Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
SHOULDER JOINT AND GIRDLE BRACE AND METHOD FOR MONITORING PROPRIOCEPTION OF SHOULDER OF AN INDIVIDUAL

Applicant:
Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th Floor,
Nariman Point, Mumbai 400021,
Maharashtra, India

The following specification particularly describes the invention and the manner in which it is to be performed.
TECHNICAL FIELD
The disclosure herein generally relates to health monitoring, and, more particularly, to a shoulder joint and girdle brace and method for monitoring proprioception of shoulder of an individual.
BACKGROUND
Basic function of shoulder of an individual along with elbow, forearm and hand is to position in space to perform various possible activities. The shoulder therefore is a highly mobile and unconstrained articulation with a good range of motion (ROM). In course of routine assessment of the shoulder, the range of motion and muscle strength are both essential components of clinical examination protocols. These parameters are also essential part of indications for surgery and in determining functional recovery and patient satisfaction after both surgical and non-surgical treatments of pathology around the shoulder joint and within the shoulder. Some of these conditions include arthritis, trauma, sports injuries, and degenerative cuff tears. Over decades, existing shoulder braces act to restrict or limit movements in some directions whilst permitting other movements, also help in rest provided to the joint by stabilizing and providing directed pressure and or heat/cold measures. Conventional clinical methods such as a goniometer, visual estimation, smartphone applications, inclinometer better posture kneeling chairs, and Jobri® are used to define and quantify range of motion of the shoulder. The joint is deep within a muscular envelope and any estimation by these methods is only approximate.
Besides the shoulder movements, also have components of scapular motion which, in addition to true glenohumeral movements, provide enhanced and combined range of motion of the shoulder. These methods would be subject to inherent inter-observer variation and intra observer variation as well as error in actual quantification of motion parameters. Further, current techniques are limited with various movement or range of motion alone and are not able to provide coordinated dynamic control of muscles related to a gesture or the movement. Visual examination is a common method and single visual examination is not much different from a mean of multiple examinations in same sitting. An interobserver variation is significant and varies with the motion being tested. The best utility of the visual examination seems to be comparative evaluation of both shoulders simultaneously. The visual examination is also dependent on education and experience of examiner. For an outcome measure to be useful in clinical practice, the instrument must be highly responsive, and limits of agreement should be smaller than minimal clinical difference one intends to detect.
A universal goniometer (e.g., 360-degree manual) is a most widely used instrument in a therapist and orthopedists’ armamentarium in the clinic. Goniometry techniques are defined and dependent on operator and adherence to technique. It is time consuming and often delegated to less experienced staff, and therefore needs a simpler tool for routine ROM assessment. One may use goniometric measurements from a trained experienced therapist as ground truth. An inclinometer is another device that has been used with success and use of the inclinometer is fraught with difficulties. Digital inclinometer has also been used with some success but has significant limitations. The use of smartphone-based inclinometer apps and goniometer apps have been found to be sufficient in doing University of California–Los Angeles (UCLA) shoulder scores and Constant-Murley (CM) shoulder scores in diseased populations. However, these are limited by inability to provide any kind of quantitative information on coordinated dynamic activity involved in any of the defined activities. Few research groups have attempted to quantify regions of interest (ROI) of an upper extremity; for example, assess the shoulder (i.e., a glenohumeral (GH) joint) movements like superior/inferior glenohumeral translations, anterior/posterior translations using surface-based electromagnetic methods, normative biplane fluoroscopy. Recording the glenohumeral joint angular motion and linear translation of an individual during shoulder motion performed in three planes of humero-thoracic elevation during their day-to-day activity is a challenging task and intensive process. Similarly, when a synchronous movement like glenohumeral and scapulothoracic movements together contribute for an action (e.g., Flexion), existing techniques lag in ability to differentiate and quantify the same. Further, the complexity of quantification increases when it's extended to monitor the movement/action with respect to the musculoskeletal mechanism. In addendum, computer vision techniques are prone to degradation of accuracy in exchange for improving latency and wearable based technique accuracy get compromised due to skin and muscle movement artefact.
SUMMARY
Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, there is provided a shoulder joint and girdle brace to monitor a proprioception of shoulder of an individual. The shoulder joint and girdle brace includes a proprioceptive sensor unit to detect a linear and an angular movements of one or more joints of a shoulder; and an electronics unit, wherein the electronics unit comprises: a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to: receive, a raw electromyography (EMG) signal from the shoulder joint and girdle brace based on a muscle contraction in the individual; preprocess, the raw EMG signal to obtain a normalized EMG signal; process, the normalized EMG signal to determine a muscle force associated with the individual; estimate, a torque around the one or more joints of the shoulder of the individual based on one or more phenomenological models; determine, a parameter score for one or more sensors associated with each joint of the shoulder based on a range of motion (ROM), and the torque around the one or more joints; and estimate, a performance index (PI) of the one or more joints of the shoulder of the individual based on the parameter score for the one or more sensors. The raw EMG signal pertains to at least one of: (i) one or more electrical signals, (ii) one or more physio-electrical signals. The raw EMG signal is returned as an output at one or more sensor units.
In another embodiment, a processor implemented method of monitoring a proprioception of shoulder of an individual by a shoulder joint and girdle brace is provided. The processor implemented method includes at least one of: receiving, via one or more hardware processors, a raw electromyography (EMG) signal from the shoulder joint and girdle brace based on a muscle contraction in the individual; preprocessing, via the one or more hardware processors, the raw EMG signal to obtain a normalized EMG signal; processing, via the one or more hardware processors, the normalized EMG signal to determine a muscle force associated with the individual; estimating, via the one or more hardware processors, a torque around one or more joints of the shoulder of the individual based on one or more phenomenological models; determining, via the one or more hardware processors, a parameter score for one or more sensors associated with each joint of the shoulder based on a range of motion (ROM), and the torque around the one or more joints; and estimating, via the one or more hardware processors, a performance index (PI) of the one or more joints of the shoulder of the individual based on the parameter score for the one or more sensors. The raw EMG signal pertains to at least one of: (i) one or more electrical signals, (ii) one or more physio-electrical signals. The raw EMG signal is returned as an output at one or more sensor units.
In yet another embodiment, there are provided one or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors causes at least one of: receiving, a raw electromyography (EMG) signal from the shoulder joint and girdle brace based on a muscle contraction in the individual; preprocessing, the raw EMG signal to obtain a normalized EMG signal; processing, the normalized EMG signal to determine a muscle force associated with the individual; estimating, a torque around one or more joints of the shoulder of the individual based on one or more phenomenological models; determining, a parameter score for one or more sensors associated with each joint of the shoulder based on a range of motion (ROM), and the torque around the one or more joints; and estimating, a performance index (PI) of the one or more joints of the shoulder of the individual based on the parameter score for the one or more sensors. The raw EMG signal pertains to at least one of: (i) one or more electrical signals, (ii) one or more physio-electrical signals. The raw EMG signal is returned as an output at one or more sensor units.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:
FIGS. 1A-1D are exemplary views of one or more components of a shoulder joint and girdle brace to monitor proprioception of shoulder of an individual, according to an embodiment of the present disclosure.
FIG. 2, illustrates an exemplary exploded view of a electronics unit of the shoulder joint and girdle brace as depicted in FIGS. 1A-1D, according to an embodiment of the present disclosure.
FIG. 3A and FIG. 3B are exemplary flow diagrams illustrating a method of monitoring the proprioception of the shoulder of the individual by the shoulder joint and girdle brace, according to an embodiment of the present disclosure.
DETAILED DESCRIPTION OF EMBODIMENTS
Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments.
There is a need for a device to provide stability of a shoulder and assess proprioception of the shoulder’s functional movement. Embodiments of the present disclosure provide a shoulder joint and girdle brace and method for monitoring proprioception of shoulder of an individual. The embodiment of the present disclosure quantitatively documents movement of a shoulder girdle, and strength as part of upper extremity functional assessment. The embodiment of the present disclosure provides support to the shoulder girdle including glenohumeral, scapulothoracic articulations, and minor acromioclavicular articulation during day-to-day activities, during planned therapy sessions both at home and in clinic, and during clinical examination. The shoulder is described as the shoulder girdle consisting of a glenohumeral joint, an acromioclavicular joint, a scapulothoracic joint, and a sternoclavicular joint. The shoulder joint and girdle brace are embedded with one or more sensors to quantitatively document, analyze and trend of the strength, stability, and range of motion of the joints or one or more activities.
Referring now to the drawings, and more particularly to FIGS. 1 through 3B, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
Reference numerals of one or more components of the shoulder joint and girdle brace to monitor the proprioception of the shoulder of the individual, as depicted in the FIG. 1A through FIG. 2 are provided in Table 1 below for ease of description.
S.NO NAME OF COMPONENT REFERENCE NUMERALS
1 Shoulder joint and girdle brace 100
2 Ventilation holes 102
3 Forearm Rest Strap 104
4 forearm rest 106
5 Arm Strap 108
6 strap for forearm rest 110
7 Strap for body fit 112
8 Cut-out for better arm fitment 114
9 Temperature sensor 116
10 IMU sensor (SUPRASPINATUS) 118A
11 IMU sensor (HUMERUS) 118B
12 IMU sensor (DELTOIDEUS) 118C
13 EMG sensor (LATISSIMUS) 120A
14 EMG sensor (BICEPS) 120B
15 EMG sensor (DELTOIDEUS) 120C
16 EMG sensor (TRAPEZIUS) 120D
17 Electronics unit 122
18 Armpit cutout 124
19 Hardware processor (s) 202
20 Memory 204
21 I/O interface (s) 206
22 System bus 208
23 Module (s) 210
24 Repository 212
25 system database 214
26 other data 216
TABLE 1
FIGS. 1A-1D are exemplary views of one or more components of the shoulder joint and girdle brace 100, according to an embodiment of the present disclosure. The shoulder joint and girdle brace 100 includes the three patterned ventilation holes 102 to allow airflow to a skin of the individual (e.g., a user or a patient) covered under the shoulder joint and girdle brace 100. An armpit cutout 124 is configured to move or rotate arm of the individual. The arm strap 108 is configured to tightly hold different size of arms with the shoulder joint and girdle brace 100. The forearm rest 106 is configured to rest the forearm of the individual on the shoulder joint and girdle brace 100. In an embodiment, the forearm rest 106 can be fitted onto different sizes as three straps are provided i.e., a first strap to hold the forearm rest 106 from front position, a second strap to hold the forearm from backside position to provide stability, and a third strap to remove or wear the forearm rest 106. The forearm rest straps 104 is configured to hold the forearm rest 106. The body strap 112 is configured to fit and hold the shoulder joint and girdle brace 100 on one or more body size pattern associated with each individual. The cut-out 114 is configured to fit and hold the shoulder joint and girdle brace 100 onto one or more arm size pattern of each individual.
The shoulder joint and girdle brace 100 is configured to monitor the proprioception of the shoulder of the individual by. The shoulder joint and girdle brace 100 is configured to maintain the stability of the shoulder joint of the individual and assess the proprioception of shoulders functional movement, while a shoulder motion (e.g., combination of movements at one or more joints) is performed in three planes of humerothoracic elevation, by comparing one or more differences in a glenohumeral joint angular motion and one or more linear translations between before and after of an event. For example, the event could be surgery or injury. In an embodiment, the shoulder is referred to as the shoulder girdle including the one or more joints i.e., the glenohumeral joint, the acromioclavicular joint, the scapulothoracic joint, and the sternoclavicular joint. In an embodiment, a shoulder joint mobility is classified as a flexion (e.g., forward movement), an extension (e.g., backward movement), an abduction (e.g., an elevation), and rotations.
The shoulder joint and girdle brace 100 includes a proprioceptive sensing unit 118A-C and 120A-D, and a proprioceptive analytics unit (Not shown in FIGURE). A raw EMG signal is received from the shoulder joint and girdle brace 100 based on a muscle contraction in the individual. The raw EMG signal corresponds to: (i) one or more electrical signals, or (ii) one or more physio-electrical signals, and (iii) a combination thereof. In an embodiment, the raw EMG signal is returned as an output at one or more sensor unit. In an embodiment, the proprioceptive sensing unit 118A-C and 120A-D is alternatively referred to as the one or more sensor unit. The one or more sensor unit 118A-C and 120A-D includes the one or more electromyography (EMG) sensors 120A-D, the one or more inertial measurement unit (IMU) sensors 118A-C, and the temperature sensor 116. The one or more electromyography (EMG) sensors 120A-D is configured to detect electrical activities of respective muscles i.e., an individual’s muscle signal (e.g., patient's muscle signal). The one or more inertial measurement unit (IMU) sensors 118A-C is configured to detect one or more motions of joints (e.g., a range of motion (ROM)) and quantitatively measure collected data. For example, the one or more activities corresponds to joint movements (e.g., range of motion (ROM)). In an embodiment, the temperature sensor 116 determines a local skin temperature around the joint occurs due to changes in a blood flow in that region which helps to determine condition of injury. The raw EMG signal is preprocessed to obtain a normalized EMG signal. The raw EMG signal is processed at a filter unit to obtain a noise-filtered EMG signal. In an embodiment, the filter unit includes: (i) a Butterworth filter, and (ii) a Notch filter. The noise-filtered signal is rectified with a half-wave rectifier and a filtered EMG signal is obtained by eliminating high frequency noise components in a frequency domain. The rectified and the filtered EMG signal is normalized to extract one or more frequency features from a power spectrum and obtain the normalized EMG signal by applying a windowed power spectral density (PSD) technique. In an embodiment, the one or more frequency features corresponds to (i) a root mean square (RMS) of the EMG signal, (ii) a mean power frequency (MPF), (iii) a median power frequency (MNF), (iv) total power (TP), and (v) a power spectral density (PSD). For example, in a frequency domain, 1024 point windowed power spectral density (PSD) algorithm is applied on rectified EMG signals. Features extracted are but not limited to as a root mean square (RMS) of EMG signal, a mean power frequency (MPF), a median power frequency (MNF), a total power (TP), 95 percentile power spectral density (PSD) and 50 percentile power spectral density (PSD) from a power spectrum.
The range of motion (ROM) is a measurement of an extent of a movement of a joint (i.e., shoulder joint), which is used to evaluate and classify joint impairments in patients or an efficacy of a rehabilitation program or a surgical procedure. For example, the one or more IMU sensors 118A-C may be composed of a three-dimensional (3D) accelerometer, a magnetometer, and a gyroscope. The accelerometer sensor determines status or activities i.e., walking, jogging of an individual. In addition, built-in inertial sensors allow detecting and monitoring both linear and angular movements of the shoulder or other joints. The shoulder joint is a multiaxial ball-socket synovial joint type connecting an upper limb to a thorax. In an embodiment, the shoulder joint and girdle brace 100 is configured to estimate contribution of bone, and muscle of the shoulder joints for a particular activity with the ROM, and joint movement classification like flexion, extension abduction-adduction, medial and lateral rotation. In an embodiment, an application component that is inbuilt with a pre-processing unit of a surface electromyography (sEMG) and the IMU sensors 118A-C to determine the muscle strength and corresponding contribution towards movement of the bone. The sEMG is a kinesiological tool utilized to understand a mechanistic insight of muscle such as the muscle strength, the muscle fatigue, and type of muscle contraction, a joint torque. The proprioceptive sensing unit 118A-C and 120A-D is configured to detect and monitor both linear and angular movements of the shoulder joints. The proprioceptive sensing unit 118A-C and 120A-D is embedded with one or more processors to pre-process the signal to reduce noise, movement artefact, feature extraction. The resultant signal enhances causal insights leveraging post-operative outcomes using actionable in-sights mining. In an embodiment, a storage unit is either inbuilt (e.g., locally or a remote unit (e.g., server)).
The proprioceptive analytics unit is configured to determine a muscle force, muscle recruitment, muscle strength, and the ROM of joints to estimates a performance index (PI) of the shoulder joints. The normalized EMG signal is processed to determine a muscle force associated with the individual. The muscle activity is captured by the embedded sensor integrated with the shoulder joint and girdle brace 100. In an embodiment, the sEMG data is logged at a sampling rate of 1000Hz and 50Hz for EMG signals and the goniometer or one or more IMU sensors 118A-C respectively. Electrodes are carefully placed over muscles, and a reference electrode is attached to the bone at a wrist with a smash or a belt. In an embodiment, prior to placement of the electrode, skin of the individual is cleaned with a diluted ethanol to produce site clean to reduce resistance and movements. The raw EMG data is filtered by a high pass filter second order Butterworth to cut off low frequency signal less than two Hz. Further, a low pass filter is applied to cut off high frequency is but not limited to e.g., more than 400 Hz. In an embodiment, a notch filter (e.g., a band stop filter) of second order is applied for power line interference in range e.g., 47-49 Hz.
The muscle force is computed from the normalized EMG signal of the one or more EMG sensors 120A-D acquired during contraction:
F_EMG (t)=cE(t) (1)
where,
F_EMG is muscle force at instant t of a sensor
C is a gain constant
E(t) is Normalized EMG signal
In the frequency domain, the mean power frequency (MPF) of the filtered signals is obtained using a short-time Fourier transform (STFT) spectrogram. In an embodiment, a slope of regression of the MPF is an indicator for local muscle fatigue. For example, a decreasing trend of the muscle force or negative value of the slope of regression is an indicative of a marker for muscle fatigue. A muscle fatigue is computed from a slope of regression of the muscle force extracted from the normalized EMG signal. The influence of muscle fatigue is determined by assessing the frequency domain power spectrum of the EMG. As the muscle fatigues, there is a concomitant change in the power spectrum of the sEMG signals. There is an increase in an amplitude of lower frequency band and a relative decrease in higher frequency band. The extracted MPF from the power spectrum is used as feature parameters to determine the muscle fatigue. The filtered EMG data is sent to a calibration routine to identify maximum and minimum muscle contraction.
A torque around one or more joints of the shoulder of the individual is estimated based on one or more phenomenological models which are derived by a Hill-type muscle model. The Hill’s model muscle activation, force-length and force-velocity properties are considered independently. An EMG driven muscle model for determining muscle forces in the ankle, knee, back and upper limb, for normal and pathological condition. A parameter score is determined for one or more sensors (e.g., the one or more IMU sensors 118A-C attached to the shoulder joint and girdle brace 100) associated with each joint of the shoulder based on a range of motion (ROM), and the torque around the one or more joints.
f_t=?S(??_t)+t_t+?_t+F_EMG (t)+?(?_t-??_((t-1)))+µ(t) (2)
where,
?S(??_t)-Slope of ROM of a joint in degrees at an instant t
t(t)- Slope of joint torque at instant t
?(t)- Slope of velocity of arm movement at instant t
F_EMG –Peak muscle force estimated from the sEMG at instant t
? –DASH score at instant t
t –No of assessment
µ(t) –Slope of muscle fatigue at instant t
f_t – Parameter score of sensors at instant t(sec)
A performance index (PI) of the one or more joints of the shoulder of the individual is estimated based on the parameter score for the one or more sensors. The performance index (PI) of the joint is estimated using a Sen’s slope technique as shown in equation (3):
PI(t)=f_t-b*t (3)
where,
b –Median of f_t
t –time instant in sec.
In an embodiment, a mobile application component receives sensor data, process and determine improvement in shoulder performance. For example, (a) reinforced or active assisted range of motion where a therapist assists the patient through an exercise, and (b) the exercise against resistance.
With reference to FIG. 2, illustrates an exemplary exploded view of the electronics unit 122 of the shoulder joint and girdle brace 100 as depicted in FIGS. 1A-1D, according to an embodiment of the present disclosure. The electronics unit 122 includes one or more processor(s) 202, communication interface device(s) or input/output (I/O) interface(s) 206, and one or more data storage devices or memory 204 operatively coupled to the one or more processors 202. The memory 204 includes a database. The one or more processor(s) processor 202, the memory 204, and the I/O interface(s) 206 may be coupled by a system bus such as a system bus 208 or a similar mechanism. The one or more processor(s) 202 that are hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the one or more processor(s) 202 is configured to fetch and execute computer-readable instructions stored in the memory 204. The electronics unit 122 may include a battery unit (Not shown in FIGURE). In an embodiment, the electronics unit 122 may be implemented remotely in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud, and the like.
The I/O interface device(s) 206 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface device(s) 206 may include a variety of software and hardware interfaces, for example, interfaces for peripheral device(s), such as a keyboard, a mouse, an external memory, a camera device, and a printer. Further, the I/O interface device(s) 206 may enable the system 200 to communicate with other devices, such as web servers and external databases. The I/O interface device(s) 206 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, local area network (LAN), cable, etc., and wireless networks, such as Wireless LAN (WLAN), cellular, or satellite. In an embodiment, the I/O interface device(s) 206 can include one or more ports for connecting a number of devices to one another or to another server.
The memory 204 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic random-access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, the memory 104 includes a plurality of modules 210 and a repository 212 for storing data processed, received, and generated by the plurality of modules 210. The plurality of modules 210 may include routines, programs, objects, components, data structures, and so on, which perform particular tasks or implement particular abstract data types.
Further, the database stores information pertaining to inputs fed to the electronics unit 122 and/or outputs generated (e.g., data/output generated at each stage of the data processing), specific to the methodology described herein. More specifically, the database stores information being processed at each step of the proposed methodology.
Additionally, the plurality of modules 210 may include programs or coded instructions that supplement applications and functions of the electronics unit 122. The repository 212, amongst other things, includes a system database 214 and other data 216. The other data 216 may include data generated as a result of the execution of one or more modules in the plurality of modules 210. Further, the database stores information pertaining to inputs fed to the electronics unit 122 and/or outputs generated by the system (e.g., at each stage), specific to the methodology described herein. Herein, the memory for example the memory 204, and the computer program code configured to, with the hardware processor for example the one or more processors 202, causes the electronics unit 122 to perform various functions described herein under.
FIG. 3A and FIG. 3B are exemplary flow diagrams illustrating a method 300 of monitoring the proprioception of the shoulder of the individual by the shoulder joint and girdle brace 100, according to an embodiment of the present disclosure. In an embodiment, the electronics unit 122 comprises one or more data storage devices or the memory 204 operatively coupled to the one or more hardware processors 202 and is configured to store instructions for execution of steps of the method by the one or more processors 202. The flow diagram depicted is better understood by way of following explanation/description. The steps of the method of the present disclosure will now be explained with reference to the components of the shoulder joint and girdle brace 100 as depicted in FIGS. 1A through 1D.
At step 302, a raw electromyography (EMG) signal is received from the shoulder joint and girdle brace 100 based on a muscle contraction in the individual. The raw EMG signal pertains to (i) one or more electrical signals, and (ii) one or more physio-electrical signals. The raw EMG signal is returned as an output at one or more sensor units 118A-C and 120A-D. The one or more sensor units 118A-C and 120A-D includes the one or more electromyography (EMG) sensors 120A-D, the one or more inertial measurement unit (IMU) sensors 118A-C, and the temperature sensor 116. In an embodiment, the temperature sensor 116 determines a local skin temperature around the joint occurs due to changes in a blood flow in that region which helps to determine condition of injury. At step 304, the raw EMG signal is preprocessed to obtain a normalized EMG signal. At step 304A, the raw EMG signal is processed at the filter unit to obtain a noise-filtered EMG signal. In an embodiment, the filter unit includes: (i) the Butterworth filter, and (ii) the Notch filter. At step 304B, the noise-filtered EMG signal is rectified with a half-wave rectifier and filtered to eliminate high frequency noise components in a frequency domain to obtain a filtered EMG signal. At step 304C, the rectified and the filtered EMG signal is normalized to extract one or more frequency features from a power spectrum and obtain the normalized EMG signal by applying a windowed power spectral density (PSD) technique. In an embodiment, the one or more frequency features pertains to (i) a root mean square (RMS) of the EMG signal, (ii) a mean power frequency (MPF), (iii) a median power frequency (MNF), (iv) total power (TP), and (v) a power spectral density (PSD). At step 306, the normalized EMG signal is processed to determine a muscle force associated with the individual. At step 308, the torque around one or more joints of the shoulder of the individual is estimated based on one or more phenomenological models. A muscle fatigue from a slope of regression of the muscle force extracted from the normalized EMG signal. At step 310, the parameter score is determined for one or more sensors associated with each joint of the shoulder based on the range of motion (ROM), and the torque around the one or more joints. At step 312, the performance index (PI) of the one or more joints of the shoulder of the individual is estimated based on the parameter score for the one or more sensors. At step 314, a muscle fatigue is computed from a slope of regression of the muscle force extracted from the normalized EMG signal. In an embodiment, the decreasing trend of the muscle force or negative value of the slope of regression is an indicative of a marker for the muscle fatigue. In an embodiment, the one or more phenomenological models are derived by the Hill-type muscle model.
The embodiment of the present disclosure herein addresses the unresolved problem of continuously monitoring the shoulder movement of the individual. The embodiment of the present disclosure also addresses challenges arising due to lack of dynamic muscular control which occur due to deficits in proprioception, i.e., as the glenohumeral joint's mechanism of providing feedback for reflexive muscular contraction. The embodiment of the present disclosure is capable of continuously monitoring proprioception of the shoulder of the individual by the shoulder joint and girdle brace. The embodiment of the present disclosure provides stability aid which decreases proprioceptive deficits for both patients after injury and players returning to sports. The shoulder joint and girdle brace is capable of continuously monitoring the shoulder movement of the individual’s by measuring the range of motion of the shoulder girdle which includes specifically the glenohumeral joint and the associated scapulothoracic and acromioclavicular joints. The embodiment further provides quantifies shoulder joint movement, and (b) individual glenohumeral and the scapulothoracic joints movement during the synchronous movement. The quantification of corresponding muscle recruitment, and strength for a given tasks is performed. The shoulder joint and girdle brace acts as a solution for both in-clinic quantitative and functional evaluation as well as real time monitoring of function during at home therapy sessions, in-clinic assessment of the shoulder before and after a procedure and physiotherapy sessions, and activities of daily living. The shoulder joint and girdle brace provides coordinated dynamic control of muscles corresponding to the shoulder joint assessment which add value to the clinical examination. The quantitative investigation of shoulder proprioception value is in shoulder arthroscopy, rotator cuff repairs, restoration of athletes to their prior level of activity, post-operative rehabilitation in total shoulder, and reverse shoulder arthroplasty. An extended indication would be using such an assessment in professional athletes to prevent injury by a tailored exercise regimen.
The features for the smart shoulder brace selection are (i) flexion and extension, (ii) abduction-adduction, (iii) medial and lateral rotation, (iv) local skin temperature, (v) muscle forces, (vi) skin conductance which varies with change of skin moisture by sweating, and (vii) temperature control modes. In addition, one or more features corresponds to: (i) handling movement artifact of muscle and skin that contributes to joint mobility, (ii) precisely, defining or quantifying the bones involved for an action i.e., (a) true glenohumeral mobility and how much the patient is compensating with other movements in the real-life scenarios, and (b) indications for shoulder reconstruction, to know what actual benefits have accrued by accurate monitoring of range of motion and quantification, and one or more roles of the shoulder joint and girdle brace. The shoulder joint and girdle brace provides coordinated dynamic control of muscles related to the shoulder movement. The correlation of the bones and corresponding muscle recruitment for a specific or defined activities are easily monitored. The shoulder joint and girdle brace is designed with a reusable and washable material with embedded sensor system and method of sensor fusion approach which overcome one or more challenges faced by the existing commercial brace. The shoulder joint and girdle brace is designed to determine a velocity of the motion, thereby provides quality and quantity of the motion. The quantity of the motion, and the EMG signal is combined to estimate the performance index.
The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g., any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g., hardware means like e.g., an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g., an ASIC and an FPGA, or at least one microprocessor and at least one memory with software processing components located therein. Thus, the means can include both hardware means, and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g., using a plurality of CPUs.
The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various components described herein may be implemented in other components or combinations of other components. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
It is intended that the disclosure and examples be considered as exemplary only, with a true scope of disclosed embodiments being indicated by the following claims.
, Claims:
1. A shoulder joint and girdle brace (100) to monitor a proprioception of shoulder of an individual, comprising:
a proprioceptive sensor unit (118A-C and 120A-D) to detect a linear and an angular movements of one or more joints of a shoulder; and
an electronics unit (122), wherein the electronics unit (122) comprises:
a memory (204) storing instructions;
one or more communication interfaces (206); and
one or more hardware processors (202) coupled to the memory (204) via the one or more communication interfaces (206), wherein the one or more hardware processors (202) are configured by the instructions to:
receive, a raw electromyography (EMG) signal from the shoulder joint and girdle brace based on a muscle contraction in the individual, wherein the raw EMG signal pertains to at least one of: (i) one or more electrical signals, (ii) one or more physio-electrical signals, wherein the raw EMG signal is returned as an output at one or more sensor units;
preprocess, the raw EMG signal to obtain a normalized EMG signal;
process, the normalized EMG signal to determine a muscle force associated with the individual;
estimate, a torque around the one or more joints of the shoulder of the individual based on one or more phenomenological models;
determine, a parameter score for one or more sensors associated with each joint of the shoulder based on a range of motion (ROM), and the torque around the one or more joints; and
estimate, a performance index (PI) of the one or more joints of the shoulder of the individual based on the parameter score for the one or more sensors.

2. The shoulder joint and girdle brace (100) as claimed in claim 1, wherein proprioceptive sensor unit (118A-C and 120A-D) comprises the one or more sensor units, and wherein the one or more sensor units corresponds to one or more electromyography (EMG) sensors (120A-D), one or more inertial measurement unit (IMU) sensors (118A-C), and a temperature sensor (116).

3. The shoulder joint and girdle brace (100) as claimed in claim 1, wherein the normalized EMG signal is obtained from the raw EMG signal, comprises:
(a) process, the raw EMG signal at a filter unit to obtain a noise-filtered EMG signal, wherein the filter unit comprises of (i) a Butterworth filter, and (ii) a Notch filter;
(b) process, the noise-filtered EMG signal with a half-wave rectifier to eliminate high frequency noise components in a frequency domain and to obtain a filtered EMG signal; and
(c) normalize, the rectified and the filtered EMG signal to extract one or more frequency features from a power spectrum and obtain the normalized EMG signal by applying a windowed power spectral density (PSD) technique.

4. The shoulder joint and girdle brace (100) as claimed in claim 3, wherein the one or more frequency features pertains to (i) a root mean square (RMS) of the EMG signal, (ii) a mean power frequency (MPF), (iii) a median power frequency (MNF), (iv) total power (TP), and (v) a power spectral density (PSD).

5. The shoulder joint and girdle brace (100) as claimed in claim 1, wherein the one or more hardware processors (202) are configured by the instructions to: compute, a muscle fatigue from a slope of regression of the muscle force extracted from the normalized EMG signal, wherein a decreasing trend of the muscle force or negative value of the slope of regression is an indicative of a marker for the muscle fatigue, and wherein the one or more phenomenological models is derived by a Hill-type muscle model.

6. A processor implemented method (300) for monitoring a proprioception of shoulder of an individual by a shoulder joint and girdle brace (100), the method comprising:
receiving, via one or more hardware processors, a raw electromyography (EMG) signal from the shoulder joint and girdle brace (100) based on a muscle contraction in the individual, wherein the raw EMG signal pertains to at least one of: (i) one or more electrical signals, (ii) one or more physio-electrical signals, wherein the raw EMG signal is returned as an output at one or more sensor units (302);
preprocessing, via the one or more hardware processors, the raw EMG signal to obtain a normalized EMG signal (304);
processing, via the one or more hardware processors, the normalized EMG signal to determine a muscle force associated with the individual (306);
estimating, via the one or more hardware processors, a torque around one or more joints of the shoulder of the individual based on one or more phenomenological models (308);
determining, via the one or more hardware processors, a parameter score for one or more sensors associated with each joint of the shoulder based on a range of motion (ROM), and the torque around the one or more joints (310); and
estimating, via the one or more hardware processors, a performance index (PI) of the one or more joints of the shoulder of the individual based on the parameter score for the one or more sensors (312).

7. The processor implemented method (300) as claimed in claim 6, wherein proprioceptive sensor unit (118A-C and 120A-D) comprises the one or more sensor units, and wherein the one or more sensor units corresponds to one or more electromyography (EMG) sensors (120A-D), one or more inertial measurement unit (IMU) sensors (118A-C), and a temperature sensor (116).

8. The processor implemented method (300) as claimed in claim 6, wherein the normalized EMG signal is obtained from the raw EMG signal, comprises:
(a) processing, via the one or more hardware processors, the raw EMG signal at a filter unit to obtain a noise-filtered EMG signal, wherein the filter unit comprises of (i) a Butterworth filter, and (ii) a Notch filter (304A);
(b) processing, via the one or more hardware processors, the noise-filtered EMG signal with a half-wave rectifier to eliminate high frequency noise components in a frequency domain and to obtain a filtered EMG signal (304B); and
(c) normalizing, via the one or more hardware processors, the rectified and the filtered EMG signal to extract one or more frequency features from a power spectrum and obtain the normalized EMG signal by applying a windowed power spectral density (PSD) technique (304C).

9. The processor implemented method (300) as claimed in claim 8, wherein the one or more frequency features pertains to (i) a root mean square (RMS) of the EMG signal, (ii) a mean power frequency (MPF), (iii) a median power frequency (MNF), (iv) total power (TP), and (v) a power spectral density (PSD).

10. The processor implemented method (300) as claimed in claim 6, further comprising, computing, via the one or more hardware processors, a muscle fatigue from a slope of regression of the muscle force extracted from the normalized EMG signal (314), wherein a decreasing trend of the muscle force or negative value of the slope of regression is an indicative of a marker for the muscle fatigue, and wherein the one or more phenomenological models are derived by a Hill-type muscle model.