients; (2) adding a field for“clinic name,” which allows for monitoring of device use and/or patient compliance on a clinic by clinic basis (e.g., to inform best practices); and/or (3) allowing a controller to be disabled if misuse is suspected.

[0052] FIG. 7A shows a log in screen 704 for the back-end administrator, which includes an email (i.e., a username) and a password field, together with a forgotten password retrieval link and a“Log in” button. FIG. 7B shows a super user home screen 708, which includes links to “View Users” (e.g., to manage the user database by adding, editing, disabling or deleting users); “View Controllers” (e.g., to search for and/or manage controllers);“View Organizations” (e.g., to manage the organization database); and“View Account” (e.g., to search for and manage the patient database). FIG. 7C shows a user search screen 712 seen by the administrator when the “View Users” tab on the prior home screen 708 is clicked together with a list of users. (In this view, no users are present, and so an option to“Get started by adding your first user” is shown instead). For each user, fields for the user’s name, email, organization, and account status are available. In addition, the screen 712 shows a user search functionality and an“Add new user” button. FIG. 7D shows a success screen 716 displayed for the administrator to confirm that a new user has been added and that an activation email has been sent to

firstname.lastname@example.com. The screen 716 can be exited by either selecting“Add another user” or simply“Close.” FIG. 7E shows a populated user database screen 720 having multiple placeholder user records. In this view, several functionalities are shown for the highlighted user third down from the top of the list (View account, Quick edit, Reset password, and Disable account). FIG. 7F shows an account settings screen 724 that provides the administrator with the ability to disable a particular account, e.g., by clicking the“Enabled” or “Disabled” radio buttons as shown. FIG. 7G shows a personal information screen 728, which displays, and provides the ability to edit, personal information for a user (e.g., first name, last name, email, and organization).

[0053] FIG. 8 shows a flowchart 800 of an activity monitoring algorithm for determining an activity of a patient during a therapy session, according to an illustrative embodiment of the invention. The algorithm can function by checking to see if accelerometer readings from the patient’s portable neurostimulation device exceed a set of thresholds during periods of a specified length (e.g., one second periods) and then checking to see whether periods that have an acceleration measurement exceeding a given threshold are adjacent. The data can then be classified by considering the fraction of the periods that are above or below a second set of thresholds associated with each acceleration threshold. In this way, both the magnitude and regularity of activity can be used to divide activity into different classifications (e.g., walking, balancing, breathing and awareness, or inactivity). In some embodiments, the algorithm can classify data best using data sets that are at least 120 seconds long. A full therapy session of 20 minutes might therefore either be classified as a single data set or be divided into multiple (e.g., 10) sub-sections of 120 seconds each, which can be classified individually. The latter method could be used, e.g., to determine whether a patient was resting for part of a walking session. In some embodiments, accelerometer data is run through a low-frequency filter before being analyzed. High pass filtering can be used to remove long-term trends that are due largely to gravity and/or the orientation of the device. In some embodiments, the accelerometer provides a high pass filter that can be configured by firmware of the device to have a cut-off frequency of 0.5 HZ, which is deemed to be reasonable as the frequency of footsteps is around 0.5-1.0Hz, and the other activities are likely to have longer characteristic timescales.

[0054] FIGS. 9A and 9B show exemplary graphs (900, 950, respectively) of acceleration vs. time measured by an accelerometer of a patient’s portable neurostimulation device, according to an illustrative embodiment of the invention. The exemplary data measured in graphs 900, 950 help illustrate one embodiment of application of an activity monitoring algorithm in accordance with the principles of the present invention as applied to data taken over a period of 24 seconds. In these graphs, the different Ti (Ti, T2, and T3) represent thresholds of acceleration magnitude (one positive and one negative value for each threshold). In this example, the specific magnitudes for Ti, T2, and T3 correspond to O.lg, O.Olg, and 0.005g, respectively.“Ti total” represents the total numbers of periods above each of the three thresholds (e.g., as applied to the example data, Ti total is 2, T2 total is 22, and T3 total is 24). Ai represent the total numbers of periods that are both above the thresholds and are immediately preceded by a period that is also above the threshold (e.g., as applied to the example data, Ai total is 0, A2 total is 20, and A3 total is 24). Let NT be the total number of periods (here 24), and let Fi be the activity fractions for each threshold and NT, defined as Fi /NT. In this case, the‘activity fractions’ for each threshold are calculated as follows:

Fi = Ai / NT = 0 / 24 = 0.000

F₂ = A₂ / NT = 20 / 24 = 0.833

F₃ = A3 / NT = 24 / 24 = 1.000

[0055] Next, compare each Fi to its corresponding‘activity fraction threshold’ Pi until a classification is reached. FIG. 10 shows a flowchart 1000 displaying mathematical details of this portion of the activity monitoring algorithm, according to an illustrative embodiment of the invention. In this example, Pi is set at 0.900, P2 is set at 0.850, and P3 is set at 0.800. Thus, the computing device on which the algorithm is performed first determines whether Fi (0.000) is greater than or equal to Pi (0.9). Here it is not, so the algorithm proceeds to the next step. (Note that if it were, a classification of“walking” would be determined with no further comparisons needed.) The next determination to be made is whether F2 (0.833) is greater than or equal to P2

(0.850). Here again it is not, so the algorithm proceeds to the next step. (Note that if it were, a classification of“balancing” would be determined with no further comparisons needed.) The next determination to be made is whether F3 (1.000) is greater than or equal to P3 (0.800). Since it is, the activity can be classified as“Breathing and Awareness” with no further comparisons needed. (Note that if it were not, a classification of“inactive” would be determined with no further comparisons needed.) In some embodiments, the“counting” can be done directly on the firmware, with the thresholds applied afterward. In some embodiments, the PoNS^® software receives four numbers from the PONS^® device: three activity fractions Al, A2, A3, and NT, a total number of 1 second periods. In some embodiments, the PoNS^® software already has stored Tl, T2, and T3. In some embodiments, Tl, T2, and T3 are the same for every activity.

[0056] The accelerometer can be used with settings of a 100 Hz sample rate, although analysis of the test data shows that the accelerometer can be run at a lower sample rate (e.g., 50 Hz) without significantly affecting accuracy, while reducing power consumption. In one exemplary, non limiting test group of 63 data sets, every inactive data set was correctly identified as inactive (a total of 48 data sets) using the above algorithm, and 14 out of 15 data sets were also correctly classified using the above algorithm (the exception dealt with a set relating to a‘pronounced sway’ activity, which was assumed to be attempting to emulate a patient with TBI balancing). Thus, in total, 62 of 63 data sets were correctly identified, or about 98%. Furthermore, a ‘pronounced sway’ activity is unlikely to be a good approximation of a patient with poor balance, and it is therefore unsurprising that it was incorrectly identified during testing.

[0057] FIG. 11 is a method flow chart 1100 of a method of processing data, according to an illustrative embodiment of the invention. In a first step 1102, a computing device receives, from a portable neurostimulation device having a controller, portable neurostimulation device identifying information. In a second step 1104, the computing device receives, from a healthcare professional, patient identifying information for a patient. In a third step 1106, the computing device creates an electronic record for the patient, the electronic record pairing the portable neurostimulation device identifying information with the patient identifying information. In a fourth step 1108, the computing device receives, via a healthcare professional, first input specifying a first treatment regimen for the patient. In a fifth step 1110, the computing device processes the first input specifying the first treatment regimen, thereby creating a device-ready first treatment regimen. In a sixth step 1112, the computing device sends, to the portable neurostimulation device, the device-ready first treatment regimen. In a seventh step 1114, the computing device receives, from the portable neurostimulation device, first treatment regimen data for the patient. In an eighth step 1116, the computing device generates, based on the first treatment regimen data, a first treatment regimen data set for the patient.

[0058] FIG. 12 is a method flow chart 1200 of a computerized method of determining a patient activity for a time interval, according to an illustrative embodiment of the invention. In a first step 1202, a computing system receives, from a portable neurostimulation device having an accelerometer, acceleration data measured for the time interval corresponding to movement of a patient during the time interval, the acceleration data reflecting a set of acceleration values. In a second step 1204, the computing system parses the acceleration data into subsets corresponding to multiple discrete periods within the time interval. In a third step 1206, the computing system determines, for each of one or more threshold magnitudes, a number of discrete periods for which any acceleration value within a discrete period exceeds the threshold magnitude. In a fourth step 1208, the computing system determines, for each of the one or more threshold magnitudes, for the number of discrete periods, a subset of the number of discrete periods for which an immediately preceding discrete period also includes an acceleration value exceeding the threshold magnitude. In a fifth step 1210, the computing system calculates, for each of the one or more threshold magnitudes, an activity fraction by dividing the number of the subset of discrete periods by the number of discrete periods. In a sixth step 1212, the computing system classifies, based on at least one of the activity fractions, an activity of the patient during the time interval.

[0059] The above-described techniques can be implemented in digital and/or analog electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. The implementation can be as a computer program product, i.e., a computer program tangibly embodied in a machine-readable storage device, for execution by, or to control the operation of, a data processing apparatus, e.g., a programmable processor, a computer, and/or multiple computers. The computer program can be deployed in a cloud computing environment (e.g., Amazon® AWS, Microsoft® Azure, etc.). Method steps can be performed by one or more processors executing a computer program to perform functions of the invention by operating on input data and/or generating output data.

[0060] To provide for interaction with a user, the above described techniques can be

implemented on a computing device in communication with a display device, e.g., a plasma or LCD (liquid crystal display) monitor or a mobile computing device display or screen for displaying information to the user and a keyboard and a pointing device, e.g., a mouse, a touchpad, or a motion sensor, by which the user can provide input to the computer (e.g., interact with a user interface element). Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, and/or tactile input.

[0061] The above-described techniques can be implemented in a distributed computing system that includes a back-end component. The back-end component can, for example, be a data server, a middleware component, and/or an application server. The above described techniques can be implemented in a distributed computing system that includes a front-end component. The front-end component can, for example, be a client computer having a graphical user interface, a Web browser through which a user can interact with an example implementation, and/or other graphical user interfaces for a transmitting device. The above described techniques can be implemented in a distributed computing system that includes any combination of such back-end, middleware, or front-end components.

[0062] The components of the computing system can be interconnected by transmission medium, which can include any form or medium of digital or analog data communication (e.g., a communication network). Transmission medium can include one or more packet-based networks and/or one or more circuit-based networks in any configuration. Packet-based networks can include, for example, the Internet, a carrier internet protocol (IP) network (e.g., local area network (LAN), wide area network (WAN), campus area network (CAN),

metropolitan area network (MAN), home area network (HAN)), a private IP network, an IP private branch exchange (IPBX), a wireless network (e.g., radio access network (RAN), Bluetooth, near field communications (NFC) network, Wi-Fi, WiMAX, general packet radio service (GPRS) network, HiperLAN), and/or other packet-based networks. Circuit-based networks can include, for example, the public switched telephone network (PSTN), a legacy private branch exchange (PBX), a wireless network (e.g., RAN, code-division multiple access (CDMA) network, time division multiple access (TDMA) network, global system for mobile communications (GSM) network), and/or other circuit-based networks.

[0063] Information transfer over transmission medium can be based on one or more

communication protocols. Communication protocols can include, for example, Ethernet protocol, Internet Protocol (IP), Voice over IP (VOIP), a Peer-to-Peer (P2P) protocol, Hypertext Transfer Protocol (HTTP), Session Initiation Protocol (SIP), H.323, Media Gateway Control Protocol (MGCP), Signaling System #1 (SS7), a Global System for Mobile Communications (GSM) protocol, a Push-to-Talk (PTT) protocol, a PTT over Cellular (POC) protocol, Universal Mobile Telecommunications System (UMTS), 3GPP Long Term Evolution (LTE) and/or other communication protocols.

[0064] Devices of the computing system can include, for example, a computer, a computer with a browser device, a telephone, an IP phone, a mobile computing device (e.g., cellular phone, personal digital assistant (PDA) device, smart phone, tablet, laptop computer, electronic mail device), and/or other communication devices. The browser device includes, for example, a computer (e.g., desktop computer and/or laptop computer) with a World Wide Web browser (e.g., Chrome™ from Google, Inc., Microsoft® Internet Explorer® available from Microsoft Corporation, and/or Mozilla® Firefox available from Mozilla Corporation). Mobile computing device include, for example, a Blackberry® from Research in Motion, an iPhone® from Apple Corporation, and/or an Android™-based device. IP phones include, for example, a Cisco® Unified IP Phone 7985G and/or a Cisco® Unified Wireless Phone 7920 available from Cisco Systems, Inc.

[0065] It should also be understood that various aspects and embodiments of the technology can be combined in various ways. Based on the teachings of this specification, a person of ordinary skill in the art can readily determine how to combine these various embodiments. In addition, modifications may occur to those skilled in the art upon reading the specification.

Claims

[0066] What is claimed is:

1. A computerized method of processing data, the computerized method comprising:

receiving, by a computing device, from a portable neurostimulation device having a controller, portable neurostimulation device identifying information;

receiving, by the computing device, from a healthcare professional, patient identifying information for a patient;

creating, by the computing device, an electronic record for the patient, the electronic record pairing the portable neurostimulation device identifying information with the patient identifying information;

receiving, by the computing device, via a healthcare professional, first input specifying a first treatment regimen for the patient;

processing, by the computing device, the first input specifying the first treatment regimen, thereby creating a device-ready first treatment regimen;

sending, by the computing device, to the portable neurostimulation device, the device ready first treatment regimen;

receiving, by the computing device, from the portable neurostimulation device, first treatment regimen data for the patient; and

generating, by the computing device, based on the first treatment regimen data, a first treatment regimen data set for the patient.

2. The method of claim 1 further including displaying, by the computing device, the first treatment regimen data set for the healthcare professional.

3. The method of claim 1 further including sending, by the computing device, via an electronic communications network, the first treatment regimen data set to a remote server.

4. The method of claim 3 further including storing, by the remote server, the first treatment regimen data set in a database in electronic communication with the remote server.

5. The method of claim 1 further including:

receiving, by the computing device, input specifying a second treatment regimen for the patient via the healthcare professional;

processing, by the computing device, the input specifying the second treatment regimen, thereby creating a device-ready second treatment regimen;

sending, by the computing device, to the portable neurostimulation device, the device ready second treatment regimen;

receiving, by the computing device, from the portable neurostimulation device, second treatment regimen data for the patient; and

generating, by the computing device, based on the second treatment regimen data, a second treatment regimen data set for the patient.

6. The method of claim 5 further including displaying, by the computing device, the second treatment regimen data set for the healthcare professional.

7. The method of claim 5 further including sending, by the computing device, via an electronic communication medium, the second treatment regimen data set to a remote server.

8. The method of claim 7 further including storing, by the remote server, the second treatment regimen data set in a database in electronic communication with the remote server.

9. The method of claim 1 wherein the patient identifying information includes a full name, a date of birth, a time zone, and a patient reference code for the patient.

10. The method of claim 1 wherein the input specifying the first treatment regimen includes (i) a number of therapy sessions, (ii) a type of periodic activity for each of the therapy sessions, and (iii) a type of device feedback to signal an end of each of the therapy sessions.

11. The method of claim 1 wherein the input specifying the first treatment regimen includes an expiration date.

12. The method of claim 11 wherein the expiration date is set to occur after a subsequent patient visit to a healthcare professional.

13. The method of claim 1 further including generating, by the computing device, based on the first treatment regimen data set, at least one of a summary page, an activity page, and a report for the treatment regimen.

14. The method of claim 1 wherein the first device-ready treatment regimen includes one or more commands, each command including multiple ASCII characters representing a payload start, a payload, a payload end, a checksum, and a message end.

15. The method of claim 1 wherein the first treatment regimen data for the patient includes one or more responses, each response including multiple ASCII characters representing a payload start, a payload, a payload end, a checksum, and a message end.

16. A computerized system including a computing device for processing patient data, the computing device configured to:

receive, from a portable neurostimulation device having a controller, portable

neurostimulation device identifying information;

receive, from a healthcare professional, patient identifying information for a patient; create an electronic record for the patient, the electronic record pairing the portable neurostimulation device identifying information with the patient identifying information; receive, via a healthcare professional, first input specifying a first treatment regimen for the patient;

process the first input specifying the first treatment regimen, thereby creating a device ready first treatment regimen;

send, to the portable neurostimulation device, the device-ready first treatment regimen; receive, from the portable neurostimulation device, first treatment regimen data for the patient; and

generate, based on the first treatment regimen data, a first treatment regimen data set for the patient.

17. The system of claim 16 further including a portable neurostimulation device having a controller and an interface for communicating electronically with the computing device.

18. The system of claim 16 further including a remote server in electronic communication with the computing device over an electronic communications network.

19. The system of claim 16 further including a database in electronic communication with the remote server.

20. A computerized method of determining a patient activity during a time interval, the computerized method comprising:

receiving, by a computing system, from a portable neurostimulation device having an accelerometer, acceleration data measured for the time interval corresponding to movement of a patient during the time interval, the acceleration data reflecting a set of acceleration values; parsing, by the computing system, the acceleration data into subsets corresponding to multiple discrete periods within the time interval;

determining, by the computing system, for each of one or more threshold magnitudes, a number of discrete periods for which any acceleration value within a discrete period exceeds the threshold magnitude;

determining, by the computing system, for each of the one or more threshold magnitudes, for the number of discrete periods, a subset of the number of discrete periods for which an immediately preceding discrete period also includes an acceleration value exceeding the threshold magnitude;

calculating, by the computing system, for each of the one or more threshold magnitudes, an activity fraction by dividing the number of the subset of discrete periods by the number of discrete periods;

classifying, by the computing system, based on at least one of the activity fractions, an activity of the patient during the time interval.

21. The method of claim 20 wherein the computing system includes a healthcare professional personal computing device and a portable neurostimulation device having a controller, the portable neurostimulation device determining, and providing to the healthcare professional computing device, the activity fractions and the number of discrete periods; and the healthcare computing device classifying the patient activity based on the activity fractions and the number of discrete periods.

22. The method of claim 20 wherein classifying an activity of the patient during the time interval includes determining an activity according to activity fraction within the time interval, as follows: (i) a first activity fraction of greater than or equal to 0.9 denotes walking; (ii) a second activity fraction of greater than or equal to 0.85 denotes balancing; (iii) a third activity fraction of greater than or equal to 0.8 denotes breathing; and (iv) a third activity fraction of less than 0.8 denotes inactivity.

23. The method of claim 20 further including displaying, by the computing device, a fraction of patient activities constituting walking, breathing and awareness training, balancing, and inactivity.

24. The method of claim 23 wherein balancing and breathing and awareness training are included in the same fraction.

25. The method of claim 22 wherein the threshold magnitudes of acceleration are 0. lg,

O.Olg, and 0.005g.

26. The method of claim 20 wherein a sample rate of the accelerometer is at least 50Hz.

27. The method of claim 20 wherein the length of each discrete period is 1 second.

28. The method of claim 20 wherein a length of the time interval is at least 120 seconds.

29. The method of claim 20 further including generating, by the computing device, a report summarizing a patient activity determined for the time interval.