[0001] This application claims the benefit of provisional patent application No. 63/158,375 filed March 9 ^th , 2021, titled “AN END-TO-END TELE-HEALTH SYSTEM THAT INCLUDES SOFTWARE AND HARDWARE COMPONENTS WITH ADDITIONAL SUB SYSTEMS”, which is hereby incorporated by reference in its entirety without giving rise to disavowment. TECHNICAL FIELD

[0002] The present disclosure relates to health information technology in general, and to treatment personalization with real time adaptation, in particular.

BACKGROUND [0003] Clinical practice guidelines (CPG) are systematically developed statements to assist practitioner and patient decisions about appropriate health care for specific clinical circumstances. The statements comprise recommendations intended to optimize patient care and reduce errors or unjustified variations in medical practice. The recommendations may be informed by a systematic review of evidence or experts recommendations, and an assessment of the benefits and harms of alternative care options. A CPG focuses on a single disorder and addresses frequent comorbidities, based on clinical context.

[0004] Clinical Decision Support Systems (CDSSs) assists care providers in defining patient-specific recommendation at the point of care during clinical encounters. The CDSS may be configured to match the patient's data from the medical record with medical knowledge from computer-interpretable CPGs to provide patient-specific recommendations at the point of care during clinical encounters. BRIEF SUMMARY

[0006] One exemplary embodiment of the disclosed subject matter is a method comprising: obtaining a treatment plan for a subject, wherein the treatment plan comprises instructions to the subject to perform a physical action; monitoring the subject during performing the physical action, wherein said monitoring is performed using a device attached to a body of the subject, wherein the device comprises a motion sensor and an electromyography sensor; automatically analyzing sensor readings obtained from the device, wherein the sensor readings comprise motion sensor readings from the motion sensor that are associated with the physical action, wherein the sensor readings comprise recordings of electrical activity produced by the skeletal muscle of the subject when the subject performed the physical action, wherein the recordings of the electrical activity are recorded by the electromyography sensor; and modifying the treatment plan, based on said automatically analyzing, whereby generating a modified treatment plan for the subject, wherein the modified treatment plan comprises an instruction to the subject to perform a modified physical action instead of the physical action, whereby dynamically adapting the physical action to the subject based on the sensor readings.

[0007] Optionally the method further comprises providing the modified treatment plan to the subject.

[0008] Optionally the method further comprises obtaining demographic data of the subject, wherein the demographic data comprise at least one non-clinical demographic parameter; wherein said modifying the treatment plan is further performed based on the at least one non-clinical demographic parameter, whereby generating the modified treatment plan based on a combination of demographic data and behavioral data.

[0009] Optionally, said automatically analyzing comprises identifying a movement pattern indicative of mental detachment of the subject with respect to performance of the physical action, wherein said modifying is performed based on said identifying, whereby the modified treatment plan is generated in order to increase adherence of the subject in performing the modified treatment plan compared to the subject’s adherence to the performance of the treatment plan.

[0010] Optionally, the motion sensor is configured to continuously monitor motions performed by an organ of the subject, wherein the electromyography sensor is configured to selectively monitor electrical activity produced by a skeletal muscle of the subject, wherein the method further comprises: automatically identifying, based on the motion data, that the subject is intending to perform the physical action; and automatically instructing the device to activate the electromyography sensor in response to said automatically identifying.

[0011] Optionally, said automatically analyzing the sensor readings comprises determining physical incapability of the subject to perform the physical action.

[0012] Optionally the method further comprises obtaining from the subject, subjective feedback related to performing the physical action; wherein said modifying the treatment plan is further performed based on the subjective feedback.

[0013] Optionally, said modifying comprises: evaluating, based on said automatically analyzing, a physical diagnosis of the subject, whereby the treatment plan is modified based on the physical diagnosis.

[0014] Optionally, the modified treatment plan comprises a recommendation to avoid performing an action, wherein the recommendation to avoiding performing the action is determined based on the sensor readings obtained from the device.

[0015] Optionally, the treatment plan is devised to achieve a clinical goal, wherein the method further comprises automatically updating, based on said automatically analyzing, the clinical goal of the treatment plan, whereby determining an updated clinical goal, wherein the modified treatment plan is devised to achieve the updated clinical goal.

[0016] Optionally the method further comprises automatically updating a duration of a treatment according the modified treatment plan.

[0017] Optionally, said automatically analyzing the sensor readings comprises determining movement habits of the subject and reaction of the skeletal muscle thereto; and wherein said modifying the treatment plan comprises adapting the treatment plan to the movement habits of the subject.

[0018] Another exemplary embodiment of the disclosed subject matter is a system comprising: a device configured to be attached to a body of a subject, wherein the device comprises: a motion sensor configured to continuously monitor motions performed by an organ of the subject; and an array of electromyography sensors configured to evaluate electrical activity produced by one or more skeletal muscles of the subject, wherein at least a portion of the array of electromyography sensors is configured to be selectively activated based on analysis of sensor readings of said motion sensor; a controller operatively coupled with said device, wherein the controller is configured to selectively activate and de-activate said array of electromyography sensors; an analysis module configured to automatically analyzing sensor readings obtained from said device, wherein the sensor readings are associated with executing a treatment plan by the subject; and a treatment module configured to generate a modified treatment plan for the subject based on the treatment plan and analysis of the sensor readings. [0019] Optionally, said analysis module is configured to analyze the sensor readings with respect to demographic data of the subject, wherein the demographic data comprise at least one non-clinical demographic parameter; wherein said treatment module is configured to further modifying the treatment plan based on the at least one non-clinical demographic parameter, whereby generating the modified treatment plan based on a combination of demographic data and behavioral data.

[0020] Optionally, said analysis module is configured to identify a movement pattern indicative of mental detachment of the subject with respect to performance of the treatment plan; wherein said treatment module is configured to generate the modified treatment plan in order to increase adherence of the subject in performing the modified treatment plan compared to the subject’s adherence to the performance of the treatment plan.

[0021] Optionally, the motion sensor is configured to continuously monitor motions performed by an organ of the subject, wherein the electromyography sensor is configured to selectively monitor electrical activity produced by a skeletal muscle of the subject; and in response to said analysis module is configured to identify, based on the motion data, that the subject is intending to perform a physical action; and wherein said controller is configured, in response to said analysis module identification, to automatically instructing the device to activate the electromyography sensor.

[0022] Optionally, said analysis module is configured to determining physical incapability of the subject to perform a physical action. [0023] Optionally, said treatment module is configured to modifying the treatment plan based on subjective feedback from the subject related to performing a physical action;

[0024] Optionally, said treatment module is further configured to evaluate, based on analysis of the sensor readings, a physical diagnosis of the subject; and modify the treatment plan based on the physical diagnosis.

[0025] Optionally, the modified treatment plan comprises a recommendation to avoid performing an action, wherein the recommendation to avoiding performing the action is determined based on the sensor readings obtained from the device.

[0026] Optionally, the treatment plan is devised to achieve a clinical goal, wherein said treatment module is further configured to automatically update based on analysis of the sensor readings, the clinical goal of the treatment plan, whereby determining an updated clinical goal, wherein the modified treatment plan is devised to achieve the updated clinical goal.

[0027] Optionally, said treatment module is further configured to automatically update a duration of a treatment according the modified treatment plan.

[0028] Optionally, said analysis module is configured to determine movement habits of the subject and reaction of the skeletal muscle thereto; and wherein said treatment module is configured to adapt the treatment plan to the movement habits of the subject.

[0029] Yet another exemplary embodiment of the disclosed subject matter is a computer program product comprising a non-transitory computer readable storage medium retaining program instructions, which program instructions when read by a processor, cause the processor to perform a method comprising: obtaining a treatment plan for a subject, wherein the treatment plan comprises instructions to the subject to perform a physical action; monitoring the subject during performing the physical action, wherein said monitoring is performed using a device attached to a body of the subject, wherein the device comprises a motion sensor and an electromyography sensor; automatically analyzing sensor readings obtained from the device, wherein the sensor readings comprise motion sensor readings from the motion sensor that are associated with the physical action, wherein the sensor readings comprise recordings of electrical activity produced by the skeletal muscle of the subject when the subject performed the physical action, wherein the recordings of the electrical activity are recorded by the electromyography sensor; and modifying the treatment plan, based on said automatically analyzing, whereby generating a modified treatment plan for the subject, wherein the modified treatment plan comprises an instruction to the subject to perform a modified physical action instead of the physical action, whereby dynamically adapting the physical action to the subject based on the sensor readings.

THE BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0030] The present disclosed subject matter will be understood and appreciated more fully from the following detailed description taken in conjunction with the drawings in which corresponding or like numerals or characters indicate corresponding or like components. Unless indicated otherwise, the drawings provide exemplary embodiments or aspects of the disclosure and do not limit the scope of the disclosure. In the drawings:

[0031] Figure 1 shows a schematic illustration of an exemplary physical activity monitoring device, in accordance with some exemplary embodiments of the disclosed subject matter; [0032] Figures 2A-2E show flowchart diagrams of a method, in accordance with some exemplary embodiments of the disclosed subject matter; and

[0033] Figure 3 shows a block diagram of a system, in accordance with some exemplary embodiments of the disclosed subject matter.

DETAILED DESCRIPTION

[0034] One technical problem dealt with by the disclosed subject matter is adapting CPGs involving physical activity to real capabilities of the patient. In some exemplary embodiments, recommendations of CPG in different disciplines, such as in physical therapy, physical training, sports, or the like, may comprise recommendations to perform certain physical actions, such as specific exercises, specific movements, avoid performing other actions, or the like. Such recommendations may be general and may be generated based on clinical context or medical insights related to the medical record of the patient, without taking into account physical and psychological capabilities of the patient, such as physical and mental ability to perform such recommendations, environmental circumstances, or the like. The statements comprise recommendations intended to optimize patient care and reduce errors or unjustified variations in medical practice. The recommendations may be informed by a systematic review of evidence, and an assessment of the benefits and harms of alternative care options. [0035] Additionally or alternatively, it may be required to adapt CPGs to define the relevant data that needs to be collected, clinical goals and possible clinical actions, along with criteria that rank these actions according to their relevance for a patient’s state. Additionally or alternatively, a dynamic involvement of a physician that may assess the patient’s state, based on the values of the patient’s data items, such as clinical components, psychosocial components, demographic components, behavioral components, or the like, may be required.

[0036] Another technical problem dealt with by the disclosed subject matter is providing a dynamic personalized CDSS that assists care providers in defining real-time patient-specific recommendation. In some exemplary embodiments, CDSS may be configured to match the patient's data from the medical record with medical knowledge from computer-interpretable CPGs to provide patient-specific recommendations at the point of care during clinical encounters. However, such systems may have limited capabilities in providing personalized recommendations in real-time, due to the limited available tracking of patients during performing the treatments, limited ability to monitor the reaction of the treatment to the body of the patient (e.g., long-term reactions or immediate reactions, that cannot be measured by classic tests, such as blood tests, scanning, or the like). Additionally or alternatively, such systems may not take into account important types of data, such as subjective response of the patient to the treatment, ability to maintain the treatment process, dynamic updates of the patient physical reaction, or the like, that may be required to be integrated in taking medical decisions.

[0037] Yet another technical problem dealt with by the discloses subject matter is providing a remote dynamic treatment that enables the patient and the care-taker to improve the treatment process, by providing feedback and instructions during preforming the treatment. In some exemplary embodiments, such as in physical care-taking, patient- care taker relations and connection may be some of the most important and effect- enhancing or effect-reducing processes. The effectiveness of these relationships and connections may on many occasions, determine the effectiveness of the treatment as a whole. Additionally or alternatively, it may be required to track and ensure precision of the patient's movements and positions, in relation to those specified by the care-taker as those movements and positions specifically beneficial to the patient's condition and abilities.

[0038] Yet another technical problem dealt with by the disclosed subject matter is sustaining patient adherence to treatment recommendations over time. In some exemplary embodiments, patients may have challenges to persist in ongoing treatment. These challenges may result from incompleteness of the CPG with respect to psycho social and demographic considerations. Care providers are aware of this and when medically warranted, they manually adjust the treatment recommendations, taking into the considerations additional data items outside the scope of the guideline, such as comorbidities, socio-demographic, psychological, behavioral or other domains. However, automatic treatment systems or CDSSs, may not integrate such additional considerations. It may be required to ensure that the guideline provides treatment options that address these additional considerations, modulating the recommended advice by re ranking of the options.

[0039] As an example, the field of physiotherapy is experiencing a very high chum rate, which is strongly reflected in the community physical therapy. According to WebPT, about 70% of PT patients fail to complete their full course of care, 20% of them dropout within the first three visits. The attrition rates may be caused by different factors, such as: lack of immediate results, patient lifestyle nor conforming with the treatment requirement, and the like. A major problem causing these factors is that physical therapists not having the tools to control throughout the therapeutic process, especially when the process combines several types of treatments.

[0040] Yet another technical problem dealt with by the discloses subject matter is providing adapted CDSSs for physical health treatment, such as in physiotherapy, rehabilitation, physiological treatment, or the like, that require continuous monitoring and updating of the treatment for predetermined durations. In some exemplary embodiments, specifying the treatment for the patient, monitoring the patient's movements and progress through the treatment process and setting realistic and beneficial goals may all be key points in making patients follow through and benefit, both mentally and physiologically from said treatments.

[0041] Yet another technical problem is providing a dynamic real-real time diagnosis based on routine or daily activity of the patients. As an example, in physical therapy, it may be required to address the illnesses or injuries that limit a person's abilities to move or perform functional activities in their daily lives. In some exemplary embodiments, physical therapists may use an individual's history and physical examination to arrive at a diagnosis and establish a management plan. Additionally or alternatively, results of laboratory and imaging studies such as X-rays, Computed Tomography (CT)-scan, Magnetic Resonance Imaging (MRI) findings, or the like, electrodiagnostic testing, or the like, may be incorporated in order to establish a more accurate diagnosis. However, such techniques may not be capable of monitoring physical activity of the patients in their daily lives, according to their habits and during performing their normative activity, in order to determine a more accurate diagnosis and evaluate the effectiveness of the provided therapy. As an example, it may be required to monitor the patient’s physical activity during standing, walking, sitting, or the like. As another example, a major portion of the patient’s activity that may be involved with high importance for physical therapy and diagnosis is during sleeping. Many physical conditions, disorders or problems occur during or in consequent to wrong sleeping positions. It may be required to monitor physical activity of the patient during sleeping in order to identify such wrong position, effect of sleeping position or movements on the patient’s body, on the therapy plan, or the like.

[0042] Yet another technical problem is dynamically managing physical training for athletes in accordance with their medical history. In some exemplary embodiments, a dynamic real-real time monitoring of sport activity for athletes and the physical reaction of athletes bodies during training, may be required to ensure that the certain physical activity is perform accurately, does not harm the athlete's body or affect a medical problem thereof, or the like. Additionally or alternatively, a dynamic continuous monitoring of relevant physical activity of athletes and its effect on patients’ bodies, may be required in order to provide an accurate non-limited diagnosis in sport physical therapy and athletic injury management. In order to provide an accurate assessment and diagnosis of injuries or other conditions, it may be required to monitor activity of the muscles of the patient during preforming the sport activity.

[0043] It is to be noted that any descriptions of the hereby disclosed invention are meant to be descriptive in nature rather than limiting or restrictive.

[0044] One technical solution is utilizing a layered data system configured to automatically monitor a subject during or before a treatment, and generate a modified treatment plan for the subject.In some exemplary embodiments, the system may serve as a dynamic personalized CDSS that assists care providers in providing real-time patient- specific recommendation. Each layer may comprise a different type of data on which the clinical decision or recommendation is determined based on.

[0045] In some exemplary embodiments, one data layer may comprise real time sensor readings from a monitoring device worn by or attached to the subject's body prior or during providing the recommendations. The monitoring device may be configured to monitor day to day routine activity of the subject, monitor an execution of a certain treatment or training plan for the subject, or the like. Additionally or alternatively, the monitoring device may be configured to monitor the subject during performing a certain physical action comprised by the treatment plan.

[0046] In some exemplary embodiments, the monitoring device may be configured to be attached to a body of the subject, worn by the subject, carried by the subject, or the like. The monitoring device may comprise motion sensors and electromyography sensors. The motion sensors may be configured to continuously monitor motions performed by the subject. The electromyography sensors may be configured to selectively monitor electrical activity produced by skeletal muscles of the subject, based on readings of the motion sensors. [0047] In some exemplary embodiments, the system may be configured to automatically analyze sensor readings obtained from the monitoring device. The sensor readings may comprise motion sensor readings from the motion sensors that are associated with the physical action and recordings of electrical activity produced by the skeletal muscle of the subject when the subject performed the physical action, as recorded by the electromyography sensors. The system may be configured to modify the treatment plan, based on the analysis of the sensor readings to generate a modified treatment plan adapted for the subject.

[0048] In some exemplary embodiments, analysis of sensor reading and modification of the treatment plan may be performed automatically using machine learning techniques and classifiers. The classifiers may be trained based on physiotherapist expertise and measurements, based on manually annotated examples, or the like. Additionally or alternatively, the analysis may be performed automatically and the modification may be semi-automatic, e.g., performed or supervised by physical therapists or physicians. Additionally or alternatively, both training and modifying may be performed automatically and controlled by the care taker.

[0049] In some exemplary embodiments, another data layer of the system may comprise behavioral data. The behavioral data may be obtained based on analysis of the sensor readings, such as identifying movements habits of the subject, incapability to perform certain action, or the like. As an example, the behavioral data may comprise continuous sitting or standing postures. Sensor analysis with respect to such behavioral data may be performed to identify potential tension or muscle problems caused by the behavior that might affect treatment recommendations. As another example, walking or running habits that may affect ability to complete recommendations provided by the initial treatment plan. Additionally or alternatively, the behavioral data may be obtained from other devices, such as a wearable device monitoring sleeping quality, a Fitbit™ device monitoring general physical activity, or the like. [0050] Additionally or alternatively, another data layer may comprise demographic data that is not directly related to the medical condition of the subject. The system may be configured to obtain demographic data of the subject. The demographic data may comprise clinical and non-clinical demographic parameter. As an example, the demographic data may comprise gender, age, education, socioeconomic status, language, place of residence, occupation, family status, income, or the like. A parameter like income may be a non-clinical parameter. However, such parameter may have influence on the treatment process. Treatment plan may further be modified based on non-clinical demographic parameters of the subject. [0051] Additionally or alternatively, another data layer may comprise subjective feedback from the subject related to the treatment. The subjective feedback may comprise feedback from the subject about the pain level, difficulty of performing a certain action, preferences of treatment protocols, inconvenience in performing an exercise or activity, lack of suitable space to perform required activity, lack of required tools for performing the exercise, or the like. Additionally or alternatively, the subjective feedback may comprise feedback from the subject prior to starting the treatment, such as general preferences, requests to avoid certain actions, restrictions (e.g., religious, cultural, personal, or the like), impacts from previous treatments, or the like. As an example the subject may inform the care taker about a previous treatment she had that assisted her in a similar condition, and require to integrate it in the treatment plan. Additionally or alternatively, another data layer may comprise physiological factors, such as mental detachment, exhaustless, lack of interest or concern in performing the treatment plan or portion thereof, insouciance, or the like. The system may be configured to identify movement patterns indicative of mental detachment of the subject with respect to performance of the treatment plan or a certain action or instruction therein, and modify the treatment plan in order to increase adherence of the subject thereto.

[0052] In some exemplary embodiments, the system may be configured to personalize CPG based treatments to the subject, personal parameters and preferences thereof, along with physiotherapist expertise and equipment. [0053] In some exemplary embodiments, the system may be configured to evaluate, based on analysis of the sensor readings, a physical diagnosis of the subject, update a clinical goal of the treatment, update expected or recommended duration of the treatment, or the like. As an example, the system may be configured to evaluate performance levels and chances of the treatment plan and modify it accordingly, e.g., extending or limiting the duration of the treatment, eliminate or add certain recommendations, or the like. As another example, the system may be configured to identify widening time gaps between activity performance, analyze possible reasons therefor, and modify the treatment plan accordingly. Additionally or alternatively, the system may be configured to identify of increasing anomalies, such as skipping treatments, reducing the total exercise time, or the like. [0054] In some exemplary embodiments, the system may be associated with a pre treatment and/or preventive analysis ability, such as in diagnosis, goal setting and timeline prediction ability, or the like. Additionally or alternatively, the system may be associated with pre-treatment and/or preventive recommendation ability, such as providing recommendations related to avoiding performing some daily activity, adapting routine, or the like. Additionally or alternatively, the system may be associated with patient treatment analysis and recommendation ability, such as providing dynamic recommendations, enabling real-time monitoring and adjustment of treatment parameters ability, or the like.

[0055] In some exemplary embodiments, the system may be associated with a semi- autonomous parameter altering option. An expert, such as a physician, a care-taker, a trainer, a physical therapist, or the like, may set basic guidelines, parameters, thresholds, or other minimum and maximum values for a specific treatment. The system may be configured to alter, in accordance with basic guidelines and parameters set by the expert, parameters relating to the treatment of the patient. The system may be configured to determine values set by the therapist, the either in real-time or between sessions.

[0056] Additionally or alternatively, the system may be associated with an adaptive treatment abilities. The system may be configured to determine adherence level of the patient to a certain treatment protocol assigned to her, based on physical parameters, psychological parameter, objective and subjective real-time feedback, or the like. The system may be configured to automatically update or modify the treatment protocol to suite the patient's abilities. As an example, if the assigned treatment protocol is determined, by the system, to be easy or difficult for the patient, the system may be configured to notify the therapist or care-taker as to said situation so the therapist or care taker can adjust the treatment protocol accordingly. Additionally or alternatively, the system may be configured, by the therapist, to make adjustments to the treatment protocol according to personal information obtained from the patient during the treatment.

[0057] In some exemplary embodiments, the system may be configured to simultaneously obtain, analyze and process information (e.g., sensor readings and records) from different body parts or organ, such as from different monitoring devices, for different components of the monitoring device, from other devices related to the body, or the like. The system may be configured to monitor the effect of the subject's movements of said parts or organs on the area or part of the body being monitored. AS an example, the system may be configured to monitor and analyze arm and leg movements to determine their physical effect on the lower back area.

[0058] One technical effect of utilizing the disclosed subject matter is prolonging the physical therapy process in a manner that guaranties efficiency of the treatment. Being easy to utilize and operate, adapted for daily use without limiting movement, lifestyle or normal activity of the patient, the disclosed device enables the patient to preserve with the treatment and avoid dropping out before finishing the therapeutic process.

[0059] Another technical effect of utilizing the disclosed subject matter is decreasing the costs and resources required for diagnosis and treatment in physical therapy. Utilizing the disclosed subject matter reduces burden on hospitals and medical centers, reduces the use of highly expensive equipment (such as EMG, CT-scans, MRI, or the like) and human resources for both of the diagnostic process and the treatment process. Furthermore, the disclosed subject matter enables monitoring and controlling the treatment process without the need for face-to-face contact, thus reducing pressure on therapists and load on medical centers.

[0060] Yet another technical effect of utilizing the disclosed subject matter is providing the care taker, trainer or the physical therapists with tools to control throughout the therapeutic process. The disclosed subject matter enables the patient and the physical therapists to improve the treatment process, by providing feedback and instructions that will be delivered back and forward forth between the two, so as to enhance treatment efficiency and facilitate an optimal connection between care-taker and client as well as the highest possible effectiveness of the physical treatment itself. The disclosed device enables the therapist to accompany the patient anywhere and at any time with the ability to adjust the treatment on the fly and pass the results on to the therapist. [0061] Yet another technical effect of utilizing the disclosed subject matter is personalizing the treatments to the patients with adjustments according to the patient needs, willing and abilities. The disclosed subject matter assists in diagnosing the problem and personalizing the treatment according to the patient habits and lifestyle, which may not be feasible using traditional diagnostic methods. On the other hand, the disclosed subject matter enables creating full uniformity in the side of care providers while offering the best personalized treatments following the most up-to-date evidence- based guidelines, by specifying the treatment for the patient, monitoring the patient's movements and progress through the treatment process and setting realistic and beneficial goals. [0062] Yet another technical effect of utilizing the disclosed subject matter is providing an enhanced personalized treatment for patients, that is adapted dynamically, especially in physical therapy. The disclosed subject matter combines different layers of data that enable personalizing CPG based treatments to a specific patient, such as based on patient personal parameters and preferences, along with physiotherapist expertise and equipment. The disclosed system dynamically accumulates multiple layers of data for determining clinical decisions, thereby enabling continues adaptation of the treatment, before and during execution thereof.

[0063] Yet another technical effect of utilizing the disclosed subject matter is adapting clinical or medical automatic tools and systems for other uses, such as sports, life wellness, research and ergonomics, or the like. The disclosed subject matter provides a new perspective in monitoring and analyzing physical performance, by precisely and dynamically measuring reaction of the body and specific skeletal muscles for performing each action, and continuously perform, in real-time, adjustments and improvements.

[0064] The disclosed subject matter may provide for one or more technical improvements over any pre-existing technique and any technique that has previously become routine or conventional in the art. Additional technical problem, solution and effects may be apparent to a person of ordinary skill in the art in view of the present disclosure. It is further noted than any descriptions of the hereby disclosed invention are meant to be descriptive in nature rather than limiting or restrictive.

[0065] Referring now to Figure 1 showing schematic illustrations of an exemplary physical activity monitoring device, in accordance with some exemplary embodiments of the disclosed subject matter.

[0066] In some exemplary embodiments, a monitoring Device 100a may be worn by or attached to Subject 110a while being in a first position, such during a physical therapy treatment, while holding a training Instrument 130a, or the like. Monitoring Devices 100b, 105b and 115b, may be worn by or attached to Subject 110b while being in a sitting position, while performing routine activity such as during working, in front of a computer, or the like. Monitoring Devices 100c and 105c may be worn by or attached to Subject 110c during sleeping. Monitoring Device lOOd may be worn by or attached to Subject lOOd during performing a sport activity, such as while playing with a Ball 130d. [0067] It may be noted that Devices 100b, 105b and 115b may be separated devices, independent devices, or the like. Devices 100b, 105b and 115b can communicate therebetween, may be associated with Body Area Network (BAN) capabilities, or the like. Additionally or alternatively, Devices 100b, 105b and 115b may be components of a single device, that can be operated simultaneously, by the same controller, or the like. Subjects 110a, 110b, 110c and llOd may be the same person, different persons, or the like. Devices 100a, 100b, 105b, 115b, 100c, 105c and 100b, may be the same device, similar devices, devices with similar capabilities, or the like.

[0068] In some exemplary embodiments, Device 100a may be a wearable monitoring unit, configured to monitor, diagnose, alert, provide feedback to Subject 110a, or the like. Additionally or alternatively, Device 100a may be configured to provide sensor readings and other data to an operating system, such as a digital system, a clinical decision system, or the like. The system may be operated by a Care Taker 120a, or any other data or a medical data expert, physical therapist, physician, trainer, or the like. The operating system may be configured to provide analyzed data, recommendations, or other types of output to Care Taker 120a via operated via a designated Device 125a. Additionally or alternatively, the system may be configured to provide output to Subject 100a, such as via Device 100a (using haptic tactile feedback, light indications, sound indications, vibration indications, or the like), using personal Device 115a of Subject 100a or any other alerts and data transferring deemed preferable.

[0069] In some exemplary embodiments, the operating system may be an automatic system operated by an external server (not shown) and connected to Device 100a and Device 125a, connecting therebetween, obtaining input therefrom, providing output thereto, or the like. Additionally or alternatively, the operating system may be located on Device 110a, on Device 125a, or the like.

[0070] In some exemplary embodiments, Device 100a may comprise motion sensors configured to continuously monitor motions performed by an organ of Subj ect 110a, such as the lower back, the upper back, the hand, or the like. Device 100a may further comprise an array of electromyography sensors configured to selectively evaluate electrical activity produced by one or more skeletal muscles of Subject 110a. Additionally or alternatively, Device 100a may comprise other sensors such as ROM sensors, muscle conductivity, heart rate, blood pressure, visual sensors providing video or images of the subj ect, inertial measurement unit (IMU), or the like.

[0071] In some exemplary embodiments, the motion sensors may be configured to measure orientation, Range Of Motion (ROM), or other motion aspects of Subject 110a. The motion sensors may be configured to identify any motion that exceeds a predetermined angle, changes in the motion angle, or the like. Additionally or alternatively, Device 100a may comprise a 9-axis orientation sensor. The 9-axis orientation sensor may be a combination of a 3-axis acceleration sensor, a 3-axis gyroscope and a 3-axis geomagnetic sensor. The sensors may be combined, separated, or the like. Additionally or alternatively, Device 100a may comprise may comprise other combinations of orientation sensors, such as manometers, Global Positioning System (GPS) sensors, gyroscopes, inclinometers, magnetometers, seismometers, vibration sensors, temperature sensors, sweat sensors, or the like.

[0072] In some exemplary embodiments, Device 100a may be attached to a certain organ of the body, such as the neck, pelvis, lower back, or the like, in a manner enabling the one or more motion sensors to sense and record the movement pattern performed by the certain organ. Additionally or alternatively, Device 100a may be attached to a different organ, adjacent to the certain organ, distinct from the certain organ, or the like, in order to position the one or more sensors in an appropriate positioning for sensing without limiting the subject's movement or affecting her daily routine. In some exemplary embodiments, motion detected in a certain organ may affect other organ. Thus, electromyography sensors monitoring muscles associated with a certain organ may be automatically activated based on analysis of motion senor readings associated with another organ. As an example, movement of the neck and the arms may affect the lower back. LBP. As another example, movement of the back and pelvic may cause neck pain.

[0073] In some exemplary embodiments, Device 100a may comprise multiple components. Each component may be located on a different portion of the body, thereby providing a more accurate sensing of the movement of the subject, enabling simultaneously monitoring movements of different organs, enabling identification of movement patterns associated with more than one organ, or the like. In some exemplary embodiments, different components may comprise the same motion sensors. Additionally or alternatively, different types of motion sensors may be located on different components of the monitoring device, such as based on the organ being sensed, based on the location on which the component is configured to be placed, or the like. Additionally or alternatively, the one or more motion sensors may be centralized in a single component or a portion of the components of the monitoring device, while other components may not comprise motion sensors.

[0074] In some exemplary embodiments, the relevant portion of the array of electromyography sensors may be automatically selected and activated based on analysis of sensor readings of said motion sensor. The selection of the relevant portion may be performed automatically by the operating system, based on previous data analysis, based on medical history of Subject 110a, based on information provided by Subject 110a, using machine learning techniques, or the like. The selective activation and deactivation of the electromyography sensors may be performed by Device 100a, by the operating system, by a controller on Device 100a or external thereto, or the like. In some exemplary embodiments, Subject 110a may be enabled to control activation and deactivation of the electromyography sensors, via Device 100a, using Device 115a, or the like. [0075] It may be noted that Device 100a may be associated with electronic and/or mechanical abilities implemented therein, in order to facilitate the different capabilities and related data. As an example, Device 110a may be battery operated, rechargeable, or the like. Additionally or alternatively, Device 100a may be associated with Wi-Fi connection, Bluetooth, or any other wireless communication options, to enable communication with other devices, such as Device 125a, Device 115a, server, or the like. Additionally or alternatively, Device 100a may be associated with BAN abilities, enabling to communicate with other wearable products or devices of Subject 110a, such as smartwatch, blood pressure sensors, thermometer, or the like. Referring again to the sleep monitoring embodiment, Device 100a may be configured to obtain data related to the state and quality of sleep from smart watches, for example, and may integrate such data with the motion data analysis.

[0076] In some exemplary embodiments, Device 100a may be associated with computational abilities and resources enabling preforming the required analysis on the fly, retaining sensor readings, providing recommendation, IoT capabilities, or the like.

[0077] Additionally or alternatively, Device 100a may be associated with treatment capabilities, such as pain reliving capabilities, curing capabilities, or the like. As an example, Device 100a may be configured to apply low intensity vibration on Subject 110a using integrated vibration motors, applying a heat therapy using cooling and heating components, applying local electrical pulses for TENS treatment, or the like. Additionally or alternatively, Device 100a may be associated with feedback capabilities, such as tactile feedback actuator, sound, light, or the like.

[0078] In some exemplary embodiments, Device 100a may be worn by Subject 110a as a belt, however, Device 100a may be shaped in different wearables shapes based on the body area required to be monitored, such as a shirt, a vest, un upper back belt (Device 100b), a collar belt (Device 105b), a bracelet shape (Device 105c), or the like.

[0079] In some exemplary embodiments, Device 100a may be operated by Subject 110a, using buttons, touch screens, verbal control component, or the like. Additionally or alternatively, Device 100a may be controlled by Subject 110a using a mobile application installed on Device 115a, by contacting Care Taker 120a, or the like. [0080] Device 100a may be configured to automatically report data (e.g., sensor readings, recorded electrical activity, or the like) to the operating system, to Care Taker 120a via Device 125a, or the like. Additionally or alternatively, Device 100a may be configured to directly provide output or instructions to Subject 110a via Device 115a that may be a designated device, a non-designated device such as personal computing device, a mobile phone, or the like.

[0081] In some exemplary embodiments, Device 100a may be configured to be worn by Subject 110a for a predetermined duration, such as for 24 hours, 48 hours, few days, or the like. The predetermined duration may be set automatically based on the symptoms of Subject 110a, based on the treatment process, based on the monitoring process, or the like. Additionally or alternatively, the predetermined duration may be set by Care Taker 120a. The predetermined duration may be designated to cover daily routine of Subject 110b, such as during working, over the night to cover sleeping positions and sleeping quality (110c), during performing sport activity to enable monitoring the treatment execution by Subject lOOd, or the like. This may be done in order to prevent or reduce the need for clinical treatment, to recommend the types of treatments both beneficial and best suiting the physical attributes of Subject 100a, to manage goals and timelines for the treatment being performed by the Subject 100a, to predict the effects of specific treatments on specific patients or the like. Additionally or alternatively, Device lOOd may be utilized to monitor physical activity and muscular reaction during training, practicing a certain sport, or the like. Device lOOd may be configured to provide a subjective feedback on the training efficiency such as by measuring the effect of a particular workout or exercise on a specific muscle and its development, identifying whether Subject llOd is performing the training correctly or not and optimize it accordingly, or the like.

[0082] Referring now to Figures 2A and 2E showing a flowchart diagram of a method, in accordance with some exemplary embodiments of the disclosed subject matter.

[0083] On Step 210, a treatment plan for a subject may be obtained. The treatment plan may comprise instructions to the subject to perform a physical action. In some exemplary embodiments, the treatment plan may be a medical treatment plan, such as physical therapy plan, provided to the subject (e.g., a patient) based on her medical history, to treat a medical situation, to relief pain, or the like. The medical situation may be associated with a chronic disease, a long-term condition, an acute condition, or the like. Additionally or alternatively, the treatment plan may be a physical training plan, such as sport training plan, physical therapy exercises, movement teaching and guidance, yoga, dance lessons, rehabilitation activities, or the like.

[0084] On Step 220, the subject may be monitored during performing the physical action using a device attached to a body of the subject, such as Devices 100a, 100b, 105b, 115b, 100c, 105c and lOOd illustrated in Figure 1.

[0085] On Step 225, sensor reading may be obtained from a motion sensor and an electromyography sensor located on the device. In some exemplary embodiments, the motion sensor may be configured to continuously monitor motions performed by an organ of the subject. The electromyography sensor may be configured to selectively monitor electrical activity produced by a skeletal muscle of the subject. In some exemplary embodiments, the sensor readings may comprise motion sensor readings from the motion sensor that are associated with the physical action. Additionally or alternatively, the sensor readings may comprise recordings of electrical activity produced by the skeletal muscle of the subject when the subject performed the physical action, as recorded by the electromyography sensor.

[0086] In some exemplary embodiments, the electromyography sensor may be selected and activated based on an analysis of the motion sensor readings, such as depicted on Figure 2D. The activation of the selected electromyography sensors may be triggered by an identification of an intention of the subject to perform the physical action.

[0087] Additionally or alternatively, the motion sensor readings may be analyzed to identify exceptional events, such as irregular movements, pain gestures, or the like. The electromyography sensor may be automatically activated based on identification of such exceptional events. On Step 230, sensor readings obtained from the device may be automatically analyzed. In some exemplary embodiments, the sensor readings may be analyzed using machine learning techniques, medically trained classifiers, or the like. Additionally or alternatively, the automatic analysis may be performed with respect to medical records of the subject, in accordance with expert or care-takers input, or the like. [0088] In some exemplary embodiments, the automatic analysis of the sensor readings may be performed to determine physical capability or incapability of the subject to perform the physical action. The motion sensor readings may be analyzed to determine whether the subject completed performing the physical action, whether the subject performed the physical action in a correct form, or the like. Additionally or alternatively, the recordings of electrical activity produced by the skeletal muscle of the subject while performing the physical action may be analyzed to determine whether the body of the subject is capable of performing the physical action, response of the muscles thereto, effectivity of performing the physical action for achieving the treatment goal, potential harm or damage of performing the physical action on the subject's body, or the like.

[0089] In some exemplary embodiments, the analysis of the sensor readings may be performed with respect to additional criteria, in order to establish a certain analysis, or the like. Several examples of types, goals, or sub-steps of Step 230 are provided in Figure 2E. [0090] On Step 232e, the sensor readings may be analyzed to determine a movement pattern indicative of mental detachment of the subject with respect to performance of the physical action.

[0091] On Step 234e, the sensor readings may be analyzed to determine movement habits of the subject and reaction of the skeletal muscle thereto. [0092] On Step 236e, a physical diagnosis of the subject may be automatically evaluated based on the automatic analysis of the sensor readings.

[0093] On Step 238e, an updated clinical goal may be automatically determined based on the automatic analysis of the sensor readings. In some exemplary embodiments, the treatment plan may be devised to achieve a clinical goal. The clinical goal may be updates in accordance with analysis of the sensor readings.. It may be noted The updated clinical goal may be required to be aligned with the original clinical goal of the treatment and within predetermined thresholds boundaries provided by the care taker, or the CPG. The predetermined threshold and boundaries may be generated manually (e.g., by a physician, a care taker, or the like), automatically (e.g., based on CPG, by a CDSS, or the like), a combination thereof, or the like. As an example, the treatment plan may be originally devised to achieve the clinical goal of "completing a sitting duration of more than two hours without substantial pain". Based on the analysis of the sensor readings, the goal may be updated to "completing a sitting duration of at least 1.5 hours without substantial pain".

[0094] On Step 240, the treatment plan may be modified based on the automatic analysis of the sensor readings. In some exemplary embodiments, the modified treatment plan may comprise an instruction to the subject to perform a modified physical action instead of the physical action. The modified physical action may be determined by dynamically adapting the physical action to the subject based on the sensor readings (Step 242). Additionally or alternatively, the modified treatment plan may comprise a recommendation to avoid performing an action. The action to be avoided may be determined based on the sensor readings obtained from the device (Step 246).

[0095] In some exemplary embodiments, the modification of the treatment plan may be performed with respect to additional criteria, based on specific analysis results, or the like. Several examples of modifications, adaptations or sub-steps of Step 240 are provided in Figure 2E.

[0096] On Step 242e, the treatment plan may be modified in order to increase adherence of the subject in performing the modified treatment plan compared to the subject’s adherence to the performance of the treatment plan.

[0097] On Step 244e, the treatment plan may be adapted to the movement habits of the subject, based on sleeping habits of the subject, physical position during the day, or the like. As an example, the treatment plan may be updated based on sleeping position causing back tension according to the sensor readings. The system may provide an alternative activity that can help relief back tension. As another example, the system may change the activities order, update activity or any other approved activity change, based on identified sitting positions preferred by the subject, to help relief back pain, neck tension or other unwanted phenomenon.

[0098] On Step 246e, the treatment plan may be further modified based on an updated physical diagnosis evaluated based on the sensor readings.

[0099] On Step 248e, the treatment plan may be further modified to achieve the updated goal. [0100] Additionally or alternatively, the treatment plan may be further modified based combination of demographic data and behavioral data (Figure 2B).

[0101] Additionally or alternatively, the treatment plan may be further modified based on subjective feedback provided by the subject (Figure 2C), other objective feedback provided by other devices or sensors (such as sleeping quality measurements provided by a smart watch), or the like.

[0102] On Step 250, a duration of a treatment may be automatically updated according the modified treatment plan. In some exemplary embodiments, the initial treatment plan may be associated with a predetermined duration, based on medical restrictions or needs. The predetermined duration may be automatically determined based on CPGs, manually set by a physician or a physical therapist, based on the medical history of the subject, or the like. In some exemplary embodiments, the duration of the treatment may be updated based on the sensor readings analysis, such as based on the reaction of the patient's body to the treatment, based on ability to perform the treatment, based on physiological elements, or the like.

[0103] On Step 260, the modified treatment plan may be provided to the subject. Additionally or alternatively, the modified treatment plan may be first provided to an expert, such as a physical therapist or trainer, a physician, or the like, for further analysis or adaptation, or for further inspection, validation, or the like, before being provided to the subject. Additionally or alternatively, the treatment plan modification process (e.g., Step 240) may be performed in association with a human expert assistance and validation.

[0104] In some exemplary embodiments, the modified treatment plan may be provided automatically to the subject via a computing device, using a dynamic treatment provided by the monitoring device itself or any other medical device, or the like. Additionally or alternatively, a notification may be provided to the subj ect or the expert in real-time, such as to inform the subject of performing wrong or harmful motions or movements, either in the course of treatment or in day-to-day life. Other means may be utilized for providing the modified treatment plan, such as an instruction manual, visual, audio, a combination thereof, or the like. [0105] It may be noted that the modified treatment plan may be provided to the subj ect and/or the care taker prior to the treatment. Additionally or alternatively, other actions may be performed with respect to the modified treatment plan, such as instructing the subject to replace the monitoring device, changing a position of the electromyography sensors, or the like.

[0106] In some exemplary embodiments, the modified treatment plan may comprise autonomous prioritizing and recommendation of treatment options, while enabling the subject to select a preference of one treatment type or protocol over the another.

[0107] It may be noted that each of Figures 2B-2E may show complementary steps to the method diagramed in Figure 2A. Each of methods diagramed in Figures 2B-2E may be performed independently, in accumulation with the method diagramed in Figure 2A, in combination with one or more steps of methods diagramed in the other figures, or the like.

[0108] Referring now to Figure 2B showing a flowchart diagram of a method, in accordance with some exemplary embodiments of the disclosed subject matter.

[0109] On Step 210b, non-clinical demographic parameters of the subject may be obtained.

[0110] On Step 220b, the subject may be monitored during performing the physical action, in accordance with the non-clinical demographic parameters.

[0111] On Step 240b, the treatment plan may be modified based on combination of demographic data (non-clinical demographic parameters) and behavioral data as recorded by the sensor readings.

[0112] Referring now to Figure 2C showing a flowchart diagram of a method, in accordance with some exemplary embodiments of the disclosed subject matter.

[0113] On Step 225c, subjective feedback related to performing the physical action may be obtained from the subject. The subjective feedback may be indicative of difficulty level of the treatment as perceived by the subject, pain level, or the like. As an example, the subject may report pain when reaching maximum range of motion, discomfort in performing a particular action or personal discomfort from performing a particular exercise (which can be caused by psychological or physical factors). [0114] In some exemplary embodiments, the subjective feedback may be obtained via the monitoring device, by providing question or queries to the subject, using a mobile device application, using other IoT devices, or the like.

[0115] In some exemplary embodiments, the monitoring device or system may have a self-feedback option, enabling the user to provide into the system's data-bank her own impressions and observations regarding the treatment, either in the course of the treatment session itself or afterwards.

[0116] On Step 230c, the sensor readings may be analyzed in accordance with the subjective feedback. As an example, a feedback from the subject indicating a certain level of pain associated with preforming the physical action may be analyzed with respect to the objective physical reaction of the body as recorded by the electromyography sensors, based on the correctness of performing the physical action as recorded by the motion sensors, or the like.

[0117] On Step 240c, the treatment plan may be modified based on the subjective feedback. As an example, more difficult exercises may be added in case the subject reports on non-hard actions, or another predefined exercise, with a higher level of difficulty, depending on the of the system restrictions, or the like. As yet another example, the number of repetitions in a given exercise, the length of stay in a given range of motion, may be increased, decreased, or the like. As yet another example, exercise performance may be updated to include a greater range of motion and repetitions.

[0118] Referring now to Figure 2D showing a flowchart diagram of a method, in accordance with some exemplary embodiments of the disclosed subject matter.

[0119] On Step 225d, motion sensor readings may be obtained from the motion sensor of the monitoring device.

[0120] On Step 23 Id, the sensors readings may be analyzed to identify movement patterns of the subject. In some exemplary embodiments, a movement pattern of the subject may be identified based on the data obtained from the one or more motion sensor. In some exemplary embodiments, the movement pattern may be identified by calculating an angle of a position of the subject. The angle may be compared to predetermined angles associated with the previous positioning, such as from a personal dataset associated with the subject, from a general database, based on physical and geometrical analysis, or the like. Additionally or alternatively, the movement pattern may be identified using machine learning classifiers trained based on dataset of sensor readings and manually annotated movement patterns of the subject. In some exemplary embodiments, the movement pattern may be determined using a combination of statistical characteristics of the accelerometer readings, and other sensors readings. As an example, a movement pattern may be classified and distinguished from other activities by detecting constant constraint vibrations occurring while performing a certain action compared to abrupt accelerations when preforming another action or very low acceleration when the organ is not moving. As another example, the direction of the movement may be determined based the change of the tilt rotation vector of the organ as sensed by the one or more motion sensors.

[0121] On Step 232d, an identification that the subject is intending to perform the physical action may be automatically performed based on analysis of the movement pattern. In some exemplary embodiments, the movement pattern may be consistent with a predetermined and learned movements patterns associated with the physical action. As an example, a movement pattern indicative of seated rotation or low back rotation stretch may comprise sitting (such as on a seat, a chair, or the like), keeping the feet flat on the floor, rotating the core to the right or left, and keeping the hips fixed and the spine high. As another example, the movement pattern may be indicative of lifting a high weight, over stretching an organ, bending an organ (such as the back) over a predetermined bending angle, or the like.

[0122] On Step 233d, the monitoring device may be automatically instructed to activate the electromyography sensor with respect to the relevant skeletal muscle. In some exemplary embodiments, the selection of which electromyography sensor to be activated, the activation time duration (Step 234d), or the like, may be performed based on analysis of the movement pattern of the subject, the physical action, capabilities of the sensor, or the like. On Step 235d, the electromyography sensor records (recordings of electrical activity produced by the skeletal muscle of the subject when the subject performed the physical action, as recorded by the electromyography sensor) may be obtained from the monitoring device. [0123] Referring now to Figure 3 showing a block diagram of a system, in accordance with some exemplary embodiments of the disclosed subject matter.

[0124] In some exemplary embodiments, System 300 may be utilized to manage a communication between a User 380, such as a patient, a client, a trainee, or the like, and an Expert 390, such as a physical therapist, a physician, a sport trainer, or the like. In some exemplary embodiments, System 300 may be a health personalized system, configured to provide personalized treatment recommendation levels, to fit patient’s day to day routine constraints. System 300 may be utilized for physiotherapy or other physical-related treatments. System 300 may be configured to provide, for User 380 or Expert 390 personalized treatment recommendations, based on specific physical movements and capabilities of User 380. Additionally or alternatively, System 300 may be utilized for physical training or sport performance monitoring. Additionally or alternatively, System 300 may be a tele-health system with a synchronous (store-and- forward) management and communication. System 300 may be utilized in diagnostic processes, pre-treatment analysis processes, treatment processes, rehabilitation processes, or the like.

[0125] In some exemplary embodiments, System 300 may be a layered CDSS, configured to process patient comorbidities, personal aspects, other layers of background information, subjective feedback, psychological aspects, behavioral aspects, or the like; along with real-time physical data. System 300 may be configured to maintain treatment recommendations and management for User 380. System 300 may be configured to prioritize treatment recommendations for User 380, based on an inclusive and layered assessment of the patient's overall condition, context, or the like. Additionally or alternatively, System 300 may be configured to update medical diagnosis and prioritize treatment recommendations based on real-time input provided by one or more wearable or attachable devices, worn or attached to User 380, such as Device 385. In some exemplary embodiments, System 300 may be configured to learn the movements, constraints and capabilities of User 380 and adjust personal recommendations, determine treatment preferences, providing feedback, or the like. [0126] In some exemplary embodiments, System 300 may comprise an Apparatus 301 configured to manage decision making, communicate with the wearable devices, or the like. Apparatus 301 may be a server, a multi-server system, or the like. Apparatus 301 may be configured to support parallel user interaction with a real-world physical system and a digital representation thereof, in accordance with the disclosed subject matter.

[0127] In some exemplary embodiments, Apparatus 301 may comprise one or more Processor(s) 302. Processor 302 may be a Central Processing Unit (CPU), a microprocessor, an electronic circuit, an Integrated Circuit (IC) or the like. Processor 302 may be utilized to perform computations required by System 300 or any of its subcomponents.

[0128] In some exemplary embodiments of the disclosed subject matter, Apparatus 301 may comprise an Input/Output (I/O) module 305. I/O Module 305 may be utilized to provide an output to and receive input from a user, such as, for example a physical trainer, a sport trainer, a physician (e.g., Expert 390); a client, a patient, a trainee (e.g., User 380) or the like. Additionally or alternatively, EO Module 305 may be utilized to provide an output to and receive input from devices, such as monitoring devices worn by subjects (e.g., Device 385), other IoT devices of User 380, such as Smartwatch 382, Device 384, or the like; devices of Expert 390, such as Device 395 or any other computing device, server, or the like. Additionally or alternatively, EO Module 305 may be utilized to provide an data to and receive data from a Database 397 of System 300.

[0129] In some exemplary embodiments, Apparatus 301 may comprise Memory 307. Memory 307 may be a hard disk drive, a Flash disk, a Random Access Memory (RAM), a memory chip, or the like. In some exemplary embodiments, Memory 307 may retain program code operative to cause Processor 302 to perform acts associated with any of the subcomponents of System 300.

[0130] In some exemplary embodiments, EO Module 305 may be utilized to obtain sensor readings from a Device 385. Additionally or alternatively, EO Module 305 may be utilized to provide feedback to User 380, data to Device 395, instructions to Device 385, or the like.

[0131] In some exemplary embodiments, System 300 may behave as an asynchronous communication system (store-and-forward), a synchronous (real-time) communication system, a combination therebetween, or the like. System 300 may be configured to analyze information from several body areas and segments simultaneously, thus creating a BAN scheme. In some exemplary embodiments, System 300 may be configured to predict the outcomes and timelines of specific treatment plans and parameters in regard to specific patients, before the start of treatment as well as during an ongoing one.

[0132] In some exemplary embodiments, System 300 may be configured to provide pre- treatment feedback, in-treatment feedback, or the like, to both therapist (e.g., Expert 390) and patient (User 380). Additionally or alternatively, System 300 may be configured to provide a post-treatment feedback in order to ascertain the effectiveness of the treatment.

[0133] In some exemplary embodiments, Device 385 may be an attached to a body of a User 380, such as using an on-body sticker, a sticky pad, a medical glue, bandage, or the like. Additionally or alternatively, Device 385 may be a wearable device configured to be worn by User 380, such as a belt, a head-band, a bracelet, or the like. Device 385 may be capable of tracking and analyzing the movements of different body areas and segments of User 380, based on their effect on core movement in a singular body area- such as, but not limited to, the lower back area. [0134] In some exemplary embodiments, Device 385 may comprise Motion Sensors

370 configured to continuously monitoring motions performed by User 380 and Electromyography Sensors 375 configured to selectively monitor electrical activity produced by a skeletal muscle of User 380. In some exemplary embodiments, communication with Device 380, activation and deactivation thereof, or the like, may be performed by Monitoring Module 310.

[0135] In some exemplary embodiments, Analysis Module 320 may be configured to analyze data obtained from Device 385, from other devices associated with User 380, such as Smartwatch 382 and Device 384, data from Database 397, instructions provided by Expert 390, such as via Device 395, or the like. In some exemplary embodiments, Analysis Module 320 may comprise a Treatment Plan Analysis Module 323. Treatment Plan Analysis Module 323 may be configured to analyze treatment plans provided or recommended for patients, trainees, or the like (e.g., User 380). The treatment plans may be manual treatment plans generated by physician, trainers, care takers, or the like (e.g., Expert 390). Additionally or alternatively, the treatment plans may be automatically generated based on CPGs, using CDSS, or the like. In some exemplary embodiments, Treatment Plan Analysis Module 323 may be configured to identify certain recommendations or instructions related to physical movements, such as instructions to User 380 to perform a physical action.

[0136] In some exemplary embodiments, Monitoring Module 310 may be configured to manage monitoring of User 380 during performing the physical action. Monitoring Module 310 may be configured to instruct Device 385 to activate the relevant sensors, based on analysis of the treatment plan performed by Treatment Plan Analysis Module 323.

[0137] In some exemplary embodiments, Motion Analysis Module 321 may be configured to analyze sensor readings obtained from Motion Sensors 370 of Device 385 to identify movement patterns performed by User 380. In some exemplary embodiments, Motion Analysis Module 321 may be configured to analyze sensor readings from Motion Sensors 370 with respect to a specific body area f User 380 in order to determine movement patterns associated with other body areas of User 380. Motion Analysis Module 321 may be configured to analyze the data obtained from Device 385 with respect to the body part or area which Device 385 is worn on or attached to, in order to determine the positions and status of other body parts and areas of User 380, resulting in a comprehensive over all analysis of movements of User 380. Additionally or alternatively, Motion Analysis Module 310 may be configured to identify a movement pattern of User 380 indicative of intending to perform the physical action being analyzed. Such analysis may be utilized by EMG Activation/deactivation Module 315.

[0138] Additionally or alternatively, Motion Analysis Module 321 may be configured to analyze sensor readings obtained from Device 385 to determine movement habits of User 380 and reaction of the skeletal muscle thereto. As an example, Motion Analysis Module 321 may identify that User 380 sits for long durations, left heavy objects, sleep for less than 6 hours, or the like.

[0139] In some exemplary embodiments, Motion Analysis Module 310 may be configured to identify a movement pattern indicative of mental detachment of User 380 with respect to performance of the physical action. As an example, a pattern indicative of not-completing a physical action without the sensor readings being indicative of physical difficulty in completing the physical action. [0140] Additionally or alternatively, Analysis Module 320 may be configured to determine physical incapability of User 380 to perform the physical action. The physical incapability may be determined based on analysis of the motion sensor readings performed by Motion Analysis Module 321, such as a determination that User 380 is incapable of lifting objects above a certain weight. Not being able to bend beyond a certain angle, or the like. Additionally or alternatively, physical incapability may be determined based on analysis of the muscles activity during performing or attempt to perform the physical action, as performed by EMG Analysis Module 324. As an example, EMG Analysis Module 324 may be configured to detect unwanted muscle tension, in an attempt to perform an activity or lift a given weight, unwanted muscle tension in a sitting or standing position for prolonged periods (e.g., at an undesirable angle), or the like.

[0141] In some exemplary embodiments, EMG Activation/Deactivation Module 315 may be configured to activate Electromyography Sensors 375 located on Device 385, or instruct Device 385 or an operator thereof to activate Electromyography Sensors 375 or portion thereof, in response to identifying that User 385 performed movement pattern indicative of intendance to perform the physical action. In some exemplary embodiments, EMG Activation/Deactivation Module 315 may be configured to determine the relevant skeletal muscles on which the electromyography sensors are required to be activated based on the movement pattern identified by Motion Analysis Module 321. Additionally or alternatively, EMG Activation/Deactivation Module 315 may be configured to deactivate the electromyography sensors after a predetermined time duration elapses, or instruct Device 385 or an operator thereof to deactivate the electromyography sensors. The predetermined time duration may be automatically determined by EMG Activation/Deactivation Module 315 based on the identified movement pattern, the associated skeletal muscle, or the like. Additionally or alternatively, predetermined time duration may be set by an Expert 390 (e.g., a physical therapist, a physician, or the like), automatically by a Device 395 operated by Expert 390, or the like.

[0142] In some exemplary embodiments, EMG Analysis Module 324 may be configured to automatically analyze electrical activity recorded by the electromyography sensors of Device 385. In some exemplary embodiments, EMG Analysis Module 324 may be configured to analyze records that are associated with a certain movement pattern identified by Motion Analysis Module 310. Additionally or alternatively, EMG Analysis Module 324 may be configured to automatically analyze EMG recordings to recommend and forecast the effectivity of personalized treatment options and treatment types for User 380.

[0143] In some exemplary embodiments, BAN Module 328 may be configured to automatically manage communication with other wearable products or devices of User 380, such as Smartwatch 382, Device 384, blood pressure sensors, thermometer, or the like. BAN Module 328 may be configured to automatically analyze data obtained from the other devices to recommend and forecast the effectivity of personalized treatment options and treatment types for User 380. [0144] In some exemplary embodiments, Demographic Data Analysis Module 326 may be configured to analyze demographic data of User 380, and its effects on the treatment process. In some exemplary embodiments, the demographic data may be obtained directly from User 380. Additionally or alternatively, the demographic data may be automatically determined based on data obtained from other devices and analyzed by Ban Module 328, based on medical history, or the like. The demographic data may comprise age, gender, profession, education, background, address, or the like. In some exemplary embodiments, the demographic data may comprise at least one non-clinical demographic parameter. As an example, a non-clinical demographic parameter may be marital status. Such demographic parameter may be non-clinical as it may not affect the medical situation directly. However, such parameter may have different implications on the treatment process, such as in treatment requiring an assistance from another person, treatment requiring mental support, or the like. It may be noted that some demographic parameters may be considered as clinical related for certain medical situations but non- clinical for other. As an example, profession or work style may be clinical parameter for physical therapy, as it may affect the physical treatment and diagnosis. However, it may not be considered as clinical for other medical situations such as for diabetes, migraine, or the like. Additionally or alternatively, some demographic parameters may not considered as clinical for diagnosis (such as genetic disorders) however, may have clinical aspects for the treatment process. [0145] In some exemplary embodiments, Treatment Plan Modification Module 360 may be configured to modify the treatment plan of User 380, based on the analysis performed by Analysis Module 320 or components thereof. Treatment Plan Modification Module 360 may be configured to generate a modified treatment plan for User 380, that comprises an instruction to User 380 to perform a modified physical action instead of the physical action being analyzed. Treatment Plan Modification Module 360 may be configured to dynamically adapt the physical action to User 380 based on the sensor readings. Additionally or alternatively, Treatment Plan Modification Module 360 may be configured to generate an independent, new, treatment plan, that may or may not be based on the original treatment plan, or a plan provided by Expert 390. In some exemplary embodiments, Treatment Plan Modification Module 360 may be configured to obtain boundary instructions or parameter values, such as minimal and maximal thresholds, a set of optional instructions, or the like, from Expert 390, CPG, or the like.

[0146] In some exemplary embodiments, Treatment Plan Modification Module 360 may be configured to determine an instruction based on the automatic analysis of the recorded electrical activity performed by EMG Analysis Module 324 and the movement pattern identified by Motion Analysis Module 321.

[0147] In some exemplary embodiments, Diagnosis Module 361 may be configured to evaluate a physical diagnosis of User 380 based on the automatic analysis of the recorded electrical activity performed by EMG Analysis Module 324. Additionally or alternatively, Diagnosis Module 361 may be configured to detect routine, day to day movements, carried out by the User 380 before the start of treatment, which can cause symptoms of a condition of User 380, or the like. Additionally or alternatively, Diagnosis Module 361 may be configured to utilized analysis of movements of User 380 in order to provide recommendations that may, in certain cases, entirely eliminate the need for physical or clinical treatment. Treatment Plan Modification Module 360 may be configured to modify the treatment plan based on the updated physical diagnosis.

[0148] In some exemplary embodiments, the treatment plan may be devised to achieve a clinical goal. Clinical Goal Updating Module 362 may be configured to automatically update the clinical goal of the treatment plan based on the analysis performed by Analysis Module 320. Treatment Plan Modification Module 360 may be configured to modify the treatment plan based on the updated clinical goal. The modified treatment plan may be devised to achieve the updated clinical goal. As an example, Clinical Goal Updating Module 362 may be configured to automatically update treatment goal, to reduce spine pressure, in order to reduce the risks of movement limitations. As another example, Clinical Goal Updating Module 362 may be configured to automatically update treatment goal, to improve neck ROM, in order to reduce chronic headaches, resulting from a sitting, working or sleeping positions.

[0149] In some exemplary embodiments, Duration Module 365 may be configured to automatically update a duration of a treatment according the modified treatment plan. As an example, Duration Module 365 may be configured to automatically update the exercise repetitions, according to lifestyle changes (resulting work change or vacation). As yet another example, Duration Module 365 may be configured to automatically update exercises intensity reduction, stems from treatment goal update, e.g. in a situation that the goal was updated - instead of 2 hours sitting without pain to 1 hour sitting without pain, the exercises intensity may be reduced, by reducing repetitions, duration or any other update. [0150] In some exemplary embodiments, Treatment Plan Modification Module 360 may be configured to update a treatment plan for User 380 based on updating a treatment plan for the subject the automatic analysis of the recorded electrical activity performed by EMG Analysis Module 324 and the movement pattern identified by Motion Analysis Module 321. Additionally or alternatively, Treatment Plan Modification Module 360 may be configured to automatically update the treatment plan based on current treatment results and treatment definitions and parameters provided by Expert 390. Additionally or alternatively, Treatment Plan Modification Module 360 may be configured to determine a next treatment plan, such as automatically and autonomously update goals (using Clinical Goal Update Module 362), forecasts and treatment protocols from one treatment to the next, and update treatment duration of each protocol (using Duration Module 365) based on the performance of User 380 in former treatment sessions, or the like. Treatment Plan Modification Module 360 may be configured to dynamically update treatment goals for User 380, taking into consideration all the variables that arise during the course of treatment. Additionally or alternatively, Treatment Plan Modification Module 360 may be configured to determine day to day activities performed by User 380, which can inhibit or delay the effectiveness of the treatment. Treatment Plan Modification Module 360 may be configured to adjust goals and timelines regarding the treatment based on such information and analysis as well as adjust treatment preferences and predictions, thus enabling Expert 390 and User 380 to coordinate views during treatment, as well as determining and predicting the treatment's outcome and timelines.

[0151] In some exemplary embodiments, Treatment Plan Modification Module 360 may be configured to generate the modified treatment plan based on a combination of demographic data and behavioral data. Treatment Plan Modification Module 360 may be configured to combine analysis of demographic data, and specifically non-clinical demographic parameters analyzed by Demographic Data Analysis Module 326, with the behavioral data analyzed by Motion Analysis Module 321 and EMG Analysis Module 324, with the medical recommendations, to provide an optimized treatment plan, that is adapted to capabilities and physical abilities of User 380. Referring to the above example, a demographic parameter indicating that User 380 is in a relation, living with a partner, or the like, may be indicative that User 380 can get help, such as from a spouse, a partner, or the like, in performing some actions. As another example, a profession of User 380 may affect a duration of the treatment, such as due to availability of time and space for performing certain actions, fatigue elements, exhaustion, or the like; besides being with high relevance to diagnosis update, such as professions involving physical activity, lifting heavy items, sitting for a continuous duration, or the like.

[0152] In some exemplary embodiments, Treatment Plan Modification Module 360 may be configured to generate the modified treatment plan in a manner that increases adherence of User 380 in performing the modified treatment plan compared to the adherence thereof to the performance of the previous or original treatment plan. Treatment Plan Modification Module 360 may be configured to modify the treatment plan in response to identifying, by Motion Analysis Module 310, a movement pattern indicative of mental detachment of User 380 with respect to performance of the physical action.

[0153] In some exemplary embodiments, Treatment Plan Modification Module 360 may be configured to modify the treatment plan based on subjective feedback related to performing the physical action obtained from User 380. The subjective feedback may be provided by User 380 using Device 380, via Expert 390, directly, indirectly, or the like. Additionally or alternatively, the subjective feedback may be determined based on analysis of data provided by other devices of User 380, as analyzed by BAN Module 328. As an example, data from media of a mobile device of User 380, from Smartwatch 382, or the like. Treatment Plan Modification Module 360 may be configured to modify the treatment plan based on objective feedback on the treatment obtained from other devices related to User 380, such as blood pressure measurements obtained from Device 385, screening results provided by Expert 390, or the like.

[0154] In some exemplary embodiments, Apparatus 301 may be configured to provide the modified treatment plan to User 380, such as using a computing device, via Expert 390, or the like. Additionally or alternatively, Apparatus 301 may be configured to instruct Device 385 to issue an alert indicating User 380 performed a discommended action, instructing User 380 to perform or avoid performing a physical action, providing the results to Expert 390 for further analysis, or the like. In some exemplary embodiments, the actions determined and/or performed by Apparatus 301 may be one or more steps of a series of ongoing treatment two-way feedback, allowing both Expert 390 and User 380 to monitor progress, make treatment adjustments and set beneficial yet realistic goals.

[0155] In some exemplary embodiments, Apparatus 301 may be configured to instruct Device 385 or other computational device of User 385 to perform a certain action. Additionally or alternatively, Apparatus 301 may be configured to issue an alert to User 385, such as using Device 385 (haptic tactile feedback, auditory cue, or the like), via an application installed on a mobile device of User 385, or the like.

[0156] Additionally or alternatively, Apparatus 301 may be configured to establish connections with other health devices of User 380, such as, for example, a Smart Watch 382, a Smart Pressure Measurement Device 384, or the like, in order to receive additional patient information, enlarge Database 397 of System 300 for more accurate and comprehensive analysis processes, or the like.

In some exemplary embodiments, System 300 may be configured to store and retain sensor readings, analysis thereof, or the like, in Database 397 or in any other a designated database, in a general database, in a personal database or library associated with the subject, or the like. Additionally or alternatively, System 300 may be configured to identify and store interesting cases, e.g., cases relevant for the medical condition of Subject 380, cases relevant for additional training, or the like.

[0157] The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.

[0158] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non- exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0159] Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.

[0160] Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field- programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.

[0161] Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.

[0162] These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.

[0163] The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

[0164] The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. [0165] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly 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.

[0166] The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present invention has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.