阅读说明：本技术 用于增强神经康复的计算机系统和方法 (Computer system and method for enhancing neurological rehabilitation ) 是由 K·L·斯金纳 R·W·萨布林 J·A·P·雷尼什 M·S·贝利 K·C·莱迪 A·N·帕 于 2020-02-26 设计创作，主要内容包括：一种处理数据的计算机化方法,包括：从具有控制器的便携式神经刺激设备接收便携式神经刺激设备标识信息；从医疗保健专业人员接收患者的患者标识信息；创建患者的电子记录,所述电子记录将便携式神经刺激设备标识信息与患者标识信息进行配对；经由医疗保健专业人员接收指定患者的第一治疗方案的第一输入；处理指定第一治疗方案的第一输入,从而创建设备就绪的第一治疗方案；将设备就绪的第一治疗方案发送到便携式神经刺激设备；从便携式神经刺激设备接收患者的第一治疗方案数据；以及基于第一治疗方案数据来生成患者的第一治疗方案数据集。(A computerized method of processing data, comprising: receiving portable neurostimulation device identification information from a portable neurostimulation device having a controller; receiving patient identification information for a patient from a healthcare professional; creating an electronic record of the patient, the electronic record pairing portable neurostimulation device identification information with patient identification information; receiving, via a healthcare professional, a first input specifying a first treatment protocol for a patient; processing a first input specifying a first treatment protocol, thereby creating a device-ready first treatment protocol; sending a device-ready first therapy regimen to the portable neurostimulation device; receiving first treatment protocol data for the patient from the portable neurostimulation device; and generating a first therapy plan data set for the patient based on the first therapy plan data.)

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

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

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

creating, by a computing device, an electronic record of a patient, the electronic record pairing portable neurostimulation device identification information with patient identification information;

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

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

sending, by the computing device, a device-ready first therapy regimen to the portable neurostimulation device;

receiving, by a computing device, first therapy regime data for a patient from a portable neurostimulation device; and

generating, by the computing device, a first therapy regimen dataset for the patient based on the first therapy regimen data.

2. The method of claim 1, further comprising: the first therapy protocol data set is displayed by the computing device for the healthcare professional.

3. The method of claim 1, further comprising: the first therapy regimen data set is transmitted by the computing device to a remote server via an electronic communications network.

4. The method of claim 3, further comprising: the first treatment protocol data set is stored by the remote server in a database in electronic communication with the remote server.

5. The method of claim 1, further comprising:

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

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

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

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

generating, by the computing device, a second therapy regimen dataset for the patient based on the second therapy regimen data.

6. The method of claim 5, further comprising: displaying, by the computing device, the second treatment plan dataset for the healthcare professional.

7. The method of claim 5, further comprising: the second therapy regimen data set is transmitted by the computing device to the remote server via the electronic communication medium.

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

9. The method of claim 1, wherein the patient identification information includes the patient's full name, date of birth, time zone, and patient reference code.

10. The method of claim 1, wherein the input specifying the first treatment protocol comprises: (i) a number of therapy sessions, (ii) a type of periodic activity for each therapy session, and (iii) a type of device feedback for signaling the end of each therapy session.

11. The method of claim 1, wherein the input specifying the first treatment protocol comprises an expiration date.

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

13. The method of claim 1, further comprising: generating, by the computing device, at least one of a summary page, an activity page, and a report of the treatment plan based on the first treatment plan dataset.

14. The method of claim 1, wherein the first device-ready therapy protocol comprises one or more commands, each command comprising a plurality of ASCII characters representing a payload start, a payload end, a checksum, and a message end.

15. The method of claim 1, wherein the patient's first treatment plan data comprises one or more responses, each response comprising a plurality of ASCII characters representing a payload start, a payload end, a checksum, and a message end.

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

receiving portable neurostimulation device identification information from a portable neurostimulation device having a controller;

receiving patient identification information for a patient from a healthcare professional;

creating an electronic record of the patient, the electronic record pairing portable neurostimulation device identification information with patient identification information;

receiving, via a healthcare professional, a first input specifying a first treatment protocol for a patient;

processing a first input specifying a first treatment protocol, thereby creating a device-ready first treatment protocol;

sending a device-ready first therapy regimen to the portable neurostimulation device;

receiving first treatment protocol data for the patient from the portable neurostimulation device; and

a first therapy regimen data set for the patient is generated based on the first therapy regimen data.

17. The system according to claim 16, further comprising a portable neurostimulation device having a controller and an interface for electronic communication with a computing device.

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

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

20. A computerized method of determining 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 the patient during the time interval, the acceleration data reflecting a set of acceleration values;

parsing, by a computing system, the acceleration data into subsets corresponding to a plurality of discrete time periods within the time interval;

determining, by the computing system, for each of the one or more threshold amplitudes, a number of discrete time periods as follows: for the discrete period, any acceleration value within the discrete period exceeds a threshold amplitude;

determining, by the computing system, for each of the one or more threshold amplitudes, a subset of the number of discrete time periods for which an immediately preceding discrete time period also includes an acceleration value that exceeds the threshold amplitude;

calculating, by the computing system, for each of the one or more threshold amplitudes, an activity score by dividing the number of discrete time periods by the number of discrete time periods;

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

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

22. The method of claim 20, wherein classifying the patient's activity during the time interval comprises determining the activity as follows from the activity score within the time interval: (i) a first activity score greater than or equal to 0.9 indicates walking; (ii) a second activity score greater than or equal to 0.85 indicates balance; (iii) a third activity score greater than or equal to 0.8 indicates respiration; and (iv) a third activity score less than 0.8 indicates no activity.

23. The method of claim 20, further comprising: scores of patient activity comprising walking, breathing and awareness training, balance, and inactivity are displayed by the computing device.

24. The method of claim 23, wherein balance and respiration and awareness training are included in the same score.

25. The method of claim 22, wherein the threshold magnitude of acceleration is 0.1g, 0.01g, and 0.005 g.

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

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

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

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

Technical Field

The present application relates generally to systems, methods, and apparatus, including computer programs, for enhancing neurological rehabilitation. More particularly, the present application relates to software tools for processing information of a patient undergoing neurorehabilitation therapy.

Background

Neurorehabilitation is an emerging field in medical science, where patients suffering from damage or injury to all or part of their Central Nervous System (CNS) are treated to rehabilitate neural pathways, and/or new neural pathways are established to at least partially compensate for the damage/injury. Examples of neurological rehabilitation devices are described in previously issued patents, such as 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 entirety.

Neurological rehabilitation is typically achieved by non-invasive methods, such as physical therapy, occupational therapy, or speech therapy, which involve the use of exercise to attempt to increase the patient's ability. For example, a person suffering from spinal cord injury may exercise the affected areas of the body to increase coordination and range of movement. However, these methods suffer from the following disadvantages: time consuming, difficult and exhausting the patient. Invasive methods also exist, such as electrical stimulation, in which electrodes are implanted to deliver electricity at or near neural pathways to enhance neural function, and/or to combat errant neural function. For example, Deep Brain Stimulation (DBS) may be used to treat parkinson's disease and depression, Left Vagal Nerve Stimulation (LVNS) may be used to treat epilepsy, or a subdural implantable stimulator may be used to assist stroke recovery. These invasive methods are risky and expensive and are therefore generally used as a last resort (last report) when all other therapeutic interventions have failed.

While non-invasive or minimally invasive methods and devices for neurorehabilitation are available, it would be beneficial to have a more sophisticated tool (e.g., software) for tracking and analyzing the progress of a particular patient during neurorehabilitation therapy to better assist in the neurorehabilitation therapy.

Disclosure of Invention

Accordingly, the present invention provides a novel framework, including a computing system and associated computing methods and modules, for storing, processing, transmitting, analyzing, and displaying information collected from patients undergoing neurorehabilitation therapy. A portable neurostimulation device delivered to a patient may 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 may install a Data Management Application (DMA) on his or her personal computer for receiving, processing, analyzing and/or displaying the data on the personal computer, and/or uploading the data to a remote server installed with a corresponding DMA cloud service. The DMA cloud service may receive data for patients of one or more HCPs and store some or all of the patient data in a remote DMA database. The systems and methods described herein may also assist in monitoring a patient's specific activity during a therapy session, for example, 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 the therapy session.

In one aspect, the invention features a computerized method of processing data. The computerized method comprises: portable neurostimulation device identification information is received by a computing device from a portable neurostimulation device having a controller. The computerized method further comprises: patient identification information for a patient is received by a computing device from a healthcare professional. The computerized method further comprises: creating, by the computing device, an electronic record of the patient that pairs the portable neurostimulation device identification information with the patient identification information. The computerized method further comprises: a first input specifying a first treatment regimen for a patient is received by a computing device via a healthcare professional. The computerized method further comprises: processing, by the computing device, a first input specifying a first treatment protocol, thereby creating a device-ready (device-ready) first treatment protocol. The computerized method further comprises: sending, by the computing device, the device-ready first therapy regimen to the portable nerve stimulation device. The computerized method further comprises: first therapy regimen data for a patient is received by a computing device from a portable neurostimulation device. The computerized method further comprises: generating, by the computing device, a first therapy regimen dataset for the patient based on the first therapy regimen data.

In some embodiments, the method comprises: the first therapy protocol data set is displayed by the computing device for the healthcare professional. In some embodiments, the method comprises: the first therapy regimen data set is transmitted by the computing device to a remote server via an electronic communications network. In some embodiments, the method comprises: the first treatment protocol data set is stored by the remote server in a database in electronic communication with the remote server. In some embodiments, the method comprises: input specifying a second treatment regimen for the patient is received by the computing device via the healthcare professional. In some embodiments, the method comprises: processing, by the computing device, the input specifying the second treatment protocol to create a device-ready second treatment protocol. In some embodiments, the method comprises: sending, by the computing device, the device-ready second therapy regimen to the portable nerve stimulation device. In some embodiments, the method comprises: second treatment protocol data for the patient is received by the computing device from the portable neurostimulation device. In some embodiments, the method comprises: generating, by the computing device, a second therapy regimen dataset for the patient based on the second therapy regimen data.

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

In another aspect, the invention features a computerized system including a computing device for processing patient data. The computing device is configured to: portable neurostimulation device identification information is received from a portable neurostimulation device having a controller. The computing device is further configured to: patient identification information for a patient is received from a healthcare professional. The computing device is further configured to: an electronic record of the patient is created that pairs portable neurostimulation device identification information with patient identification information. The computing device is further configured to: a first input specifying a first treatment protocol for a patient is received via a healthcare professional. The computing device is further configured to: a first input specifying a first treatment protocol is processed to create a device-ready first treatment protocol. The computing device is further configured to: the device-ready first therapy regimen is sent to the portable neurostimulation device. The computing device is further configured to: first treatment protocol data for a patient is received from a portable neurostimulation device. The computing device is further configured to: a first therapy regimen data set for the patient is generated based on the first therapy regimen data.

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

In another aspect, the invention features a computerized method of determining patient activity during a time interval. The method comprises the following steps: receiving, by a computing system from a portable neurostimulation device having an accelerometer, acceleration data measured for the time interval corresponding to movement of the patient during the time interval, the acceleration data reflecting a set of acceleration values. The method further comprises the following steps: parsing, by a computing system, the acceleration data into subsets corresponding to a plurality of discrete time periods within the time interval. The method also includes determining, by the computing system, for each of the one or more threshold amplitudes, a number of discrete time periods as follows: for the discrete period, any acceleration value within the discrete period exceeds a threshold magnitude. The method further comprises the following steps: determining, by the computing system, for each of the one or more threshold amplitudes, a subset of the number of discrete time periods for which an immediately preceding discrete time period also includes an acceleration value that exceeds the threshold amplitude. The method further comprises the following steps: calculating, by the computing system, an activity score for each of the one or more threshold amplitudes by dividing the number of discrete time period subsets by the number of discrete time periods. The method further comprises the following steps: classifying, by the computing system, activity of the patient during the time interval based on at least one of the activity scores.

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 the activity score and/or the number of discrete periods, and/or provides the activity score and/or the number of discrete periods to the healthcare professional computing device. In some embodiments, the healthcare computing device classifies patient activity based on the activity score and the number of discrete periods. In some embodiments, classifying the patient's activity during the time interval comprises determining the activity as follows from the activity score within the time interval: (i) a first activity score greater than or equal to 0.9 indicates walking; (ii) a second activity score greater than or equal to 0.85 indicates balance; (iii) a third activity score greater than or equal to 0.8 indicates respiration; and (iv) a third activity score less than 0.8 indicates no activity. In some embodiments, other numerical thresholds are used to isolate and/or identify these activities and/or other activities, or combinations of activities.

In some embodiments, the method comprises: scores of patient activity comprising walking, breathing, and awareness training, balance, and/or inactivity are displayed by the computing device. In some embodiments, balance and respiration and awareness training are included in the same score (e.g., added, packaged, and/or displayed together). In some embodiments, the threshold magnitude of acceleration is 0.1g, 0.01g, and 0.005 g. In some embodiments, the sampling rate of the accelerometer is at least 50 Hz. In some embodiments, the length of each discrete period is 1 second. In some embodiments, the length of the time interval is at least 120 seconds. In some embodiments, the method comprises: generating, by the computing device, a report summarizing the patient activity determined for the time interval.

Drawings

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

Fig. 1A is a high-level schematic diagram of a system for assisting neurological rehabilitation of a patient according to an illustrative embodiment of the invention.

FIG. 1B is a schematic diagram of a system for assisting neurological rehabilitation of a patient according to an illustrative embodiment of the invention.

Fig. 2A is a schematic diagram of a computer architecture implementing a data management application for processing neuro-rehabilitation patient information, according to an illustrative embodiment of the invention.

Fig. 2B is a schematic diagram of a computer architecture of a DMA cloud service for processing neuro-rehabilitation patient information, according to an illustrative embodiment of the invention.

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.

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

Fig. 5 is a sequence diagram of computerized steps that occur when a HCP is set up for a protocol implemented by a portable neurostimulation device, according to an illustrative embodiment of the invention.

6A-6N show various illustrations of screen shots of DMAs used by a HCP in accordance with an illustrative embodiment of the invention.

7A-7G show a number of illustrations of screen shots of a backend cloud interface configured for use by an administrator in accordance with an illustrative embodiment of the present invention.

Fig. 8 shows a flow diagram of an activity monitoring algorithm for determining patient activity during a therapy session in accordance with an illustrative embodiment of the invention.

Fig. 9A and 9B show exemplary graphs of acceleration versus time measured by an accelerometer of a portable neurostimulation device of a patient according to an illustrative embodiment of the invention.

Fig. 10 shows a flowchart showing mathematical details of an activity monitoring algorithm for determining patient activity during a therapy session, according to an illustrative embodiment of the invention.

FIG. 11 is a method flow diagram of a method of processing data in accordance with an illustrative embodiment of the present invention.

FIG. 12 is a method flowchart of a computerized method of determining patient activity for a time interval in accordance with an illustrative embodiment of the invention.

Detailed Description

Fig. 1A is a high-level schematic diagram of a system 100 for assisting neurological rehabilitation of a patient, according to an illustrative embodiment of the invention. The system 100 includes a portable neurostimulation device 102 having a controller, a 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 DMA cloud services may be collectively referred to as DMA software 110. The portable neurostimulation device 102 may be a device with PoNS provided by Helius Medical, Inc^®PoNS of controller^®An apparatus. The HCP computing device 104 may be a personal computer or tablet computer in a therapy clinic. The remote server computing device 106 and the remote DMA database 108 may be hosted by a third party cloud service, such as Amazon Web Services (AWS).

The portable neurostimulation device 102 provides electrical stimulation to the patient's tongue (e.g., under the patient's command) according to stimulation parameters (e.g., duration) predefined by the HCP. The portable neurostimulation device 102 may be used in conjunction with one or more physical therapy exercises, for example, with the goal of improving balance and/or gait disturbances caused by Traumatic Brain Injury (TBI), or enhancing neurological rehabilitation of a patient for various other purposes. The portable neurostimulation device 102 may be electronically connected to the HCP computing device 104, for example, by using a standard USB device interface. The HCP computing device may host a DMA that includes software used by the HCP for various purposes, such as (i) to record the number of expected daily activity sessions that the patient is expected to perform, (ii) to view usage data of the patient after a treatment period, and (iii) to configure the neurostimulation device 102 via its controller (e.g., with one or more personalized therapy protocols).

The HCP computing device 104 is in electronic communication with a remote server computing device 106, e.g., via the internet. The remote server computing device 106 hosts a DMA cloud service that can receive data from the DMA, categorize and analyze patient data, and send the data via electronic communication to the DMA database 108, e.g., for storage and retrieval. Multiple HCP computing devices may be connected to the DMA cloud service, and data for multiple patients (including protected health information or "PHI") may be stored in the DMA database 108. In some embodiments, the DMA software is not intended for use by the patient; it is a tool for HCPs to access and use only after receiving appropriate training regarding their functionality. The HCP user must log in using unique account information (e.g., username and password) to access the DMA.

Fig. 1B is a schematic diagram of a system 120 for assisting neurological rehabilitation of a patient, according to an illustrative embodiment of the invention. The system 120 includes the components shown in fig. 1A, but schematically illustrates the portable nerve stimulation device 102 in more detail. In particular, the portable neurostimulation device 102 includes a controller 122 and a mouthpiece (mouthpiece) 124 in electrical communication with the controller 122. The controller 122 includes a main user interface and drive electronics. Stimulation can be started and stopped through a main user interface; the stimulation intensity can be adjusted; and the system can be powered on and off. The primary user interface includes a visual display, an audible feedback, and a vibratory feedback. The mouthpiece 124 includes a cable connecting it to the controller 122. The mouthpiece 124 is placed in the mouth of the patient and houses electrodes that contact the patient's tongue to deliver stimulation current to the patient.

The portable neurostimulation device 102 also includes a mouthpiece holder housing 126, a carrying housing 128, and a charger 130. The mouthpiece holder housing 126 provides a sanitary and durable protective housing for the mouthpiece. The carrying 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 mains power adapter that connects to the controller 122, for example via a standard USB connector, and provides a low voltage power supply to recharge the controller battery. When the charger 130 is connected to the controller 122, the stimulation may be disabled.

The system 100 shown and described above in fig. 1A-1B can deliver electrical neuromodulation waveforms to specific regions of a patient's tongue. The waveforms may be used in conjunction with a particular physiotherapy regimen, which together may comprise a treatment regimen. The treatment regimen may be used, for example, in patients with chronic balance and/or gait disorders caused by Traumatic Brain Injury (TBI). The HCP may use the DMA to configure the controller 122 with a patient-specific treatment protocol and its treatment phase. The portable neurostimulation device 102 may then be used by the patient, potentially several times per day, over a period of several days to meet the prescribed therapy. Treatment may be administered by the patient himself during the period, or may be supervised by the HCP. After the treatment, the HCP may download the results and alarms from the controller 122 to the HCP computing device 104 using DMA and view the results and alarms.

Fig. 2A is a schematic diagram of a computer architecture 200 implementing a Data Management Application (DMA) for processing neuro-rehabilitation patient information, according to an illustrative embodiment of the invention. As described above, DMA software can be divided into two parts: (i) desktop-based DMA (client application, as shown) and cloud-based DMA cloud services (DMA server, as shown). The HCP may use the client application to (i) slave to the connected PoNS^®Retrieving and viewing patient activity link data in the controller; (ii) at the connected PoNS^®Setting a new patient prescription on the controller; and/or (iii) create a patient usage summary PDF report. The client application may send its patient activity link data to the DMA server. The cloud service may be responsible for: (i) storage PoNS^®Patient activity link data; (ii) retrieving PoNS^®Patient activity session data for display by a desktop application; (iii) storing a patient prescription history; and/or (iv) contacting the patient with PoNS^®The controllers are associated. In addition to these responsibilities, the DMA software may also include access control, auditing, and user account management responsibilities (e.g., as described in more detail herein)That is).

Fig. 2B is a schematic diagram of a computer architecture 250 for a DMA cloud service for processing neuro-rehabilitation patient information, according to an illustrative embodiment of the invention. The DMA cloud service may interact with a database for storing patient data. Fig. 2B illustrates how these components can be deployed multiple times for redundancy across the cloud infrastructure provided by the AWS, and can be supported by auxiliary internal services and services provided by the AWS. The cloud service components may include: (i) a DMA HTTP web service (shown as "DMA") that responds to incoming requests from DMA clients using data stored in a database; (ii) AWS relational database services (shown as "RDS Master" and "RDS Standby"), which store all patient, device, and practitioner data; (iii) an AWS CloudWatch log service (shown as AWS CloudWatch) that encrypts and stores web service applications and audits log streams; (iv) an AWS resilient load balancer (shown as a "load balancer") that receives incoming encrypted client requests as holders of web domain certificates of the cloud and shares the requests among multiple DMA HTTP web services; and (v) AWS simple email service (shown as "SES password reset") for sending password reset and account setup emails to users.

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 desktop application of the client application requests data from the device to verify that it is suitable for use with the current DMA version before sending a notification to the web application. The process continues by checking the device status in the cloud, for example by asking if it is registered on the system, and if it has an assigned patient. Once the process is complete, the web application is notified of the complete status of the device and the user is presented with a list of options to continue.

Fig. 4 is a sequence diagram 400 of computerized steps that occur when a HCP uploads patient data to the cloud platform, according to an illustrative embodiment of the invention. If a device with active records is connected, the HCP may be prompted to upload those records to the cloud platform. The figure shows the data flow once the user has started the upload process. The desktop app then sends all records to the cloud platform in a batch operation. Once the cloud platform has received the data, it begins the process of verifying the received data. Once completed, it updates the database with the correct record and sends a response detailing the result. The user is then notified of the results and the record is removed from the device.

Fig. 5 is a sequence diagram 500 of computerized steps that occur when a HCP is set up for a protocol implemented by a portable neurostimulation device, according to an illustrative embodiment of the invention. Setting up a scenario begins with a healthcare professional creating a scenario in a web application. When they choose to continue, the scheme is sent to the cloud where it is authenticated and returned. The web application then requests the server time, which is necessary 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.

6A-6N show various illustrations of screen shots of DMAs used by a HCP in accordance with an illustrative embodiment of the invention. Fig. 6A shows a primary account creation screen 604. The HCP or user will receive the "Create Account" e-mail, including the initiation of downloading the DMA software application (e.g., PoNS)^®Software). Once the download has been completed, the HCP opens the downloaded file and follows the instructions in the setup wizard to complete the setup. PoNS^®The software should be automatically started after it has been installed. (if PoNS)^®Without automatic initiation of the software, the HCP may click on PoNS on his or her desktop^®A software icon to launch the application. ) Once PoNS^®The software opens and the HCP clicks on the second link in the "create account" email and follows the instructions on the 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.

FIG. 6B illustrates account loginAnd a screen 608. The HCP or user clicks PoNS on his or her desktop^®Software icon to launch PoNS^®Software to ensure that s/he has a working internet connection before attempting to log 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 encountered by the user, screen 612 being the use of the provided USB cable to connect a new PoNS^®The controller is connected to the computer for prompting. After the user connects a new PoNS^®After the controller, PoNS^®The software will automatically detect that a new controller has been connected to the computer.

FIG. 6D shows the next screen 616 encountered by the user, screen 616 including selections of two options for proceeding corresponding 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 screen sequence in the case of selection of option 1; fig. 6M-6N show the sequence of screens encountered in the case of selection of option 2. Fig. 6E shows the next screen 620 encountered in the case where option (1) is selected, and screen 620 is a screen for adding new patient information. The screen 620 may 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 may be a unique code assigned to each patient. In some embodiments, it may be helpful to use the same reference used in the HCP patient record (as this would be at PoNS)^®Shown in a report generated by the software). Having entered this information, the user can click "ADD" to continue.

Fig. 6F shows the next screen 624 seen by the HCP, which screen 624 prompts the HCP to create a treatment protocol. A daily treatment regimen may be created for a patient by performing the following steps. First, the HCP selects one or more daily activity sessions to be completed by the patient each day. In some embodiments, the patient may perform up to a total of 10 activities per day, divided into the following activities: gait, balance, and Breathing and Awareness Training (BAT). In some embodiments, each link lasts for up to 20 minutes. Second, the user selects the type of device feedback that the patient will receive to signal the end of the session. In some embodiments, this is an audible beep (beep) and/or vibration. Third, the user selects the appropriate device language to be displayed on the patient device. When this information has been entered, the user clicks "SET DAILY REGIMEN (set daily regimen)" to continue.

Fig. 6G shows the next screen 628 that the HCP sees, screen 628 prompting 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 desired, the user may EXTEND the DEVICE EXPIRATION date by clicking "EXTEND DEVICE EXPIRATION". The user then disconnects the device by removing the USB cable from the computer. PoNS^®The software will confirm that the device has been disconnected. Once the device has been disconnected, it is ready for use by the patient during his or her course of treatment. The controller records patient versus PoNS^®Such as the number of links attempted and the duration of each link.

The next time the device is connected to the user's computer, PoNS^®The software may display a screen that prompts the user to log into his or her account and connect to the patient's device, similar to that described above. After the device is connected, PoNS^®The software may display a screen for confirming the date of birth of the patient of the device, as shown by screen 632 in fig. 6H. After confirming the patient's birth date, PoNS^®The software will display the screen 636 shown in fig. 6I, the screen 636 showing the number of activity links stored on the controller. The screen 636 also includes a button "UPLOAD DATA" for the user to click to begin retrieving the ring DATA from the patient's controller. The controller should not be turned off when uploading data. When the upload has been confirmed, a screen 640 as shown in fig. 6J will be displayed. Screen 640 has a click "VIE" for the userW SESSION DATA) "to review patient link DATA prompts.

Fig. 6K shows a screen 644 of a sample summary page with patient data. The summary page provides an overview of the patient link data. In some embodiments, it is divided into the following parts: (i) show (watching); (ii) daily Regimen (Daily Regimen); (iii) attempted links (Attempted Sessions); (iv) a Sessions by duration (session by duration); (v) activities (Activities) (Gait), BAT and Balance (BAT & Balance)); and/or (vi) links over time (Sessions over time). "watching" shows the time period for which the summary applies. This section may show up-to-date data, for example, up to the most recent treatment regimen change, or a maximum of up to 98 days. In some embodiments, the patient is expected to execute their daily regimen six days per week. "Daily Regimen" shows the activities that constitute the patient's current Daily treatment Regimen. "Attempted Sessions" shows the percentage of links Attempted over the number of expected links. "Sessions by duration" displays the detail (creatdown) of the recorded link based on the duration of each link executed. In some embodiments, links are ranked as follows: good (17-20 min) (Good (17-20 minutes)); normal (14-17 min) (OK (14-17 minutes)); poor (1-14 minutes) (Poor (1-14 minutes)). The activities (gait, BAT and balance) show the percentage of the number of attempted links to the number of expected links, separated by activity type. The section also shows the percentage of Good, OK and Poor links. "Sessions over time" shows which activity links occur each day, they are ranked by duration: good, OK, and Poor. In some embodiments, each column represents a single day, with each link represented by a block. In some embodiments, the HCP may export detailed reports in various formats (e.g., Microsoft Word or Excel or Adobe PDF), and/or may generate historical reports in similar formats.

Fig. 6L shows a screen 648 of a patient activity page. To navigate to the active page, the user may click "GAIT (GAIT)" or "BAT & BALANCE (BAT and BALANCE)" in the navigation bar. These pages provide an overview of the patient's activities for a particular activity type. The activity page may be divided into the following sections: (i) attempted links (Attempted Sessions); (ii) a Sessions by duration (session by duration); (iii) links over time (Sessions over time); (iv) links by day (Sessions by day); (v) links by hour (Sessions by hour); and/or (vi) Create a report (Create report). The "Attempted Sessions" section shows the percentage of links Attempted over the number of expected links. "Sessions by duration" displays a detailed list of recorded links based on the duration of each link executed. In some embodiments, links are ranked as follows: good (17-20 min) (Good (17-20 minutes)); normal (14-17 min) (OK (14-17 minutes)); poor (1-14 minutes) (Poor (1-14 minutes)). "Sessions over time" shows which activity links occur each day, they are ranked by duration: good, OK, and Poor. Each column represents a single day, with each link represented by a block. "Sessions by day" displays the recorded links based on the day the link was recorded. "Sessions by hour" displays a link recorded based on a 2-hour block in which the link is recorded. These are adjusted to the patient's time zone setting. The "Create report" enables the HCP to Create reports, change the patient's daily regimen, or extend device expiration. The HCP may click on "REPORTS" and follow instructions on the screen to save a copy of the patient summary, for example as a PDF document.

The screen 644 in fig. 6K also corresponds to the next screen in the sequence that enables the user to set the next step. In screen 644, the user may set a new daily treatment plan, for example by clicking "CHANGE REGIMEN (Change plan)" to change the patient's daily treatment plan. This opens a "Create register" page in which the patient's daily regimen may be modified. The user may also extend the device expiration on this page. When the user has finished, s/he can disconnect the controller by removing the USB cable from the computer.

Fig. 6M shows a screen 652 corresponding to selection of option (2) at the screen shown in fig. 6D above. In This case, the user selects "This is a return patient with a replacement device" to continue and is taken to screen 656 of FIG. 6N, which is a screen that permits the user to link an existing patient to a new controller. The user may SEARCH for an existing patient by entering the patient's first name, last name, and date of birth, and then clicking "SEARCH". The search results will be displayed and the user can click on the "LINK" button next to the correct patient in the list. This opens the CreatedToolProvisioning Page, for example as shown above.

7A-7G show a number of illustrations of screen shots of a backend cloud interface configured for use by an administrator in accordance with an illustrative embodiment of the present invention. Administrators (or "super-users") of the backend cloud interface may access (e.g., via the internet using a web interface) multiple administrator functions that support PoNS used by clinical end-users^®A software application. Such functions may include: (1) by completing the training at the user or no longer using PoNS for his patient^®Adding and/or removing users to manage a user list during treatment; (2) add a field for "clinic name" that allows monitoring device usage and/or patient compliance on a clinic-by-clinic basis (e.g., to inform of best practices); and/or (3) allow the controller to be disabled if misuse is suspected.

Fig. 7A shows a login screen 704 for a back-end administrator, the screen 704 including an email (i.e., username) and password fields, along with a forgotten password recovery link and a "Log in" button. FIG. 7B shows a super user home screen 708, the home screen 708 including links to: "View Users" (e.g., to manage a user database by adding, editing, disabling, or deleting Users); "View Controllers" (e.g., to search and/or manage Controllers); "View Organizations" (e.g., to manage an organization database); and "View Account" (e.g., to search and manage a patient database). FIG. 7C shows the user search screen 712 seen by the administrator when the "Views Users" tab on the previous home screen 708 was clicked on along with the user list. (in this view, there are no users, and thus the option of "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, screen 712 shows 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 the first name. The screen 716 can be exited by selecting "Add other user" or simply selecting "Close". FIG. 7E illustrates a populate user database screen 720 with a plurality of placeholder user records. In this View, several functionalities are shown for the user highlighted third from the top of the list (View account, Quick edit, Reset password, and Disable account). FIG. 7F illustrates an account setup screen 724, which screen 724 provides the administrator with the ability to disable a particular account, such as by clicking on the "Enabled" or "Disabled" radio buttons shown. Fig. 7G illustrates a personal information screen 728, which screen 728 displays the user's personal information (e.g., first name, last name, email, and organization) and provides the ability to edit the user's personal information.

Fig. 8 shows a flow chart 800 of an activity monitoring algorithm for determining the activity of a patient during a therapy session in accordance with an illustrative embodiment of the invention. The algorithm may function by: check to see if the accelerometer readings from the patient's portable neurostimulation device exceed a set of thresholds during a period of specified length (e.g., a one second period) and then check to see if the periods with acceleration measurements exceeding a given threshold are adjacent. The data may then be sorted by considering the fraction of time periods above or below the second set of thresholds associated with each acceleration threshold. In this way, both the amplitude and regularity of the activity may be used to classify the activity into different classifications (e.g., walking, balance, breathing and awareness, or no activity). In some embodiments, the algorithm may optimally classify data using a data set that is at least 120 seconds long. Thus, a 20 minute complete therapy session may be classified as a single data set, or into multiple (e.g., 10) sub-fractions of 120 seconds each, which may be separately classified. The latter method may be used, for example, to determine whether a patient is resting within a portion of a gait cycle. In some embodiments, the accelerometer data is passed through a low frequency filter before being analyzed. High pass filtering may be used to remove long term trends mainly due to gravity and/or orientation of the device. In some embodiments, the accelerometer provides a high pass filter that can be configured by the firmware of the device to have a cutoff frequency of 0.5 HZ, which is considered reasonable because the frequency of footsteps is around 0.5-1.0HZ and other activities are likely to have a longer characteristic time scale.

Fig. 9A and 9B show exemplary graphs (900, 950, respectively) of acceleration measured by an accelerometer of a portable neurostimulation device of a patient versus time, according to an illustrative embodiment of the invention. The exemplary data measured in graphs 900, 950 helps illustrate one embodiment of the application of an activity monitoring algorithm, such as applied to data taken over a 24 second period, in accordance with the principles of the present invention. In these graphs, different T_i（T₁、T₂And T₃) Thresholds (one positive and one negative for each threshold) representing the magnitude of the acceleration. In this example, T₁、T₂And T₃The specific amplitudes of (a) correspond to 0.1g, 0.01g and 0.005g, respectively. "T_iIn total "means highThe total number of time periods at each of the three thresholds (e.g., as applied to the example data, T)₁In total of 2, T₂A total of 22, and T₃The total is 24). A. the_iRepresenting the total number of time periods both above the threshold and immediately preceding the time period that is also above the threshold (e.g., as applied to the example data, a₁A total of 0, A₂A total of 20, and A₃The total is 24). Let NT be the total number of time periods (here 24), and let F_iIs an activity score for each threshold and NT, defined as F_iand/NT. In this case, the "activity score" for each threshold is calculated as follows:

F₁ = A₁ / NT = 0 / 24 = 0.000

F₂ = A₂ / NT = 20 / 24 = 0.833

F₃ = A₃ / NT = 24 / 24 = 1.000

next, each F_iCorresponding to it "activity score threshold" P_iThe comparison is performed until a classification is obtained. FIG. 10 shows a flowchart 1000 showing mathematical details of this portion of the activity monitoring algorithm, according to an illustrative embodiment of the invention. In this example, P₁Is set to 0.900, P₂Is set to 0.850, and P₃Is set to 0.800. Thus, the computing device on which the algorithm is executed first determines F₁(0.000) whether or not P is greater than or equal to₁(0.9). Where it is not greater than or equal to P₃And therefore the algorithm proceeds to the next step. (Note that if it is greater than or equal to P₃Then the classification of "walk" would be determined, with no further comparison required). Make F₂(0.833) whether or not P is greater than or equal to₂(0.850) next determination. Where it is again not greater than or equal to P₂And therefore the algorithm proceeds to the next step. (Note that if it is greater than or equal to P₂A "balanced" classification will be determined, where no further comparison is required). Make F₃(1.000) whether or not P is greater than or equal to₃(0.800) next determination. ByWhen it is greater than or equal to P₃Thus, the activity may be classified as "breathing and consciousness" where no further comparison is required. (Note that if it is not greater than or equal to P₃A classification of "no activity" will be determined, where no further comparison is needed). In some embodiments, the threshold value that is subsequently applied may be utilized to "count" directly on the firmware. In some embodiments, PoNS^®Software slave PONS^®The device receives four digits: three activity scores A₁、A₂、A₃And NT-the total number of 1 second periods. In some embodiments, PoNS^®The software has stored T₁、T₂And T₃. In some embodiments, for each activity, T₁、T₂And T₃Are the same.

The accelerometer may be used with a 100 Hz sampling rate setting, although analysis of the test data shows that the accelerometer can operate at a lower sampling rate (e.g., 50 Hz) without significantly affecting accuracy, while reducing power consumption. In one exemplary non-limiting test set of 63 data sets, each inactive data set was correctly identified as inactive using the algorithm described above (48 data sets in total), and 14 of the 15 data sets were also correctly classified using the algorithm described above (the exception relates to the set associated with "apparent swing" activity, which is assumed to attempt to simulate patients with TBI balance). Thus, a total of 62 of the 63 data sets are correctly identified, or about 98%. Furthermore, it is not surprising that "significant sway" activity is unlikely to be a good approximation for patients with poor balance, and therefore it is incorrectly identified during testing.

FIG. 11 is a method flowchart 1100 of a method of processing data in accordance with an illustrative embodiment of the invention. In a first step 1102, a computing device receives portable neurostimulation device identification information from a portable neurostimulation device having a controller. In a second step 1104, the computing device receives patient identification information for the patient from a healthcare professional. In a third step 1106, the computing device creates an electronic record of the patient that pairs the portable neurostimulation device identification information with the patient identification information. In a fourth step 1108, the computing device receives, via the healthcare professional, a first input specifying a first treatment regimen for the patient. In a fifth step 1110, the computing device processes a first input specifying a first treatment protocol, thereby creating a device-ready first treatment protocol. In a sixth step 1112, the computing device sends the device-ready first therapy regime to the portable nerve stimulation device. In a seventh step 1114, the computing device receives first treatment protocol data for the patient from the portable neurostimulation device. In an eighth step 1116, the computing device generates a first therapy regimen data set for the patient based on the first therapy regimen data.

Fig. 12 is a method flowchart 1200 of a computerized method of determining patient activity for a time interval in accordance with an illustrative embodiment of the invention. In a first step 1202, a computing system receives acceleration data corresponding to movement of a patient during a time interval measured for the time interval from a portable neurostimulation device having an accelerometer, 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 a plurality of discrete time periods within the time interval. In a third step 1206, the computing system determines a number of discrete time periods for each of the one or more threshold amplitudes as follows: for the discrete period, any acceleration value within the discrete period exceeds a threshold magnitude. In a fourth step 1208, the computing system determines, for each of the one or more threshold amplitudes, a subset of the number of discrete time periods for which an immediately preceding discrete time period also includes an acceleration value that exceeds the threshold amplitude. In a fifth step 1210, the computing system calculates an activity score for each of the one or more threshold amplitudes by dividing the number of discrete time periods by the number of discrete time periods. In a sixth step 1212, the computing system classifies the patient's activity during the time interval based on at least one of the activity scores.

The techniques described above may be implemented in digital and/or analog electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. The implementations may be implemented 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, data processing apparatus (e.g., a programmable processor, a computer, and/or multiple computers). The computer programs may be deployed in a cloud computing environment (e.g., Amazon AWS, Microsoft Aztre, etc.). Method steps may 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.

To provide for interaction with a user, the techniques described above may 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) through which the user may provide input to the computer (e.g., interact with user interface elements). Other kinds of devices may also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including audible, speech, and/or tactile input.

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

Components of the computing system may be interconnected by a transmission medium that may include any form or medium of digital or analog data communication (e.g., a communication network). The transmission medium may include one or more packet-based networks and/or one or more circuit-based networks in any configuration. Packet-based networks may 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 Communication (NFC) network, Wi-Fi, WiMAX, General Packet Radio Service (GPRS) network, HiperLAN), and/or other packet-based network. The circuit-based network may include, for example: a Public Switched Telephone Network (PSTN), a conventional 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 network.

The transfer of information over the transmission medium may be based on one or more communication protocols. The communication protocols may include, for example, ethernet protocols, Internet Protocols (IP), Voice Over IP (VOIP), peer-to-peer (P2P) protocols, hypertext transfer protocols (HTTP), Session Initiation Protocols (SIP), h.323, Media Gateway Control Protocols (MGCP), signaling system #7 (SS 7), global system for mobile communications (GSM) protocols, push-to-talk (PTT) protocols, PTT over cellular (poc) protocols, Universal Mobile Telecommunications System (UMTS), 3GPP Long Term Evolution (LTE), and/or other communication protocols.

Devices of a computing system may include, for example, computers with browser devices, telephones, IP phones, mobile computing devices (e.g., cellular phones, Personal Digital Assistant (PDA) devices, smart phones, tablets, laptops, email devices), and/or other communication devices. The browser devices include, for example, computers (e.g., desktop and/or laptop computers) having a web browser (e.g., Chrome @, Microsoft Internet Explorer available from Microsoft corporation, and/or Mozilla @ Firefox available from Mozilla). The mobile computing device comprises, for example, Black Berry @, from Research in Motion, iPhone @, from apple Inc., and/or an Android based device. The IP phones comprise, for example, Cisco ® Unifield IP Phone 7985G and/or Cisco @ Unifield Wireless Phone 7920 available from Cisco systems Inc.

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