DESC:FIELD OF INVENTION
[0001] The embodiments of the present disclosure generally relate to systems and methods for providing posture analysis. More particularly, the present disclosure relates to a system and a method for analyzing postures and generating recommendations.

BACKGROUND
[0002] The following description of the related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section is used only to enhance the reader's understanding of the present disclosure and not as an admission of the prior art.
[0003] Exercises are beneficial in prevention and treatment of musculoskeletal problems. However, they can also be potentially dangerous if performed incorrectly. Exercise mistakes are made when the user does not use the proper form or pose. Dynamic posture assessment may be necessary to provide a detailed assessment to the user based on the type of exercises performed by the user. Conventional systems lack accurate tracking and feedback that aid in providing a detailed assessment to the user based on the exercises.
[0004] There is, therefore, a need in the art to provide a system and a method that can mitigate the problems associated with the prior arts.

OBJECTS OF THE INVENTION
[0005] Technical advantage provided by various implementations of the system and method are evident from the herein disclosure. Some of the objects/advantages of the present disclosure, which at least one embodiment herein satisfies, are listed below.
[0006] It is an object of the present disclosure to provide a system and a method that provides users with feedback in real-time based on their form and posture during a physical exercise.
[0007] It is an object of the present disclosure to provide a system and a method that provides recommendations to the user on a required number of repetitions during the physical exercise.
[0008] It is an object of the present disclosure to provide a system and a method that provides a stage-wise report to a health care provider (HCP) to enable an assessment based on the user’s physical exercise.
[0009] It is an object of the present disclosure to provide a system and a method that provides real-time feedback to the users during physical activity using angle tracking and determines whether the user has reached a certain benchmark to consider proper repetitions and postures while performing the exercises.
[0010] It is an object of the present disclosure to provide a system and a method that provides real-time feedback on specific joints that are not actively performing the physical exercise and corrects the user's posture to enable the user to acquire an efficient form while performing physical exercises.
[0011] It is an object of the present disclosure to provide a system and a method that tracks a number of repetitions and a range of motion associated with the user during physical exercise, enabling the system to enhance their decision-making.
[0012] It is an object of the present disclosure to provide a system and a method that provides recommendations to the users irrespective of gender, body shape, and surroundings.
[0013] It is an object of the present disclosure to provide a system and a method that enhances user experience while performing the physical exercise and provides motivation to the users to maintain a healthy lifestyle.
[0014] It is an object of the present disclosure to provide a system and a method for assigning a movement tag that allows to personalize the exercise routines of a user. The assigned movement tag is dependent on a combination of parameters and the user's response.
[0015] It is an object of the present disclosure to provide a system and a method that precisely measures an outcome in terms of improvement in movement restriction of the user.
[0016] It is an object of the present disclosure to provide a system and a method that correlates the improvement in the user's movement restriction with the level of adherence and types of exercises suggested to the user in order to improve the level of personalization of the exercise routine for the sessions to follow.
[0017] It is an object of the present disclosure to provide a system and a method that is designed to recommend a change in the level of exercise plan associated with one or more suggested exercise routines to ensure minimal injuries or to minimize the risk of adverse health effects.
[0018] It is an object of the present disclosure to provide a system and method that recommends the change in the level of exercise plan, when it estimates that the user is ready to perform the next level of the recommended exercise plan.
[0019] It is an object of the present disclosure to provide a system and a method that predicts a failure or success rate associated with the user performing one or more of the suggested postures and thereby assists in real-time personalization of repetitions or modifications to the recommended exercise or exercises routine(s) or postures.
[0020] It is an object of the present disclosure to provide a system and a method that predicts the failure or success rate associated with the user performing one or more of the suggested postures and thereby assists in real-time adjustment in the current one or more exercise routine(s) or postures from the rest of the exercises that the user is to perform on a day.

SUMMARY
[0021] This section introduces certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.
[0022] In an aspect, the present disclosure relates to a system for analyzing postures and generating recommendations. The system includes a processor and a memory operatively coupled with the processor, where said memory stores instructions which, when executed by the processor, cause the processor to receive one or more inputs from a user associated with a computing device. The one or more inputs are based on the clinical profile of the user. Through the computing device, the processor recommends one or more exercise routines personalized to the user. The processor receives a request from the user through the computing device to analyze one or more postures or angles performed by the user during one or more exercise routines for a predetermined period. The processor analyses, via an artificial intelligence (AI) engine or a recommendation engine, one or more postures or angles the user performs for the predetermined period. The processor generates a recommendation associated with a new or a modified routine to the user based on the analyzed one or more postures or angles and the clinical profile of the user. The processor causes to display, via the computing device, the recommendation to the user to enable the user to modify the one or more postures as a corrective measure and improve the repetitions performed by the user.
[0023] In an embodiment, the clinical profile may include any or a combination of a demographic factor associated with the user, pain location of the user, pain duration and intensity associated with the user, a trauma history associated with the user, a surgical history related to the user, one or more aggravating and relieving factors associated with the user, pain radiation associated with the user, one or more active range of motions (AROM) associated with the user, and a lifestyle and an occupation details related to the user.
[0024] In an embodiment, the processor may determine at least one transition between one movement state to another movement state performed by the user during the one or more exercise routines for generating the recommendation.
[0025] In an embodiment, the processor may analyze, via the AI engine, said at least one transition as a digital biomarker and compares said at least one transition with standard routine information stored in a database to detect similarities and dissimilarities between said at least one transition and the standard routine information. Wherein, a range of motion (ROM) associated with said at least one transition may be determined as the said digital biomarker.
[0026] In an embodiment, by analyzing the one or more postures, the processor may simultaneously generate and provide one or more movement tags to the user based on said at least one transition and a response from the user. The one or more movement tags may be based on one or more restricted movements associated with the user's musculoskeletal condition recorded during the one or more exercise routines.
[0027] In an embodiment, the processor may analyze parameters selected from one or more aggravating or relieving factors or one or more movements associated with one or more joints of the user, a severity of movement restriction associated with the one or more joints of the user, and the AROM associated with the one or more joints of the user to simultaneously generate the one or more movement tags for the user.
[0028] In an embodiment, the processor may receive feedback from the user associated with the one or more movements, and generate the recommendation for the user to provide a corrective measure and/or feedback tag based on the said feedback. The one or more tags may be based on identified one or more improvements or changes in the restricted movements associated with the musculoskeletal condition of the user recorded during the one or more exercise routines.
[0029] In an embodiment, the processor may analyze parameters selected from one or more movements and the AROM during said at least one transition, pain duration, and an intensity associated with the pain duration, a pain radiation associated with the intensity, and generate the recommendation of the new or the modified routine to the user based on the analysis.
[0030] In an embodiment, the processor may analyze at least one of a flexibility parameter, a mobility parameter and an AROM parameter associated with the one or more joints of the user, a strength parameter of the user recorded during said at least one transition, and the one or more aggravating or relieving factors associated with the user for recommending change in a level of exercise plan that associates one or more exercise routines personalized to the user.
[0031] In an embodiment, the one or more exercises or exercise routines of the exercise plan may be classified by the processor based on attributes selected from one or more of the flexibility, mobility, strength, stability, intensity and an orientation associated with a musculoskeletal condition.
[0032] In an embodiment, the one or more exercise or exercise routine(s) of the exercise plan are personalized or customized to the user by the processor based on the combination of attributes selected from one or more of the flexibility, mobility, strength, stability, an intensity and an orientation associated with a musculoskeletal condition of the user.
[0033] In an aspect, the present disclosure relates to a method for analyzing postures and generating recommendations. The method may include receiving, by a processor associated with a system, one or more inputs from a user associated with a computing device, where the one or more inputs are based on a clinical profile of the user. The method may include recommending, by the processor, through the computing device, one or more exercise routines personalized to the user. The method may include receiving, by the processor, a request from the user through the computing device for analyzing one or more postures performed by the user during the one or more exercise routines for a predetermined period. The method may include analyzing, by the processor, via an AI engine, the one or more postures performed by the user for the predetermined period. The method may include generating, by the processor, a recommendation associated with a new or a modified routine to the user based on the analyzed one or more postures and the clinical profile of the user. The method may include causing to display, by the processor, via the computing device, the recommendation to the user to enable the user to modify the one or more postures as a corrective measure and improve the repetitions performed by the user.
[0034] In an embodiment, the method may include determining, by the processor, at least one transition between one movement state to another movement state performed by the user during the one or more exercise routines for generating the recommendation.
[0035] In an embodiment, the method may include analyzing, by the processor via the AI engine, said at least one transition as a digital biomarker and comparing said at least one transition with standard routine information stored in a database to detect similarities and dissimilarities between said at least one transition and the standard routine information, wherein a range of motion (ROM) associated with at least one transition is determined as the said digital biomarker.
[0036] In an embodiment, the method may include simultaneously generating, by the processor, via the AI engine, upon analyzing the one or more postures and providing one or more movement tags to the user based on said at least one transition and a response from the user. The one or more movement tags may be based on one or more restricted movements associated with musculoskeletal condition of the user recorded during the one or more exercise routines or postures.
[0037] In an embodiment, the method may include analyzing, by the processor, parameters selected from one or more aggravating or relieving factors or one or more movements associated with one or more joints of the user, a severity of movement restriction associated with the one or more joints of the user or the AROM associated with the one or more joints of the user to simultaneously generate the one or more movement tags for the user.
[0038] In an embodiment, the method may include receiving, by the processor, a feedback from the user associated with the one or more movements, and generates the recommendation such as the corrective measure and/or a feedback tag based on the said feedback for the user. The one or more tags may be based on identified one or more improvements, changes in the restricted movements associated with musculoskeletal condition of the user recorded during the one or more exercise routines and response given by the user for a set of questions.
[0039] In an embodiment, the method may include analyzing, by the processor, parameters selected from one or more movements and the AROM during said at least one transition, the pain duration, and an intensity associated with the pain duration, a pain radiation associated with the intensity, and generating the recommendation of the new or modified routine to the user based on the analysis.
[0040] In an embodiment, the method may include analyzing, by the processor, at least one of a flexibility parameter, a mobility parameter and an AROM parameter associated with the one or more joints of the user, a strength parameter of the user recorded during said at least one transition, and the one or more aggravating or relieving factors associated with the user for recommending change in a level of exercise plan that associates one or more exercise routines personalized to the user.
[0041] In an embodiment, the method may include classifying, by the processor, the one or more exercises or exercise routines of exercise plan based on attributes selected from the flexibility, mobility, strength, stability, an intensity and an orientation associated with a musculoskeletal condition.
[0042] In an embodiment, the one or more exercise or exercise routine(s) of the exercise plan are personalized or customized to the user by the processor based on the combination of attributes selected from one or more of the flexibility, mobility, strength, stability, an intensity and an orientation associated with a musculoskeletal condition of the user.
[0043] In an embodiment, a method may include predicting, by the processor, via the AI engine, a failure or a success rate associated with the one or more postures performed by the user during the one or more exercise routines for the predetermined period.
[0044] In an aspect, the present disclosure relates to a method for predicting a user performance during a routine. The method includes receiving, by a processor associated with a system, one or more inputs from a user. The one or more inputs are based on a clinical profile of the user. The method includes recommending, by the processor, through the computing device, one or more exercise routines personalized to the user. The method includes receiving, by the processor, a request from the user through the computing device for analyzing one or more postures performed by the user during the one or more exercise routines for a predetermined period. The method includes detecting, by the processor, at least one transition between one movement state to another movement state performed by the user during the predetermined period. The method includes comparing, by the processor, one or more movements or postures associated with said at least one transition performed by the user during the predetermined period. The method includes predicting, via a by the processor, via a trained model, a success or failure rate associated with the one or more movements or postures during the predetermined period. The method includes recommending, by the processor, via the trained model, a new and/or a modified routine associated with said at least one transition during the predetermined period.
[0045] In an embodiment, the method may include generating a new and/or modified routine based on the prediction of the success or failure rate of the exercise routine performed by the user, via the trained model i.e. an AI engine.
[0046] In an embodiment, the method may include estimating, by the processor, a range of motion (ROM) associated with the user during said at least one transition for the predetermined period.
[0047] In an embodiment, the method may include predicting, by the processor, via the trained model, the success or failure rate in achieving the estimated range of motion associated with the user during said at least one transition.
[0048] In an embodiment, the method may include displaying, via a user interface of the computing device, the recommendation to enable the user to utilize the recommendation as a corrective measure to modify the one or more postures and/or improve the repetitions performed by the user.

BRIEF DESCRIPTION OF DRAWINGS
[0049] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes the disclosure of electrical components, electronic components, or circuitry commonly used to implement such components.
[0050] FIG. 1 illustrates an example network architecture (100) for implementing a proposed system (108), in accordance with an embodiment of the present disclosure.
[0051] FIG. 2 illustrates an example block diagram (200) of a proposed system (108), in accordance with an embodiment of the present disclosure.
[0052] FIG. 3 illustrates an example method diagram (300) for implementing the proposed system (108), in accordance with an embodiment of the present disclosure.
[0053] FIG. 4 illustrates an example block diagram (400) for implementing the proposed system (108), in accordance with an embodiment of the present disclosure.
[0054] FIG. 5 illustrates an example method diagram (500) for generating movement tags while recommending a new or a modified routine to the user, in accordance with an embodiment of the present disclosure.
[0055] FIG. 6 illustrates an example method diagram (600) for predicting performance of the user during a routine, in accordance with an embodiment of the present disclosure.
[0056] FIG. 7 illustrates an example method diagram (700) for analyzing user postures and predicting the user performance during the routine, in accordance with an embodiment of the present disclosure.
[0057] FIG. 8 illustrates an example computer system (800) in which or with which embodiments of the present disclosure may be implemented.
[0058] The foregoing shall be more apparent from the following more detailed description of the disclosure.

DEATILED DESCRIPTION
[0059] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.
[0060] The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.
[0061] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.
[0062] Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[0063] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements.
[0064] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0065] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.
[0066] The various embodiments throughout the disclosure will be explained in more detail with reference to FIG’s. 1-8.
[0067] FIG. 1 illustrates an example network architecture (100) for implementing a proposed system (108), in accordance with an embodiment of the present disclosure.
[0068] As illustrated in FIG. 1, the network architecture (100) may include a system (108). The system (108) may be connected to one or more computing devices (104-1, 104-2…104-N) via a network (106). The one or more computing devices (104-1, 104-2…104-N) may be interchangeably specified as a user equipment (UE) (104) and be operated by one or more users (102-1, 102-2...102-N). Further, the one or more users (102-1, 102-2…102-N) may be interchangeably referred as a user (102) or users (102). Further, the system (108) may include an artificial intelligence (AI) engine (for example, 214 of FIG. 2) for processing various inputs provided by the users (102).
[0069] In an embodiment, the computing devices (104) may include, but not be limited to, a mobile, a laptop, etc. Further, the computing devices (104) may include a smartphone, virtual reality (VR) devices, augmented reality (AR) devices, a general-purpose computer, desktop, personal digital assistant, tablet computer, and a mainframe computer. Additionally, input devices for receiving input from the user (102) such as a touch pad, touch-enabled screen, electronic pen, and the like may be used. A person of ordinary skill in the art will appreciate that the computing devices (104) may not be restricted to the mentioned devices and various other devices may be used.
[0070] In an embodiment, the network (106) may include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. The network (106) may also include, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof.
[0071] In an embodiment, the system (108) may receive one or more inputs from a user (102) associated with a computing device (104), wherein the one or more inputs may be based on a clinical profile of the user (102). The clinical profile may include, but not limited to, any or a combination of a demographic factor associated with the user (102), a pain location of the user (102), a pain duration and intensity associated with user (102), a trauma history associated with the user (102), a surgical history associated with the user (102), one or more aggravating and relieving factors associated with the user (102), a pain radiation associated with the user (102), one or more active range of motions associated with the user (102), and a lifestyle and occupation details associated with the user (102).
[0072] In an embodiment, the demographic factor associated with the user (102) may include, but not limited to, age, gender, weight, allergies, occupation, address, contact details, insurance information, medical history or some combination thereof.
[0073] In an embodiment, the pain location associated with the user (102) may include, but not limited to, low back pain, neck pain, and knee pain.
[0074] In an embodiment, the pain duration and intensity associated with user (102) may be captured by recording few details, but not limited to, severity of pain episode (scale 0-10), time period of the pain (ranging from months to years) and including current episodes of pain or some combination thereof.
[0075] In an embodiment, a trauma history associated with the user (102) may include, but not limited to, an initial occurrence of a current pain episode or a worsened pain condition after an injury, an injection or surgery or some combination thereof.
[0076] In an embodiment, the surgical history associated with the user (102) may include, but not limited to, present and past surgical histories and a medical history thereof.
[0077] In an embodiment, the pain radiation associated with the user (102) may be explained with an example. In the shoulder, for example, pain from the acromioclavicular joint is usually felt in that joint, whereas pain from the glenohumeral joint or rotator cuff is usually felt in the upper arm. Pain from the knee may be felt in the knee but can sometimes be felt in the hip or the ankle. Pain due to irritation of a nerve may be felt in the distribution of the nerve (for example, as in sciatica).
[0078] In an embodiment, the range of motion (ROM) associated with the user is defined as the capability of a joint to go through its complete spectrum of movements. Measurement of range of motion can be used to evaluate available motion, determine joint stability, and determine soft-tissue elasticity as well as response to therapy over time.
[0079] In an embodiment, the Active range of motion (AROM) associated with the user is defined as the range of motion that can be achieved when opposing muscles contract and relax, resulting in joint movement. For example, the active range of motion to allow the elbow to bend requires the biceps to contract while the triceps muscle relaxes.
[0080] In an embodiment, one or more aggravating factors associated with the user may refer to factors that worsen the pain and one or more relieving factors associated with the user may refer to factors that reduce the pain.
[0081] In an embodiment, for example, the user(s) (102) may experience musculoskeletal pain due to a past or a present medical condition and may require multiple recommendations on postures performed during a physical exercise. Additionally, the user(s) (102) may request for feedback about a number of repetitions required during the physical exercise, among other such requests within the scope of the present disclosure. The system (108) may recommend through the computing device (108), one or more exercise routines personalized to the user (102).
[0082] In an embodiment, the one or more exercise or exercise routine(s) associated with an exercise plan may be classified by the system (108) based on attributes selected from one or more of the flexibility, mobility, strength, stability, an intensity, and an orientation associated with the musculoskeletal condition.
[0083] In an embodiment, the one or more exercise or exercise routine(s) of the exercise plan are personalized or customized to the user by the processor based on the combination of attributes selected from one or more of the flexibility, mobility, strength, stability, an intensity and an orientation associated with a musculoskeletal condition of the user.
[0084] In an embodiment, the system (108) may receive a request from the user (102) through the computing device (104) for analyzing one or more postures or angles performed by the user (102) during the one or more exercise routines for a predetermined period. The system (108) may determine at least one transition between one movement state to another movement state performed by the user (102) during the one or more exercise routines for generating the recommendation.
[0085] In an embodiment, the system (108) may analyze, via the artificial intelligence (AI) engine (214), the one or more postures performed by the user (102) for the predetermined period. The system (108) may analyze, via the AI engine (214), said at least one transition as a digital biomarker and compare said at least one transition with standard routine information stored in a database (210) to detect similarities and dissimilarities between said at least one transition and the standard routine information, wherein the range of motion associated with at least one transition may be determined as the said digital biomarker.
[0086] In an embodiment, the (AI) engine (214) may use a treatment-based classification system that considers the user's (102) clinical profile and symptomatic movement restriction. The treatment-based classification system may be developed on McKenzie's principles. The McKenzie classification system, supported by evidence, highlights the importance of following postural exercises tailored to the nature of symptoms, especially during chronic presentations.
[0087] In an embodiment, the treatment-based classification system may refer to a method used in physical therapy and rehabilitation to categorize patients based on their specific signs, symptoms, and clinical presentation. The classification system may aid in determining a most appropriate treatment or recommend one or more interventions for individual patients/users (102). This classification system may also help in improving treatment outcomes by matching patients with treatments that are most likely to be effective for their condition.
[0088] In an embodiment, by analyzing the one or more postures or angles, the system (108) may simultaneously generate and provide one or more movement tags to the user (102) based on said at least one transition and a response from the user (102). The one or more movement tags may be based on one or more restricted movements associated with the musculoskeletal condition of the user (102) recorded during the one or more exercise routines. The system (108) may also record one or more medical conditions associated with the user (102).
[0089] In an embodiment, the movement tag is a labelling or tagging assigned to the user based on one or more restricted movements associated with the musculoskeletal condition of the user. The movement tags as per the invention may include, but not limited to, restricted flexion, no restriction, restricted flexion and extension, and restricted extension.
[0090] In an embodiment, the system (108) may create a personalized exercise plan for the user based on the initial movement tag and other attributes of the questionnaire. Every personalized exercise plan may include a set of exercises classified or alloted based on combination of attributes selected from the flexibility, mobility, strength stability, intensity and an orientation. These set of exercises are to be performed by the user based on the level of exercise plan assigned to the user.
[0091] In an embodiment, the system (108) may analyze parameters selected from a group consisting of one or more aggravating or relieving factors or movements associated with one or more joints of the user (102), a severity of movement restriction associated with one or more joints of the user (102) or an active range of motions (AROM) associated with the one or more joints of the user (102) to simultaneously generate the one or more movement tags for the user (102). For example, the system (108) may cause to ask the user (102) if pain originates during said at least one transition. The at least one transition may include, but not limited to, bending down, arching up, bending sideways towards a right side, or bending sideways towards a left side. The system (108) may record a response from the user (102) based on the origin of pain and also record an implication on the body of the user (102) based on the pain while performing the one or more exercise routines. Then, the system (108) may generate the one or more movement tags associated with the said at least one transition. The one or more movement tags may include, but are not limited to, restricted flexion, no restriction, restricted flexion and extension, and restricted extension.
[0092] In an embodiment, the system (108) may have a set of questions designed for each joint for example lower back, neck and knee to access the pain of the user. For clarity, there will be a separate set of questions with varying parameters and logic of creation of exercise plan and to determine the progression for different joints.
[0093] In an embodiment, the system (108) may share a questionnaire to clinically assess the pain intensity of the user (102). For example, during the clinical assessment, a pain scale ranging from 0 to 10 may be shared with the user (102) to receive an input associated with a pain response of the user (102). Subsequently, the exercise plan is created and shared daily before or after performing the exercise routine, and the user (102) is asked to share their response on a pain scale ranging from 0 to 10. If the user (102) enters a number that is more than 7 on the pain scale, then the user (102) may be recommended to take rest for that particular day or not to perform the exercises today.
[0094] In an embodiment, the system (108) has limited access to physiotherapist, as it has a built in alerting system that recommends the user to opt to visit in person the clinic or health care providers if the condition of the user (102) is worsening.
[0095] In an embodiment, for example, if the user is suffering from back pain, a specific joint, such as the Lumbar spine of the user (102), is analyzed by the system (108) by considering certain parameters selected from aggravating or relieving factors and the AROM. The system (108) may cause to ask questions to the user (102) and record the response by analyzing one of the parameters AROM associated with the musculoskeletal condition of the user (102). The questions may include a request for performing one or more postures that may include but are not limited to looking back from the right side, looking back from the left side, moving hips to the right side, and moving hips to the left side, etc. Based on indication of the origin of pain during analyses of the AROM associated with one or more joints of the user (102), the system (108) may generate one or more movement tags that allow the personalization of the exercise routine.
[0096] In an embodiment, the system (108) may consider one or more first parameters associated with a specific joint of the user (102) while assigning the one or more movement tags. The one or more first parameters may include, but not limited to, Aggravating and Relieving factors and Active range of motion (AROM). Further, the system (108) may also consider one or more second parameters associated with the specific joint of the user (102) while assigning the one or more movement tags. The one or more second parameters may include, but are not limited to, age, a trauma tag, a pain rating, a duration associated with the pain/symptom, and pain radiation to generate a movement tag associated with the specific joint of the user (102).
[0097] For example, the system (108) may generate a final movement tag associated with the joint, namely the lumbar spine, by analyzing the parameters associated with the aggravating factor, the relieving factor, and the AROM and further assign a movement tag to each parameter. Based on the movement tags assigned to each of the parameters, a final movement tag may be generated. Based on the initial movement restriction and other attributes in the questionnaire, the system (108) may generates a personalized exercise plan for the user (102). Every personalized exercise plan may include a set of exercises to be performed by the user (102).
[0098] In an embodiment, the system (108) generates a final movement tag based on the one or more first and second parameters is as shown in Table 1.
Aggravating Factor Tag Relieving Factor Tag AROM Tag Final Movement Tag
Restricted Flexion Restricted Flexion Restricted Flexion Restricted Flexion
Restricted Flexion Restricted Flexion Restricted Extension Restricted Flexion
Restricted Flexion Restricted Flexion Both Flexion and Extension Restricted Restricted flexion
Restricted Extension Restricted Extension Restricted Flexion Restricted Extension
Restricted Extension Restricted Extension Restricted Extension Restricted Extension
Restricted Extension Restricted Extension Both Flexion and Extension Restricted Restricted Extension
Restricted Extension Restricted Extension No Restriction Restricted Extension
Both Flexion and Extension Restricted Both Flexion and Extension Restricted Restricted Flexion Restricted Flexion
Both Flexion and Extension Restricted Both Flexion and Extension Restricted Restricted Extension Restricted Extension
Both Flexion and Extension Restricted Both Flexion and Extension Restricted Both Flexion and Extension Restricted Both Flexion and Extension Restricted
Both Flexion and Extension Restricted Both Flexion and Extension Restricted No Restriction Both Flexion and Extension Restricted
No Restriction Restricted Flexion Restricted Flexion Restricted Flexion
No Restriction Restricted Flexion Restricted Extension Restricted Extension
No Restriction Restricted Flexion Both Flexion and Extension Restricted Both Flexion and Extension Restricted
No Restriction Restricted Flexion No Restriction No Restriction
Restricted Extension No Restriction Restricted Flexion Both Flexion and Extension Restricted
Restricted Extension No Restriction Restricted Extension Restricted Extension
Restricted Extension No Restriction Both Flexion and Extension Restricted Both Flexion and Extension Restricted
Restricted Extension No Restriction No Restriction Restricted Extension
Table 1
[0099] In an embodiment, the system (108) may receive a feedback from the user (102) associated with one or more movements based on the recommended one or more exercise routines. The system (108) may provide the corrective measures and/or feedback tags based on the feedback received from the user (102). The one or more tags may be based on identification of one or more improvements, changes in the restricted movements associated with the musculoskeletal condition of the user (102) recorded during the routine. The system (108) may simultaneously generate the one or more feedback tags for the user (102) by analysing and receiving the response of the parameters selected from one or more aggravating or relieving factors or the one or more movements, a severity of movement restriction, and the AROM associated with the one or more joints of the user (102).
[00100] In an embodiment, the feedback tag is a labelling or tagging assigned to the user based on identifying one or more improvements, changes in the restricted movements associated with musculoskeletal condition of the user. The feedback tag as per the invention may include, but not limited to, Improved, Worsened, No change, etc.
[00101] In an embodiment, the system (108) may send a reminder or pops a screen with multiple questions at a specific duration include, but not limited to, every week or consecutive weeks. The user then responds to the questionnaire (one or more parameters such as pain radiation, pain intensity, and Range of motion), and this response is stored. After periodical responses, the feedback is assessed by comparing one or more responses and generate one or more feedback tags (Improved, Worsened, no change, etc.). The generated tag helps in assessing the current progress of the user and generates a corrective measure to modify at least one transition.
[00102] In an embodiment, the system (108) may generate a feedback tag based on the assessment of multiple responses received from the user (102) is as shown in Table 2.
P1 Radiating to leg Response in a previous assessment Response in the current assessment Tag
Yes No Improved
No Yes Worsened
Yes Yes No Change
No No Improved
P2 Pain Intensity Any change < 2 points
Pain intensity reduces by >= 2 points Improved
Pain intensity increases by >= 2 points Worsened
P3 AROM The range of movement has improved from a previous session Improved
The range of movement has reduced from the previous session Worsened
The range of motion stays the same No Change
Table 2
[00103] Further, in an embodiment, the system (108) may generate a recommendation associated with a new or modified routine to the user (102) based on the analyzed one or more postures or angles and the clinical profile of the user (102). Further, the system (108) may receive a feedback from the user (102) associated with the one or more movements, and generate the recommendation for the user (102) to provide the corrective measure and/or feedback tag based on the said feedback. The one or more tags may be based on identifying one or more improvements or changes in the restricted movements associated with the musculoskeletal condition of the user (102) recorded during the routine. The system (108) may analyze at least one of the parameters selected from the one or more AROM during said at least one transition, the pain duration and intensity associated with the user (102), the pain radiation associated with the user (102), and generate the recommendation of the new or the modified routine to the user (102) based on the analysis.
[00104] In an embodiment, the system (108) may analyze at least one of the flexibility parameter, the mobility parameter, the AROM parameter associated with the one or more joints of the user (102), the strength parameter of the user (102) recorded during said at least one transition, and the one or more aggravating or relieving factors associated with the user (102) for recommending change in the one or more exercise routines of the user (102).
[00105] In an embodiment, the system (108) may cause to display, via the computing device (104) the recommendation to the user (102) to enable the user to modify the one or more postures as a corrective measure and improve the repetitions performed by the user (102). In an embodiment, the system (108) may receive inputs from a health care provider (HCP) for recommending changes in one or more exercise routines of the user (102).
[00106] In an embodiment, the HCP may include, but not limited to, orthopedic specialists and physiotherapists, who may assess an output from the system (108) and provide an additional feedback in real-time to the users (102) based on their postures during the physical exercise routine. The HCP may also set up tolerances and ranges for the patient/user (102) to perform each physical exercise and provide a customized/personalized solution to the user (102).
[00107] Although FIG. 1 shows exemplary components of the network architecture (100), in other embodiments, the network architecture (100) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 1. Additionally, or alternatively, one or more components of the network architecture (100) may perform functions described as being performed by one or more other components of the network architecture (100).
[00108] FIG. 2 illustrates an example block diagram (200) of a proposed system (108) in accordance with an embodiment of the present disclosure.
[00109] Referring to FIG. 2, the system (108) may comprise one or more processor(s) (202) that may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that process data based on operational instructions. Among other capabilities, the one or more processor(s) (202) may be configured to fetch and execute computer-readable instructions stored in the memory (204) of the system (108). The memory (204) may be configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory (204) may comprise any non-transitory storage device including, for example, volatile memory such as random-access memory (RAM), or non-volatile memory such as erasable programmable read-only memory (EPROM), flash memory, and the like.
[00110] In an embodiment, the system (108) may include an interface(s) (206). The interface(s) (206) may comprise a variety of interfaces, for example, interfaces for data input and output (I/O) devices, storage devices, and the like. The interface(s) (206) may also provide a communication pathway for one or more components of the system (108). Examples of such components include, but are not limited to, processing engine(s) (208) and a database (210), where the processing engine(s) (208) may include, but not be limited to, a data parameter engine (212), an AI engine (214), and other engine(s) (216). In an embodiment, the other engine(s) (216) may include, but not limited to, a data management engine, an input/output engine, and a notification engine.
[00111] In an embodiment, the processing engine(s) (208) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) (208). In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) (208) may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) (208) may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) (208). In such examples, the system (108) may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system (108) and the processing resource. In other examples, the processing engine(s) (208) may be implemented by electronic circuitry.
[00112] In an embodiment, the processor (202) may receive one or more inputs via the data parameter engine (212). The one or more inputs may be based on a clinical profile of the user (102). The processor (202) may record the one or more inputs in the database (210). The clinical profile may include but not limited to any or a combination of a demographic factor associated with the user (102), a pain location of the user (102), a pain duration and intensity associated with the user (102), a trauma history associated with the user (102), a surgical history associated with the user (102), one or more aggravating and relieving factors associated with the user (102), a pain radiation associated with the user (102), one or more active range of motions associated with the user (102), and a lifestyle and occupation details associated with the user (102). As an example, the users (102) may experience musculoskeletal pain due to a past or a present medical condition and may require multiple recommendations on postures performed during the physical exercise. Additionally, the users (102) may request for feedback about a number of repetitions required during the physical exercise, among other such requests within the scope of the present disclosure. The processor (202) may recommend through the computing device (104), one or more exercise routines personalized to the user (102).
[00113] In an embodiment, the one or more exercise or exercise routine(s) of an exercise plan may be classified by the system (108) based on the attributes selected from the flexibility, mobility, strength, stability, intensity, and orientation associated with the musculoskeletal condition.
[00114] In an embodiment, the processor (202) may receive a request from the user (102) through the computing device (104) to analyze one or more postures performed by the user (102) during one or more exercise routines for a predetermined period. The processor (202) may determine at least one transition between one movement state to another movement state performed by the user (102) during the one or more exercise routines for generating the recommendation.
[00115] In an embodiment, the processor (202) may analyze, via an AI engine (214), the one or more postures performed by the user (102) for the predetermined period. The processor (202) may analyze via the AI engine (214), said at least one transition as a digital biomarker and compare said at least one transition with standard routine information stored in the database (210) to detect similarities and dissimilarities between said at least one transition and the standard routine information. Wherein, a range of motion associated with at least one transition may be determined as the said digital biomarker.
[00116] In an embodiment, the AI engine (214) may use a treatment-based classification system that considers the user's (102) clinical profile and symptomatic movement restriction. The treatment-based classification system may be developed on McKenzie principles. The McKenzie classification system, supported by evidence, highlights the importance of following postural exercises tailored to the nature of symptoms, especially during chronic presentations.
[00117] In an embodiment, by analyzing the one or more postures or angles, the processor (202) may simultaneously generate and provide one or more movement tags to the user (102) based on said at least one transition and a response from the user (102). The one or more movement tags may be based on one or more restricted movements associated with the musculoskeletal condition of the user (102).
[00118] In an embodiment, the processor (202) may analyze at least one of parameters selected from one or more aggravating or relieving factors or movements associated with one or more joints of the user (102), a severity of movement restriction associated with one or more joints of the user (102) and an active range of motions (AROM) associated with the one or more joints of the user (102) to simultaneously generate the one or more movement tags for the user (102).
[00119] Further, in an embodiment, the processor (202) may generate a recommendation associated with a new or modified routine to the user (102) based on the analyzed one or more postures and the clinical profile of the user (102). Further, the processor (202) may receive a feedback from the user (102) associated with the one or more movements, and generate the recommendation for the user (102) to provide the corrective measure and/or feedback tag based on the said feedback. The one or more feedback tags may be based on identifying one or more improvements, changes in the restricted movements associated with musculoskeletal condition of the user (102) recorded during the routine. The processor (202) may analyze at least one of the parameters selected from the one or more AROM during said at least one transition, the pain duration, and intensity associated with user (102), the pain radiation associated with the user (102), and generate the recommendation of the new or the modified routine to the user (102) based on the analysis.
[00120] In an embodiment, the processor (202) may analyze at least one of the flexibility parameter, the mobility parameter, the AROM parameter associated with the one or more joints of the user (102), the strength parameter of the user (102) recorded during said at least one transition, and the one or more aggravating or relieving factors associated with the user (102) for recommending change in the exercise plan associated with one or more exercise routines of the user (102).
[00121] In an embodiment, the processor (202) may cause to display, via the computing device (104) the recommendation to the user (102) to enable the user to modify the one or more postures as a corrective measure and improve the repetitions performed by the user (102). In an embodiment, the processor (202) may receive inputs from a health care provider (HCP) for recommending changes in one or more exercise routines of the user (102).
[00122] In an embodiment, the HCP may include, but not limited to, orthopedic specialists and physiotherapists, who may assess an output from the processor (202) and provide an additional feedback in real-time to the users (102) based on their postures during the physical exercise routine. The HCP may also set up tolerances and ranges for the patient/user (102) to perform each physical exercise and provide a customized solution to the user (102).
[00123] Although FIG. 2 shows exemplary components of the system (108), in other embodiments, the system (108) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 2. Additionally, or alternatively, one or more components of the system (108) may perform functions described as being performed by one or more other components of the system (108).
[00124] FIG. 3 illustrates an example method diagram (300) for implementing the proposed system (108), in accordance with an embodiment of the present disclosure.
[00125] As illustrated, the proposed system (108) may incorporate the following steps.
[00126] At step 302: The method may include receiving, by a system (108), one or more inputs from a user (102) associated with a computing device (104), where the one or more inputs may be based on a clinical profile of the user (102).
[00127] At step 304: The method may include recommending, by the system (108), through the computing device (104), one or more exercise routines personalized to the user.
[00128] At step 306: The method may include receiving, by the system (108), a request from the user (102) through the computing device (104) for analyzing one or more postures or angles performed by the user (102) during the one or more exercise routines for a predetermined period.
[00129] At step 308: The method may include analyzing, by the system (108), via an AI engine (214), the one or more postures or angles performed by the user (102) for the predetermined period.
[00130] At step 310: The method may include generating, by the system (108), a recommendation associated with a new or modified routine to the user (102) based on the analyzed one or more postures or angles and the clinical profile of the user (102).
[00131] At step 312: The method may include causing to display, by the system (108), via the computing device (104), the recommendation to the user (102) to enable the user to modify the one or more postures as a corrective measure and improve the repetitions performed by the user (102).
[00132] FIG. 4 illustrates an example block diagram (400) for implementing the proposed system (108), in accordance with an embodiment of the present disclosure.
[00133] As illustrated in FIG. 4, in an embodiment, the system (108) may receive the clinical profile associated with the user (102). The clinical profile may include a patient demographic (402) or a demographic factor (402) associated with the user (102), a pain duration and intensity (404) associated with user (102), a trauma history (406) associated with the user (102), one or more aggravating and relieving factors (408) associated with the user (102), a pain radiation (410) associated with the user (102) and one or more active range of motions (AROM) (412) associated with the user (102).
[00134] In an embodiment, the system (108) may recommend one or more exercise routines to the user (102). The one or more exercise routines may include but are not limited to an exercise plan (level 1) (416), an exercise plan (level 2) (418), an exercise plan (level 3) (420), and an exercise plan (level 4) (422). The exercise plan personalized to the user (102) may be varied from level 1 to level 4 based on the adherence, improvement in the AROM, pain patterns of the user (102) and the feedback from the user (102). The system (108) may provide one or more movement tags based on identified one or more improvements, changes in the restricted movements associated with musculoskeletal condition of the user (102) recorded during the one or more exercise routines. The user (102) may be moved up to the next level only if there is an improvement in the musculoskeletal condition of the user (102). If there is no improvement or worsening, the user (102) may be asked to repeat the exercises. Two consecutive worsening or four consecutive no improvements may be flagged, and the user (102) may be recommended to visit a physiotherapist/ HCP in person.
[00135] In a further embodiment, the system (108) may also recommend one or more discharge exercises (424) to the user (102). The discharge exercises are one or more exercises to be performed by the user after completion of the assigned or recommended levels of exercise routines to maintain a pain free condition.
[00136] In an embodiment, the system (108) may change the level of exercise plan stage wise and also based on the user medical condition and user willingness to take up the next level of exercise routines. These stages are designed to ensure there are minimal injuries or disease-worsening scenarios. The stage wise assessment ensures that the severity of pain is getting reduced and improvement in the movement restrictions of the user.
[00137] FIG. 5 illustrates an example method diagram (500) for generating movement or feedback tags while recommending a new or a modified routine to the user, in accordance with an embodiment of the present disclosure.
[00138] As illustrated in FIG. 5, the method diagram (500) may include the following steps.
[00139] At step 502: The system (108) may receive a user login.
[00140] At step 504: The system (108) may receive one or more inputs from the user (102).
[00141] At step 506: The system (108) may determine a root cause and severity of a pain.
[00142] At step 508: The system (108) may generate one or more movement tags to the user (102) based on the root cause and the severity of pain.
[00143] At step 510: The system (108) may recommend a level of exercise personalized to the user (102) based on the generated one or more restricted movement tags.
[00144] At step 512: The system (108) may analyze one or more movements or postures performed by the user (102).
[00145] At step 514: The system (108) may generate a recommendation associated with a new or a modified routine to the user (102).
[00146] At step 516: The system (108) may receive one or more feedbacks from the user (102).
[00147] At step 518: The system (108) may generate and suggest one or more feedback tags to the user (102) based on the improvements and changes in parameters.
[00148] At step 520: The system (108) may determine a condition of the user (120) based on the suggested one or more feedback tags.
[00149] At step 522: Based on a positive determination from step 520, the system (108) may analyze that the condition of the user (102) has been improved. Based on this information, the system (108) may change the level of exercise plan associated with exercise(s) or exercise routines for the user (102).
[00150] In an embodiment, the system (108) promotes the user to the next level of the exercise plan based on the user's adherence, Active Range of Motions (AROM), pain patterns, and user feedback while ensuring the safety of the user (102).
[00151] At step 524: Based on a negative determination from step 520, the system (108) may analyze that the condition of the user (102) has not been improved. Based on this information, the system (108) may ask the user (102) to repeat the exercises (524).
[00152] FIG. 6 illustrates an example method diagram (600) for predicting the performance of the user during a routine in accordance with an embodiment of the present disclosure.
[00153] As illustrated in FIG. 6, the method diagram (600) may include the following steps.
[00154] At step 602: The method may include receiving by a system (108), one or more inputs from a computing device (104) associated with a user (102) for analyzing and/or recommending postures during the exercise routine of the user (102).
[00155] At step 604: The method may include detecting by the system (108), at least one transition between one movement state to another movement state performed by the user (102) during a predetermined period.
[00156] At step 606: The method may include comparing by the system (108), one or more movements or postures or angles associated with said at least one transition performed by the user (102) during the predetermined period.
[00157] At step 608: The method may include predicting by the system (108), via a trained model, a success or a failure rate associated with the one or more movements or postures or angles during the predetermined period.
[00158] In an embodiment, predicting the failure or success rate as used herein refers to the ability of the said system to compute through the processor via a trained AI model whether or not the user will be able to successfully complete the assigned exercise routine or plan, before the assigned exercise routine or plan is actually completed, or before the next routine is assigned.
[00159] At step 610: The method may include recommending by the system (108), via the trained model, a new or a modified routine associated with said at least one transition during the predetermined period.
[00160] FIG. 7 illustrates an example method diagram (700) for analyzing user postures and predicting the user performance during the routine, in accordance with an embodiment of the present disclosure.
[00161] As illustrated in FIG. 7, the method diagram (700) may include the following steps.
[00162] At step 702: The method may include receiving, by a system (108), one or more inputs from a user (102), where the one or more inputs may be based on a clinical profile of the user (102).
[00163] At step 704: The method may include recommending, by the system (108), through the computing device (108), one or more exercise routines personalized to the user (102).
[00164] At step 706: The method may include receiving, by the system (108), a request from the user (102) through the computing device (104) for analyzing one or more postures performed by the user (102) during the one or more exercise routines for a predetermined period.
[00165] At step 708: The method may include detecting, by the system (108), at least one transition between one movement state to another movement state performed by the user (102) during the predetermined period.
[00166] At step 710: The method may include comparing, by the system (108), one or more movements or postures or angles associated with said at least one transition performed by the user (102) during the predetermined period.
[00167] At step 712: The method may include predicting, by the system (108), via a trained model, a success or failure rate associated with the one or more movements or postures or postures during the predetermined period.
[00168] At step 714: The method may include recommending, by the system (108), via the trained model, a new and/or a modified routine associated with said at least one transition during the predetermined period.
[00169] In an embodiment, the method may include, predicting, by the processor (202), via the trained model, a success or failure rate associated with one or more movements or postures during the predetermined period by comparing one or more movements or postures associated with at least one transition performed by the user during the predetermined period.
[00170] In an embodiment, the method may include, estimating, by the processor (202), a range of motion associated with the user (102) during said at least one transition for the predetermined period. The success or failure rate of the estimated range of motion associated with the user is predicted by the processor (202) via the trained model during at least one transition.
[00171] In an embodiment, the method may include displaying the recommendation generated by the trained model via a user interface of the computing device (104). These recommendations enable the user (102) to utilize the recommendation as a corrective measure to modify the one or more postures and/or improve the repetitions performed by the user (102).
[00172] In an embodiment, wherein the trained model is a causality model that generates the new and/or modified routine based on the prediction of the success or failure rate.
[00173] In an embodiment, the term causality model refers to the set of techniques used to generate or create the incremental impact of an action or treatment on a given outcome.
[00174] In an embodiment, the terms AI engine or recommendation engine as used herein are interchangeable and referred to including, but not limited to, causality model.
[00175] In an embodiment, the method may include collecting real-time data of the patient performing exercise or exercise routine(s). This real-time data contains a mathematical equation/ graph of the user’s or patient’s performing the exercise repetitions. The trained model, i.e., a mathematical model, analyses the above data and predicts success or failure rate in real-time. Further, the mathematical model is periodically trained with data that is similar to the user's or patient's exercise data to improve the performance and accuracy of the trained model to predict the success or failure rate associated with the user (102). Further, the mathematical model is trained to generate the new and/or modified routine based on the prediction of the success or failure rate.
[00176] FIG. 8 illustrates an exemplary computer system (800) in which or with which embodiments of the present disclosure may be implemented.
[00177] As shown in FIG. 8, the computer system (800) may include an external storage device (810), a bus (820), a main memory (830), a read-only memory (840), a mass storage device (850), a communication port(s) (860), and a processor (870). A person skilled in the art will appreciate that the computer system (800) may include more than one processor and communication ports. The processor (870) may include various modules associated with embodiments of the present disclosure. The communication port(s) (860) may be any of an RS-232 port for use with a modem-based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication ports(s) (860) may be chosen depending on a network, such as a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system (800) connects.
[00178] In an embodiment, the main memory (830) may be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (840) may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chip for storing static information e.g., start-up or basic input/output system (BIOS) instructions for the processor (870). The mass storage device (850) may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces).
[00179] In an embodiment, the bus (820) may communicatively couple the processor(s) (870) with the other memory, storage, and communication blocks. The bus (820) may be, e.g., a Peripheral Component Interconnect PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), Universal Serial Bus (USB), or the like, for connecting expansion cards, drives, and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor (870) to the computer system (800).
[00180] In another embodiment, operator and administrative interfaces, e.g., a display, keyboard, and cursor control device may also be coupled to the bus (820) to support direct operator interaction with the computer system (800). Other operator and administrative interfaces can be provided through network connections connected through the communication port(s) (860). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system (800) limit the scope of the present disclosure.
[00181] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be implemented merely as illustrative of the disclosure and not as a limitation.
,CLAIMS:1. A system (108) for analyzing postures and generating recommendations, the system (108) comprising:
a processor (202); and
a memory (204) operatively coupled with the processor (202), wherein said memory (204) stores instructions which, when executed by the processor (202), cause the processor (202) to:
receive one or more inputs from a user (102) associated with a computing device (104), wherein the one or more inputs are based on a clinical profile of the user (102);
recommend, through the computing device (104), one or more exercise routines personalized to the user (102);
receive a request from the user (102) through the computing device (104) for analyzing one or more postures or angles performed by the user (102) during the one or more exercise routines for a predetermined period;
analyze, via an artificial intelligence (AI) engine (214), the one or more postures or angles performed by the user (102) for the predetermined period;
generate a recommendation associated with a new or a modified routine to the user (102) based on the analyzed one or more postures or angles and the clinical profile of the user (102); and
cause to display, via the computing device (104), the recommendation to the user (102) to enable the user to modify one or more postures as a corrective measure and improve the repetitions performed by the user (102).
2. The system (108) as claimed in claim 1, wherein the clinical profile comprises any or a combination of: a demographic factor associated with the user (102), a pain location of the user (102), a pain duration and intensity associated with the user (102), a trauma history associated with the user (102), a surgical history associated with the user (102), one or more aggravating and relieving factors associated with the user (102), a pain radiation associated with the user (102), one or more active range of motions (AROM) associated with the user (102), and a lifestyle and an occupation details associated with the user (102).
3. The system (108) as claimed in claim 1, wherein the processor (202) is configured to determine at least one transition between one movement state to another movement state performed by the user (102) during the one or more exercise routines for generating the recommendation.
4. The system (108) as claimed in claim 3, wherein the processor (202) analyses, via the AI engine (214), said at least one transition as a digital biomarker and compares said at least one transition with standard routine information stored in a database (210) to detect similarities and dissimilarities between said at least one transition and the standard routine information, and wherein a range of motion (ROM) associated with said at least one transition is determined as the digital biomarker.
5. The system (108) as claimed in claim 4, wherein by analyzing the one or more postures or angles of the user (102), the processor (202) is configured to simultaneously generate and provide one or more movement tags to the user (102) based on said at least one transition and a response from the user (102), and wherein the one or more movement tags are based on one or more restricted movements associated with musculoskeletal condition of the user (102) recorded during the one or more exercise routines.
6. The system (108) as claimed in claim 5, wherein the processor (202) is configured to analyze one or more parameters selected from aggravating or relieving factors or one or more movements associated with one or more joints of the user (102), and a severity of movement restriction associated with the one or more joints of the user (102), or the AROM associated with the one or more joints of the user (102) to simultaneously generate the one or more movement tags for the user (102).
7. The system (108) as claimed in claim 6, wherein the processor (202) is configured to receive a feedback from the user (102) associated with the one or more movements, and generate the recommendation for the user (102) to provide the corrective measure and/or feedback tag based on the feedback, and wherein the one or more feedback tags are based on identified one or more improvements, and changes in the restricted movements associated with musculoskeletal condition of the user (102) recorded during the one or more exercise routines.
8. The system (108) as claimed in claim 7, wherein the processor (202) is configured to analyze one or more parameters selected from the movements and the AROM during said at least one transition, the pain duration, intensity associated with the pain duration, and a pain radiation associated with the intensity, and generate the recommendation of the new or the modified routine for the user (102) based on the analysis.
9. The system (108) as claimed in claim 6, wherein the processor (202) is configured to analyze one or more parameters selected from flexibility, mobility, active range of motions (AROM) associated with the one or more joints of the user (102), strength of the user (102) recorded during said at least one transition, and one or more aggravating or relieving factors associated with the user (102) for recommending change in a level of exercise plan associated with one or more exercise routines personalized to the user (102).
10. The system (108) as claimed in claim 9, wherein the one or more exercise or exercise routine(s) of the exercise plan are classified by the processor (202) based on attributes selected from one or more of the flexibility, mobility, strength, stability, intensity, and orientation associated with a musculoskeletal condition.
11. A method for analyzing postures and generating recommendations, the method comprising:
receiving, by a processor (202) associated with a system (108), one or more inputs from a user (102) associated with a computing device (104), wherein the one or more inputs are based on a clinical profile of the user (102);
recommending, by the processor (202), through the computing device (104), one or more exercise routines personalized to the user (102);
receiving, by the processor (202), a request from the user (102) through the computing device (104) for analyzing one or more postures or angles performed by the user (102) during the one or more exercise routines for a predetermined period;
analyzing, by the processor (202), via an artificial intelligence (AI) engine (214), the one or more postures or angles performed by the user (102) for the predetermined period;
generating, by the processor (202), a recommendation associated with a new or a modified routine to the user (102) based on the analyzed one or more postures and the clinical profile of the user (102); and
causing to display, by the processor (202), via the computing device (104), the recommendation to the user (102) to enable the user (102) to modify the one or more postures as a corrective measure and improve the repetitions performed by the user (102).
12. The method as claimed in claim 11, comprising determining, by the processor (202), at least one transition between one movement state to another movement state performed by the user (102) during the one or more exercise routines for generating the recommendation.
13. The method as claimed in claim 12, comprising analyzing, by the processor (202) via the AI engine (214), said at least one transition as a digital biomarker and comparing said at least one transition with standard routine information stored in a database (210) to detect similarities and dissimilarities between said at least one transition and the standard routine information, and wherein a range of motion (ROM) associated with said at least one transition is determined as the digital biomarker.
14. The method as claimed in claim 13, comprising simultaneously generating, by the processor (202), via the AI engine (214), upon analyzing the one or more postures and providing one or more movement tags to the user (102) based on said at least one transition and a response from the user (102), and wherein the one or more movement tags are based on one or more restricted movements associated with musculoskeletal condition of the user (102) recorded during the one or more exercise routines.
15. The method as claimed in claim 14, comprising analyzing, by the processor (202), parameters selected from one or more aggravating or relieving factors or one or more movements associated with one or more joints of the user (102), and a severity of movement restriction associated with the one or more joints of the user (102), or the AROM associated with the one or more joints of the user (102) to simultaneously generate the one or more movement tags for the user (102).
16. The method as claimed in claim 15, comprising receiving, by the processor (202), a feedback from the user (102) associated with the one or more movements, and generating the recommendation for the user (102) to provide the corrective measure and/or feedback tag based on the feedback, and wherein the one or more feedback tags are based on identified one or more improvements, and changes in the restricted movements associated with musculoskeletal condition of the user (102) recorded during the one or more exercise routines.
17. The method as claimed in claim 16, comprising analyzing, by the processor (202), parameters selected from one or more movements and the AROM during said at least one transition, the pain duration, intensity associated with the pain duration, and a pain radiation associated with the intensity, and generating the recommendation of the new or the modified routine for the user (102) based on the analysis.
18. The method as claimed in claim 15, comprising analyzing, by the processor (202), at least one or more parameters selected from flexibility, mobility, active range of motions (AROM) associated with the one or more joints of the user (102), strength of the user (102) recorded during said at least one transition, and the one or more aggravating or relieving factors associated with the user (102) for recommending change in a level of exercise plan associated with one or more exercise routines personalized to the user (102).
19. The method as claimed in claim 18, comprising classifying, by the processor (202), the one or more exercise routines of the exercise plan based on attributes selected from the flexibility, mobility, strength, stability, intensity and an orientation associated with a musculoskeletal condition.
20. The method as claimed in claim 11, comprising predicting, by the processor (202), via the AI engine (214), a failure or a success rate associated with the one or more postures performed by the user (102) during the one or more exercise routines for the predetermined period.
21. A method for predicting a user performance during a routine, the method comprising:
receiving, by a processor (202) associated with a system (108), one or more inputs from a user (102), wherein the one or more inputs are based on a clinical profile of the user (102);
recommending, by the processor (202), through the computing device (108), one or more exercise routines personalized to the user (102);
receiving, by the processor (202), a request from the user (102) through the computing device (104) for analyzing one or more postures or angles performed by the user (102) during the one or more exercise routines for a predetermined period;
detecting, by the processor (202), at least one transition between one movement state to another movement state performed by the user (102) during the predetermined period;
comparing, by the processor (202), one or more movements, angles or postures associated with said at least one transition performed by the user (102) during the predetermined period;
predicting, by the processor (202), via a trained model, a success or failure rate associated with the one or more movements, angles or postures during the predetermined period; and
recommending, by the processor (202), via the trained model, a new and/or a modified routine associated with said at least one transition during the predetermined period.
22. The method as claimed in claim 21, wherein the trained model is a causality model that generates the new and/or modified routine based on the prediction of the success or failure rate.
23. The method as claimed in claim 21, comprising estimating, by the processor (202), a range of motion associated with the user (102) during said at least one transition for the predetermined period.
24. The method as claimed in claim 21, comprising predicting, by the processor (202), via the trained model, the success or failure rate in achieving the estimated range of motion associated with the user (102) during said at least one transition.
25. The method as claimed in claim 21, comprising displaying via a user interface of the computing device (104), the recommendation to enable the user (102) to utilize the recommendation as a corrective measure to modify the one or more postures and/or improve the repetitions performed by the user (102).