WO2019207346A1

WO2019207346A1 - System and method for rehabilitation program management in post stroke subjects

Info

Publication number: WO2019207346A1
Application number: PCT/IB2018/052938
Authority: WO
Inventors: Jayavardhana Rama GUBBI LAKSHMINARASIMHA; Prabhakar ANNAVI
Original assignee: Neuroanalytics Pty Ltd
Priority date: 2018-04-27
Filing date: 2018-04-27
Publication date: 2019-10-31

Abstract

A system for implementing a rehabilitative physiotherapy program in post stroke period is provided. The system includes a wearable device worn by a subject, configured for monitoring motor activity of the subject. The system also includes a central server configured for storing a plurality of rehabilitative physiotherapy programs to be performed by the subject. The central server is also configured for receiving an instruction from at least one other user for enabling the rehabilitative physiotherapy program to the subject. The central server is configured for receiving motor activity data from the wearable device, after exercising the enabled rehabilitative physiotherapy program by the subject. The central server is configured for analyzing the motor activity data, and communicating a recovery status to the subject and the at least one other user periodically.

Description

SYSTEM AND METHOD FOR REHABILITATION PROGRAM MANAGEMENT IN

POST STROKE SUBJECTS

FIELD OF TECHNOLOGY

[001] This disclosure relates generally to system and method for monitoring a subject in post-acute stroke period, and more particularly to a system and method for managing rehabilitation program of a post stroke subject.

BACKGROUND

[002] Acute stroke affects more than of about 6.2 million patients per annum on a global scale. A significant proportion, approximately twenty five percent, of stroke patients demonstrates neurological (brain) deterioration either concurrently or independently of heart rhythm disturbances. The monitoring of patients body for brain function deterioration and heart rhythm disturbances requires frequent nursing observations and assessments. Furthermore, the monitoring of subject is not only necessary, when the subject is admitted in intensive care unit (ICU) or under observations of medical experts, but also very much necessary during postoperative days or when the patient is discharged from the hospital.

[003] The National Institute of Health (NIH) guidelines recommend various treatment procedures to the subject depending on the affected regions and severity of stroke. For e.g., eye movement therapy for visual field functions, electric simulation for limbs, ankle-foot orthosis for lower limbs etc. are recommended to the one or more patients. Physiotherapy is an established clinical practice for stroke patients, playing an important role in improving limb function. Typically, a physiotherapist or a medical expert studies the case history of the subject comprising of CT-scans, MRI scans, treatment course, physical examination reports etc to arrive at a physiotherapy regime for the subject.

[004] Physiotherapy is recommended to subjects during postoperative days to stimulate motor function and to prevent, identify, and rectify movement of various body parts. Cardiac rehabilitation also helps heart patients get back on their feet, physically and emotionally, through exercise, education, and support. After the initial treatment in ICU, stroke patients are often subjected to rehabilitative physiotherapy. The subject undergoes various types of procedures depending on the severity of stroke under the supervision of a physiotherapist or a rehabilitation specialist, in order to improve the motor functions. [005] Generally, severe damage to motor functions is observed in subjects who have had an acute stroke. For e.g., if both the upper limbs of the subject were affected, then a qualified physiotherapist may recommend exercises for the upper limb of varying intensities over a period of time. The physiotherapist then continuously monitors the subject to evaluate and assess the progress of the affected upper limbs. The body movement monitoring and the effects of the physiotherapy is necessary to check the pace of recovery of the subject.

[006] The rehabilitation program, e.g. target level of an exercise, cannot be adjusted until the patient visits the specialist. The duration may be too long to ensure compliance of the patient. The patient may become de-motivated to do the exercise especially in an unsupervised home rehabilitation program. Several approaches have been disclosed in the prior art to address the issue of effective remote physiotherapy and rehabilitation program management.

[007] Systems known in the art have generally provided for assistive devices to guide the subject while performing exercises. For example, EP3165208 discloses a robotic arm integrated with sensors to measure the intensity and accordingly assist the subject based on training information pre-stored in the device. WO2015142781 teaches a method for visual delivery of prehab and rehabilitation that can be created during interaction between the subject and the skilled provider, i.e. the physiotherapist. The visual instructions can be modified based on the feedback provided by the subject. Yet another prior art, US20130218053 teaches application of wearable technology in rehabilitation program monitoring and management.

[008] However, existing systems and methods do not teach implementation of rehabilitation programs to post stroke subjects remotely such that the programs are managed efficiently without much intervention. Further, the prior art does not teach monitoring and modifying rehabilitation programs based on standardization of activities performed by the subject. Moreover, the prior art does not teach compatibility of the system and methods used, with the various scales used for measuring the overall motor function improvement thus limiting the scalability.

SUMMARY

[009] In order to solve at least some of the above mentioned problems, there exists a need for a system and a method that monitors the improvement in the affected parts of the subject’s body due to the stroke based on the treatment of physiotherapy. [0010] This summary is provided to introduce a selection of concepts in simple manners that are further described in the detailed description of the disclosure. This summary is not intended to identify key or essential inventive concepts of the subject matter nor is it intended to determine the scope of the disclosure.

[0011] Briefly, according to an exemplary embodiment, a system for implementing a rehabilitative physiotherapy program for subjects with stroke history is provided. The system includes wearable device(s) to be worn by the subject, configured for monitoring motor activity of the subject. The system also includes a central server. The central server is configured for storing a plurality of rehabilitative physiotherapy programs to be carried out by the one or more subjects. The central server is also configured for receiving an instruction, from at least one other user for enabling the rehabilitative physiotherapy program to the subject. The central server is configured for receiving motor activity data from the wearable device(s), at predetermined time intervals or instantaneously during the ongoing physiotherapy program. The central server is further configured for analyzing the motor activity data and for classifying the recorded actual cluster of movements, scoring and scaling the difference between the targeted cluster of movements and the actual cluster of movements recorded in real time; and communicating a recovery status to the subject and the at least one other user periodically.

[0012] Briefly, according to an exemplary embodiment, a method for implementing a rehabilitative physiotherapy program for subjects with stroke history, is provided. The method includes storing a plurality of rehabilitative physiotherapy programs to be performed by the one or more subjects. The method also includes receiving an instruction from an at least one other user, for enabling the rehabilitative physiotherapy program to the subject. Further, the method includes receiving a motor activity data from the wearable device(s), at predetermined time intervals or instantaneously during an ongoing physiotherapy program. The method includes analyzing the motor activity data, and communicating a recovery status to the subject and the at least one other user periodically.

[0013] The summary above is illustrative only and is not intended to be in any way limiting. Further aspects, exemplary embodiments, and features will become apparent by reference to the drawings and the following detailed description.

BRIEF DESCRIPTION OF THE FIGURES [0014] These and other features, aspects, and advantages of the exemplary embodiments can be better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein: [0015] FIG. 1 is a block diagram of one embodiment of a system configured for implementing a rehabilitative physiotherapy program in a post-acute stroke period for a one or more of subjects, according to an embodiment of the present disclosure;

[0016] FIG. 2 illustrates an exploded view of a wearable device of system of FIG. 1, according to an embodiment of the present disclosure; [0017] FIG. 3 is a flow chart illustrating a method for implementing a rehabilitative physiotherapy program in a post- acute stroke period for a one or more of subjects; and

[0018] FIG. 4 is a block diagram of a computing device utilized for implementing the system of FIG. 1 according to an embodiment of the present disclosure.

[0019] Further, skilled artisans will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the figures with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

DETAILED DESCRIPTION

[0020] For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiments illustrated in the figures and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates. [0021] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be restrictive thereof.

[0022] The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not comprise only those steps but may comprise other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by "comprises... a" does not, without more constraints, preclude the existence of other devices or other sub- systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components. Appearances of the phrase“in an embodiment”,“in another embodiment” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0023] FIG. 1 is a block diagram of one embodiment of a system 100 configured for implementing a rehabilitative physiotherapy program in a post-acute stroke period for a one or more of subjects, according to an embodiment of the present disclosure. In particular, FIG. 1 illustrates a subject 102, a wearable device 104 to be worn by the subject 102, a network 106, a central server 108 and a medical expert 110.

[0024] It may be noted that the system 100 is shown to have the subject 102 and the medical expert 110; however, those skilled in the art would appreciate that the system 100 can include one or more of subjects. It addition, it may also be noted that for the sake of simplicity, the present disclosure will be explained by referring to the medical expert 110 or at least one other user; however, those skilled in the art would appreciate that the present invention can be exercised by one or more of medical experts such as a doctor, a physiotherapist, or a cardiac therapist. The terms subject and patient refer the same and are used interchangeably in the description.

[0025] The system 100 is configured for monitoring and tracking the subject’s 102 motor function improvement at least during a post-acute stroke period when the subject 102 is housed in an ICU or other such environments at a medical facility and during post-operative period when physiotherapy program is recommended to the subject 102 by the medical expert 110 after conducting one or more standard tests to assess the motor functions, such as Wolf Motor Function Test (WFMT), Fugl Myer Assessment (FMA) and the like. For conciseness, the system 100 monitors the improvement of motor activity in the affected parts of the subject’s body due to the stroke based on the physiotherapy administered to the subject. In about 80 percent of stroke cases, impairment of motor activity in upper and lower limbs is observed, limiting the ability of subjects (102) to perform activities of daily living. Stroke rehabilitation is a dynamic process with the overall aim of reducing stroke-related disability.

[0026] In one embodiment, the system 100 is configured for implementing a rehabilitative physiotherapy program for the one or more of subjects (102) during post treatment rehabilitation period. Since, the subject 102 and physiotherapist (110) might be at different locations, the present disclosure provides a system 100 for remotely monitoring and managing such rehabilitation programs via a network 106 for improvement in the affected parts of the subject’s body due to the stroke based on the treatment of physiotherapy. Normally, physiotherapy programs carefully designed to imitate activities of daily routine are imparted to the subjects to improve motor functions, especially, in the upper body or the limbs.

[0027] The system 100 includes the wearable device 104 to be worn by the subject 102, configured for monitoring motor activity of the subject 102. In one embodiment, the wearable device 104 includes one or more sensors configured for sensing and capturing the movement of the affected part of the subject’s body. For conciseness, the one or more sensors are configured for sensing and capturing each of a plurality of physical activities affecting the motor activity of the subject. In one example embodiment, the plurality of physical activities include daily, routine activities of the subject. Such plurality of physical activities of the subject are monitored, co-registered and mapped with pre- stored timed movements by the central server 108. The pre- stored timed movements is the historical labelled data that is annotated by the medical experts (110) and are stored in a repository (not shown) of the central server 108. It is to be noted that the pre- stored historical labelled data is the motor activity data pertaining to one or more activities that are part of a standardized test such as Wolf Motor Function Test (WFMT), Fugl Myer Assessment (FMA), Action Research Arm Test (ARAT) etc. that are conducted on the subjects (102) during post-operative period by the medical expert 110 before prescribing the most suitable physiotherapy program.

[0028] The pre-stored timed movements are for example daily activity movements that are curated and clustered so as to closely resemble the action and intensity of the physiotherapy exercises recommended to the subject. For example, moving a cup from left to right and vice versa repeatedly throughout the day, turning a door knob in clockwise and anti-clockwise direction etc.

[0029] The wearable device 104 includes a processor, configured for processing the sensor data corresponding to the motor activity and a memory, configured for storing the processed data. The wearable device 104 also includes a communication means to communicate the processed data to the central server 108. In one embodiment, the wearable device 104 further includes a feedback module (not shown) configured to send an alert notification to the subject, at an instant the subject 102 performs an incorrect movement with respect to the enabled rehabilitative physiotherapy program. The components of the wearable device 104 are explained in FIG. 2 in detail below.

[0030] In one exemplary embodiment, the wearable device 104 is worn by the subject undergoing physiotherapy treatment at a rehabilitation center using a robotic arm. During the course of treatment at the rehabilitation center, the activities performed by the subject are monitored and stored at the remote server 108. The stored activity data is further processed to generate clusters of activities and corresponding time frequency data using a learning model. Thus, when the subject performs the physiotherapy activities in the absence of the robotic arm, for example at home, the stored activity data at the remote server 108 provides a reference to monitor and measure the progress made by the subject.

[0031] The central server 108 is configured for receiving the motor activity data, from the wearable device 104, for a pre-determined time duration. In one embodiment, the central server 108 is configured for analyzing the motor activity data. The central server 108 is configured for identifying one or more physical activities from the received motor activity data by mapping with a pre- stored annotated data. The pre- stored annotated data comprises the plurality of physical activities. In one example, the pre- stored annotated data includes a set of physical activities which are standard in nature and are derived after conducting one or more standard tests to assess the motor functions, such as Wolf Motor Function Test (WFMT), Fugl Myer Assessment (FMA) and the like.

[0032] Further, the central server 108 is configured for applying a dynamic time warping (DTW) algorithm on the time-frequency data associated with the one or more monitored physical activities to determine a variance in the motor activity measurements from two different instances. The measure of variance gives an indication of improvement or deterioration in the motor activity of the subject 102. Specifically, the pre-stored annotated physical activity data, in a linear sequence, is compared with the motor activity data received at the central server 108, to estimate a measure of non-linearity. As described in the foregoing description, the pre-stored physical activity data is grouped into a plurality of clusters based on one or more attributes such as the body part involved, intensity of the movement, frequency of the movement etc. In one example, the clusters are created based on the activities carried out by the subject during post-operative days, thus creating a reference from which a learning model is created at the remote server 108 to generate a range of time frequency data for comparison with the actual physical activity performed by the subject.

[0033] Referring to present disclosure, the input to DTW are the features extracted from the accelerometer data (obtained by the wearable device 104), which is processed by the processor of the wearable device 104 to communicate the motor activity to the central server 108. The central server 108 is configured for identifying one or more physical activities from the received motor activity data by mapping the received motor activity data with the pre-stored annotated data in the repository of the central server 108.

[0034] In the present disclosure, the central server 108 of the system 100 is configured to apply DTW to provide an estimate of non-linearity between ‘present measurement’ and ‘historical annotated data’ . In one example, the present measurement described herein refers to the identified physical activities from the received motor activity data from the wearable device 104 by the central server 108 at a first instant. The historical annotated data described herein refers to the pre-stored annotated data comprising clusters of physical activities which are standard in nature and are derived after conducting one or more standard tests to assess the motor functions, such as Wolf Motor Function Test (WFMT), Fugl Myer Assessment (FMA) and the like.

[0035] Furthermore, in the same example when the motor activity data is received from the wearable device 104 and physical activities are identified by the central server 108 at a second instant, the DTW between the new measurement (identified physical activity at a second instant) and historical annotated data is made and the variance, i.e. the transformation of differences in the measurements is obtained. The central server 108 is configured to compare the new variance (obtained at second instant) with the previous variance (obtained at first instant) to provide data that is indicative of recovery status of the subject 102. Each time, the results of the comparison are stored and the central server 108 is configured to compare the new variance (obtained at subsequent instances, n) with the previous variance (obtained at previous instant, n-l) to identify the non-linearity that signifies one of improvement or deterioration in subjects’ motor activity. Consequently, a‘warping path’ is generated at the central server 108, for each of the one or more subjects, thereby providing a detailed analysis of recovery of the subjects over a period of time.

[0036] In one embodiment, the data processed by the central server 108 using the dynamic time warping algorithm is combined with one or more other data points pertaining to one or more physiological parameters such as temperature fluctuations while performing an activity, heart rate before, during and after performing the activity, blood pressure etc. for further evaluation of subjects’ health.

[0037] Further, central server 108 is configured for performing one or more actions, based on the results of dynamic time warping such as communicating the results of recovery reports to the subject 102 and the at least one other user (medical expert 110). The one or more actions also comprises triggering an alert to the subject 102 and to the at least one other user (medical expert 110).

[0038] Further, the medical expert 110 monitors the recovery of the subject 102 with the help of the recovery report periodically using various methods such as Fugl Meyer Assessment (FMA), Wolf Motor Function Test (WMFT) or Action Research Arm Test (ARAT). Based on the monitoring and recovery reports, the medical expert 110 may make necessary changes in the exercise regime, for example, varying the frequency, type of therapy etc. The changes may be communicated to the subject 102 in form of alerts. In one embodiment, the wearable device 104 is configured to receive the alerts from the central server 108. The alerts may include another rehabilitative physiotherapy program, or an updated rehabilitative physiotherapy program for the one or more subjects (102).

[0039] The central server 108 is also configured for storing a plurality of rehabilitative physiotherapy programs to be carried out by the one or more subjects (102). The plurality of rehabilitative physiotherapy programs are stored, based on the severity of stroke for treating the affected regions of the one or more subjects (102). One exemplary representation of a database in the central server 108 configured for storing a plurality of rehabilitative physiotherapy programs to be carried out by the one or more subjects (102) is shown Table 1. below :

Table 1.

[0040] The central server 108 is also configured for receiving an instruction, from at least one other user for administering the rehabilitative physiotherapy program to the subject 102. The central server 108 is configured for receiving the instruction for enabling the rehabilitative physiotherapy program to the subject 102, from at least a medical expert 110 or a physiotherapist for sending a notification to the subject to access the enabled rehabilitative physiotherapy program. The central server 108 is configured for receiving a motor activity data from the wearable device 104, at various instances, for example, while performing daily routine activities by the subject 102. The central server 108 is configured for receiving the motor activity data, from the wearable device 104, for the entire duration of enabled rehabilitative physiotherapy program for the subject 102. [0041] For example, with reference to the Table 1 above, a subject (XXXAAA) being administered a physiotherapy program for 2 months, wherein the subject is required to perform one or more exercises such as wrist movement, grasping activities etc. each for ten minutes every day. In the exemplary scenario, the present disclosure provides a means for monitoring daily routine activities of the subject (i.e. XXXAAA) during the entire course of 2 month program such that the one or more daily routine activities are monitored by the wearable device 102 and communicated to the remote server 108, wherein a time-frequency data of the monitored physical activity is determined and further compared with the time-frequency data associated with the most closely resembling movement from the cluster of physical activities pre- stored at the central server 108. The processing results in identifying activities of daily routine that correlate with the prescribed exercises in the rehabilitation program. Further, the comparison results in determining a history of adherence to the prescribed physiotherapy regimen, improvement in relation to the cluster of movements and the overall progress of the subject at different points in time.

[0042] FIG. 2 illustrates a wearable device 104 of system 100 of FIG. 1, according to an embodiment of the present disclosure. In particular, the FIG. 2 illustrates an exploded view 200 of a wearable device 104 of FIG. 1 comprising a plurality of sensors and other components as shown. The system 100 includes the wearable device 104 to be worn by the subject 102, configured for monitoring a motor activity of the subject 102. In one embodiment, the wearable device 104 includes a one or more sensors configured for sensing and capturing the movement of the affected part of the subject’s body. For conciseness, the one or more sensors are configured for sensing and capturing each of a plurality of physical activities affecting the motor activity of the subject. In one example embodiment, the plurality of physical activities include daily, routine activities of the subject.

[0043] The wearable device 200 as shown in FIG. 2 is configured for capturing data associated with a neural and heart activity. The wearable device 200 includes a first electrode and a second electrode configured for measuring an electrical activity of a heart. The wearable device 200 also includes an accelerometer configured for detecting and acquiring neural accelerometry data. In one example embodiment, the wearable device 200 may be a sensor. The wearable device 200 may comprise a plurality of sensors configured for sensing data associated with the neural and heart activity of the subject 102 in a post-acute stroke treatment or during the rehabilitative physiotherapy program. In one embodiment, the wearable device 200 comprises a plurality of sensors and other components, as shown in FIG. 2. In one example embodiment, the lower surface of the wearable device 200 includes photoplethysmography (PPG) sensors 220, the upper surface includes another PPG sensor (where index finger is placed). Further, the wearable device 200 has an ambient sensor measurement 222 on top and skin temperature measurement 224 at the bottom. The information sensed from PPG sensors are used for deriving cardiac activities. Temperature sensors, such as galvanic skin conductance sensors are used for monitoring and making corrections to derived cardiac activities.

[0044] In one embodiment, the PPG sensor as part of the wearable device 200 and other sensors present in the wearable device 200 are used to derive a precise accurate signal (output signal). In one embodiment, the wearable device 200 employs reflectance mode Photoplethysmography (PPG) to extract the pulse signal from the wrist of the subject 102 which is equivalent to the heart beat. In another embodiment, the wearable device 200 comprises a Fully Integrated Analog Front End (AFE) 226 that consists of a low noise receiver channel 228 with an integrated Analog to Digital Converter 230, an LED transmit section 232, diagnostics for sensor 234 and LED fault detection 238. Further, the wearable device 200 comprises additional components which are an ultra-low power microcontroller (MCU) 240 for calculating the heart rate, a wireless module based on Bluetooth Low Energy (BLE) 242 for exchanging information with smart phones, tablets or PCs, a motion sensor (accelerometer) for monitoring the user’s activity, a reflectance mode sensing probe 244 , ferroelectric RAM (FRAM) 246 for data logging, a lithium-polymer rechargeable battery 248 , a battery charger and a 250 battery fuel gauge 252. Further, the wearable device 200 also includes a microcontroller 254 configured to calculate the heart rate, merge the motion sensor data, and process the AFE information. The microcontroller 254 is with specific features including the ability to maintain the context at all times. The microcontroller 254 also has a limited power budget because it has to continuously run for monitoring the subject to avoid drain of batteries.

[0045] The wearable device 200 is described in detail in Australia patent application number “2017203433” titled “System and Method for Identification of Neuro-Cardiological Disturbances” filed on the“22 May, 2017” and is incorporated herein.

[0046] FIG. 3 is a flow chart illustrating a method 300 for implementing a rehabilitative physiotherapy program in a post- acute stroke period for a one or more of subjects. FIG. 3 will be described from the perspective of a processor that is configured to execute computer- readable instructions to carry out the functionalities of the components of the central server 108 of the system 100 shown in FIG. 1 and components of the wearable device 200 as shown in FIG. 2. Each step is described in detail below.

[0047] At step 302, a plurality of rehabilitative physiotherapy programs to be carried out by the one or more subjects are stored. The central server 108 of system 100 as shown in FIG. 1 is configured for storing the plurality of rehabilitative physiotherapy programs to be carried out by the one or more subjects. The plurality of rehabilitative physiotherapy programs are stored, based on the severity of stroke for treating the affected regions of the one or more subjects. In one example, the plurality of rehabilitative physiotherapy programs are stored in a database of the central sever for providing outpatient rehabilitation and is used for people who are living safely at home but require rehabilitation therapy to help them live free of physical limitation or pain. Outpatient therapy programs are for those who no longer need inpatient therapy but may benefit from a planned schedule of therapy.

[0048] In one embodiment, the central server also includes a repository configured to store plurality of physical activities. The plurality of physical activities are pre-stored timed movements which is the historical labelled data annotated by the medical experts and are stored in a repository (not shown) of the central server. It is to be noted that the pre-stored historical labelled data is the motor activity data pertaining to one or more activities that are part of a standardized test such as Wolf Motor Function Test (WFMT), Fugl Myer Assessment (FMA), Action Research Arm Test (ARAT) etc. that are given to the subjects (102) during post operative period by the medical expert 110 before prescribing the most suitable physiotherapy program.

[0049] At step 304, an instruction from at least one other user is received, for enabling the rehabilitative physiotherapy program to the subject. The central server is configured for receiving the instruction for enabling the rehabilitative physiotherapy program to the subject, from at least a medical expert or a physiotherapist. Once the instruction is received, by the central server from at least a medical expert or a physiotherapist, the central server is configured for sending a notification to the subject to access the enabled rehabilitative physiotherapy program. The medical expert provides instructions to the central server to enable access for a particular rehabilitative physiotherapy program to the subject based on his/her medical history, earlier short-term and long-term effects of practiced post-acute physiotherapy. In particular, the medical expert provides instructions to the central server to enable access for a particular rehabilitative physiotherapy program to the subject based on the case history of the subject comprising of CT-scans, MRI scans, treatment course, physical examination reports etc to arrive at a physiotherapy regime for the subject. The rehabilitative physiotherapy program are also enabled based on the Wolf Motor Function Test (WFMT), Fugl Myer Assessment (FMA), Action Research Arm Test (ARAT) etc. that are conducted on the subjects during post operative period by the medical expert.

[0050] At step 306, a motor activity data is received from the wearable device, after exercising the enabled rehabilitative physiotherapy program by the subject. The central server is configured for receiving the motor activity data, from the wearable device, from the affected region for the for a pre-determined time duration. In one example, the wearable device is configured for overall body movement monitoring with accurate, extensive and in depth analysis of body movement monitoring. The motor activity data received comprises each of a plurality of physical activities affecting the motor activity of the subject. In one example embodiment, the plurality of physical activities include daily, routine activities of the subject. The wearable device specifically monitors the affected parts of the subject’s body due to stroke. Furthermore, the wearable device worn by the subject is configured to inform the medical expert or the physiotherapist to understand improvement based on the enable physiotherapy regime in the affected parts of the body due to the stroke.

[0051] At step 308, the motor activity data is analyzed. In one embodiment, the central server is configured for analyzing the motor activity data. The central server 100 is configured for identifying one or more physical activities from the received motor activity data by mapping with a pre- stored annotated data. The pre- stored annotated data comprises the plurality of physical activities. In one example, the pre- stored annotated data includes a set of physical activities which are standard in nature and are derived after conducting one or more standard tests to assess the motor functions, such as Wolf Motor Function Test (WFMT), Fugl Myer Assessment (FMA) and the like.

[0052] Further, the central server is configured for applying a dynamic time warping (DTW) on the identified physical activities to determine a measure of improvement or deterioration in the physical activity of the subject. Generally, in time series analysis, dynamic time warping (DTW) is one of the algorithms for measuring similarity between two temporal sequences, which may vary in speed. For instance, similarities in walking could be detected using DTW, even if one person was walking faster than the other, or if there were accelerations and decelerations during the course of an observation. In one example, the DTW has been applied to temporal sequences of video, audio, and graphics data, any data that can be turned into a linear sequence can be analyzed with DTW.

[0053] Referring to present disclosure, the input to DTW are the features extracted from the accelerometer data (obtained by the wearable device), which is processed by the processor of the wearable device to communicate the motor activity to the central server. The central server is configured for identifying one or more physical activities from the received motor activity data by mapping the received motor activity data with the pre-stored annotated data in the repository of the central server.

[0054] In the present disclosure, the central server of the system 100 is configured to apply DTW to provide a cost between‘present measurement’ and‘historical annotated data’ . In one example, the present measurement described herein refers to the identified physical activities from the received motor activity data from the wearable device by the central server at a first instant. The historical annotated data described herein refers to the pre-stored annotated data comprising the plurality of physical activities which are standard in nature and are derived after conducting one or more standard tests to assess the motor functions, such as Wolf Motor Function Test (WFMT), Fugl Myer Assessment (FMA) and the like.

[0055] Furthermore, in the same example when the motor activity data is received from the wearable device 104 and physical activities are identified by the central server at a second instant, the DTW between the new measurement (identified physical activity at a second instant) and historical annotated data is made and the variance, i.e. the transformation of differences in the measurements is obtained. The central server is configured to compare the new variance (obtained at second instant) with the old variance (obtained at first instant) to provide data that is indicative of recovery status of the subject. Each time, the results of the comparison are stored and the central server 108 is configured to compare the new variance (obtained at subsequent instances, n) with the old variance (obtained at previous instant, n-l) to identify the error or cost that signifies one of improvement or deterioration in subjects’ motor activity.

[0056] At step 310, a recovery status is communicated to the subject and the at least one other user periodically. The central server is configured for processing the motor activity data, and communicating a recovery status to the subject and the at least one other user periodically. Further, the central server performs one or more actions, based on the results of comparison such as communicating the results of recovery reports to the subject and the at least one other user such as medical expert. For example, the one or more actions may include triggering an alert to the subject and to the at least one other user as medical expert. In addition, the recovery status may include providing feedback to the patients based on the improvement and thereby recommending an updated exercise regime and further provide assistance in rectifying the incorrect exercise performed by the subject.

[0057] FIG. 4 is a block diagram of a computing device 400 utilized for implementing the system 100, according to an embodiment of the present disclosure. The components of the system 100 described herein are implemented in computing devices. One example of a computing device 400 is described below in FIG.4. The computing device comprises one or more processor 402, one or more computer-readable RAMs 404 and one or more computer- readable ROMs 406 on one or more buses 408. Further, computing device 400 includes a tangible storage device 410 that may be used to execute operating systems 420 and modules existing in central server 108 of system 100. The various components of the system 100 including a central server can be stored in tangible storage device 410. Both, the operating system and the modules existing in controller 108 of system 100 are executed by processor 402 via one or more respective RAMs 404 (which typically include cache memory).

[0058] Examples of storage devices 410 include semiconductor storage devices such as ROM 406, EPROM, flash memory or any other computer-readable tangible storage device 410 that can store a computer program and digital information. Computing device also includes R/W drive or interface 414 to read from and write to one or more portable computer-readable tangible storage devices 428 such as a CD-ROM, DVD, memory stick or semiconductor storage device. Further, network adapters or interfaces 412 such as a TCP/IP adapter cards, wireless wi-fi interface cards, or 3G or 4G wireless interface cards or other wired or wireless communication links are also included in computing device 400. In one embodiment, the modules existing in the processor of system 100 can be downloaded from an external computer via a network (for example, the Internet, a local area network or other, wide area network) and network adapter or interface 412. Computing device 400 further includes device drivers 416 to interface with input and output devices. The input and output devices can include a computer display monitor 418, a keyboard 424, a keypad, a touch screen, a computer mouse 426, and/or some other suitable input device. [0059] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein. The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.

Claims

Claims:

1. A system for implementing a rehabilitative physiotherapy program in a post-acute stroke period for a one or more of subjects, the system comprising:

a wearable device to be worn by the subject, configured for monitoring a motor activity of the subject; and

a central server, configured for:

storing a plurality of rehabilitative physiotherapy programs to be carried out by the one or more subjects;

receiving an instruction, from at least one other user for enabling the rehabilitative physiotherapy program to the subject;

receiving a motor activity data from the wearable device; analyzing the motor activity data, and

communicating a recovery status to the subject and the at least one other user periodically.

2. The system of claim 1, wherein the wearable device comprises:

one or more sensors configured for sensing and capturing each of a plurality of physical activities affecting the motor activity of the subject; a processor, configured for processing the captured data to the motor activity data; a memory, configured for storing the processed data; and

a communication means to communicate the processed data to the central server.

3. The system of claim 1, wherein the central server is configured for receiving the motor activity data, from the wearable device, instantaneously or at pre-determined instances.

4. The system of claim 1, wherein the central server is configured for analyzing the motor activity data by executing the said steps:

identifying a data associated with one or more physical activities from the received motor activity data by mapping with a pre-stored annotated data; wherein the pre-stored annotated data comprises at least a time-frequency sequence for a plurality of physical activities;

applying a dynamic time warping on the data associated with the identified physical activities to determine a recovery state of the subject; performing one or more actions, based on the results of dynamic time warping such as communicating the results of recovery reports to the subject and the at least one other user; wherein the one or more actions also comprises triggering an alert to the subject and to the at least one other user.

5. The system of claim 1, wherein the wearable device is configured to receive the alerts from the central server; wherein the alerts comprises another rehabilitative physiotherapy program, or an updated rehabilitative physiotherapy program for the one or more subjects.

6. The system of claim 2, wherein the wearable device further comprises:

a feedback module configured to send an alert notification to the subject, at an instant of detecting an incorrect movement with respect to the enabled rehabilitative physiotherapy program.

7. The system of claim 1, wherein the plurality of rehabilitative physiotherapy programs stored at the central server are based on one or more standardized tests performed by a medical expert.

8. The system of claim 1 , wherein the central server is configured for receiving the instruction for enabling the rehabilitative physiotherapy program to the subject, from at least the medical expert or a physiotherapist for sending a notification to the subject to access the enabled rehabilitative physiotherapy program.

9. A method for implementing a rehabilitative physiotherapy program in a post-acute stroke period for a one or more of subjects, the method comprising:

receiving an instruction from at least one other user, for enabling the rehabilitative physiotherapy program to the subject;

receiving a motor activity data from the wearable device, after exercising the enabled rehabilitative physiotherapy program by the subject;

analyzing the motor activity data, and

10. The method of claim 9, analyzing the motor activity data by executing the said steps: identifying a data associated with one or more physical activities from the received motor activity data by mapping with a pre-stored annotated data; wherein the pre-stored annotated data comprises at least a time-frequency sequence associated with the plurality of physical activities;

applying a dynamic time warping on the data associated with the identified physical activities to determine a recovery status of the subject; performing one or more actions, based on the results of dynamic time warping such as communicating the results of recovery reports to the subject and the at least one other user; wherein the one or more actions also comprises triggering an alert to the subject and to the at least one other user.