A system and a method for muscle stimulation during a training session

Abstract

A system (100) for muscle stimulation during a training session The system (100) includes a compression pump (120) to regulate blood flow to a target muscle by applying controlled pressure with predetermined movements). An electrical stimulation unit (130) transmits a plurality of electrical impulses causing the target 5 muscle to contract and relax. A plurality of sensors (140) captures a plurality of physiological parameters associated with the target muscle during the muscle contractions. A user interface module (180) allows a user to monitor intensity of the electrical stimulation unit, blood flow regulation pressure, and tension of the plurality of elastic bands. A receiving module (190) receives the plurality of physiological 10 parameters from the plurality of sensors. A calculation module (200) calculates an optimal repetition count required to achieve a predetermined percentage in the target muscle performance based on a plurality of parameters.

Description

[0001] A SYSTEM AND A METHOD FOR MUSCLE STIMULATION DURING A TRAINING SESSION

[0002] EARLIEST PRIORITY DATE

[0003] This Application claims priority from a Complete patent application filed in India having Patent Application No. 202511000569, filed on January 2, 2025, and titled “A SYSTEM AND A METHOD FOR MUSCLE STIMULATION DURING A TRAINING SESSION”

[0004] FIELD OF INVENTION

[0005] Embodiments of the present disclosure relate to the field of muscle stimulation, and more particularly, a system and a method for muscle stimulation during a training session.

[0006] BACKGROUND

[0007] Currently, workout is a time-consuming and physically demanding, requiring a user to dedicate hours each week. Further, weightlifting and high intensity exercise in building strength causes wear and tear on the user's muscles and joints over time, increasing chance of injury and necessitating lengthy recovery times. Further, the requirement of the user to maintain a consistent workout schedule is challenging due to hectic lifestyles, limited gym access, or exhaustion that accompanies physical activity. Moreover, traditional exercise regimens lack methods for tracking real-time progress in muscle development and fat loss of the user. Results typically manifest over extended periods, leading to frustration when instant improvements or changes are not obvious, and individuals are often left guessing about the effectiveness of workout or the exercise. This absence of accurate, session-by-session feedback makes it difficult for the user to adjust routines for optimal results, contributing to inefficiency and potential loss of motivation.Hence, there is a need for an improved system for muscle stimulation which addresses the aforementioned issue(s).

[0008] OBJECTIVE OF THE INVENTION

[0009] An objective of the present invention is to provide a compression pump for muscle that enhances muscle activation and regulates blood flow to target muscles, facilitating efficient training sessions.

[0010] Another objective of the present invention is to capture a plurality of physiological parameters during muscle contractions, utilizing a plurality of sensors to deliver realtime feedback on muscle performance, thereby the user gains real-time feedback on muscle performance, that allows the user to track and adjust workout based on data received from the plurality of sensors.

[0011] An objective of the invention is to facilitate collection and processing of the plurality of physiological parameters by a receiving module and calculation module to optimize training experience by determining an ideal repetition count, thereby enabling the user to engage in effective workouts tailored to their specific fitness levels.

[0012] Yet, another objective of the invention is to allow the user to monitor performance of the muscle activity enabling accurate tracking the muscle performance and fatigue levels during a training session.

[0013] BRIEF DESCRIPTION

[0014] In accordance with an embodiment of the present disclosure, a system for muscle stimulation during a training session is provided. The system includes a compression pump embedded in a plurality of elastic bands wherein the compression pump is adapted to regulate a blood flow to a target muscle by applying controlled pressure with predetermined movements, wherein the compression pump includes an electrical stimulation unit configured to transmit a plurality of electrical impulses causing the target muscle to contract and relax thereby mimicking a natural muscle movement. Thecompression pump includes a plurality of sensors configured to capture a plurality of physiological parameters associated with the target muscle during the muscle contractions wherein the plurality of sensors includes a plurality of electromyography electrodes, heart rate monitors, motion sensors and inertial measurement units. The system includes a processing subsystem hosted on a server wherein the processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules including a user interface module configured to allow the user to monitor intensity of the electrical stimulation unit, blood flow regulation pressure, and tension of the plurality of elastic bands. The processing subsystem also includes a receiving module operatively coupled to the user interface module wherein the receiving module is configured to receive the plurality of physiological parameters from the plurality of sensors. Further, the processing subsystem also includes a calculation module operatively coupled to the receiving module wherein the calculation module is configured to calculate an optimal repetition count required to achieve a predetermined percentage in the target muscle performance based on a plurality of parameters, wherein the plurality of parameters includes a target muscle activation level, fatigue thresholds, and a training history.

[0015] In accordance with another embodiment of the present disclosure, a method for muscle stimulation during a training session is provided. The method also includes regulating, by a compression pump, a blood flow to a target muscle by applying controlled pressure with predetermined movements. Furthermore, the method includes transmitting, by an electrical stimulation unit, a plurality of electrical impulses causing the target muscle to contract and relax thereby mimicking a natural muscle movement. Moreover, the method includes capturing, by a plurality of sensors, a plurality of physiological parameters associated with the target muscle during the muscle contractions wherein the plurality of sensors includes a plurality of electromyography electrodes, heart rate monitors, motion sensors and inertial measurement units. Additionally, the method includes allowing, by a user interface module of a processing subsystem, the user to monitor intensity of the electrical stimulation unit, blood flow regulation pressure, and tension of the plurality of elastic bands. The method includes receiving, by a receivingmodule of the processing subsystem, the plurality of physiological parameters from the plurality of sensors. The method also includes calculating, by a calculation module of the processing subsystem, an optimal repetition count required to achieve a predetermined percentage in the target muscle performance based on a plurality of parameters, wherein the plurality of parameters includes a target muscle activation level, fatigue thresholds, and a training history.

[0016] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.

[0017] BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:

[0019] FIG. 1 is a block diagram representation of a system for muscle stimulation during a training session in accordance with an embodiment of the present disclosure;

[0020] FIG. 2 is a block diagram of an exemplary embodiment of system for muscle stimulation during a training session in accordance with an embodiment of the present disclosure;

[0021] FIG. 3(a) and 3(b) is a schematic representation of front view and rear view of a compression pump of FIG. 1, in accordance with an embodiment of the present disclosure;

[0022] FIG. 4 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure; andFIG. 5 illustrates a flow chart representing the steps involved in a method for muscle stimulation during a training session in accordance with an embodiment of the present disclosure.

[0023] Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.

[0024] DETAILED DESCRIPTION

[0025] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.

[0026] The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or subsystems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.

[0027] In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.

[0028] Embodiments of the present disclosure relates to muscle stimulation during a training session. The system includes a compression pump embedded in a plurality of elastic bands wherein the compression pump is adapted to regulate a blood flow to a target muscle by applying controlled pressure with predetermined movements. The compression pump includes an electrical stimulation unit configured to transmit a plurality of electrical impulses causing the target muscle to contract and relax thereby mimicking a natural muscle movement. The compression pump includes a plurality of sensors configured to capture a plurality of physiological parameters associated with the target muscle during the muscle contractions wherein the plurality of sensors includes a plurality of electromyography electrodes, heart rate monitors, motion sensors and inertial measurement units. The system includes a processing subsystem hosted on a server wherein the processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules including a user interface module configured to allow the user to monitor intensity of the electrical stimulation unit, blood flow regulation pressure, and tension of the plurality of elastic bands. The processing subsystem also includes a receiving module operatively coupled to the user interface module wherein the receiving module is configured to receive the plurality of physiological parameters from the plurality of sensors. Further, the processing subsystem also includes a calculation module operatively coupled to the receiving module wherein the calculation module is configured to calculate an optimal repetition count required to achieve a predetermined percentage in the target muscle performance based on a plurality of parameters,wherein the plurality of parameters includes a target muscle activation level, fatigue thresholds, and a training history.

[0029] FIG. 1 is a block diagram of a system for muscle stimulation during a training session, in accordance with an embodiment of the present disclosure. The system (100) includes a processing subsystem (150) hosted on a server (160). As used herein, the processing subsystem (150) may be hosted on the system (100) or a device. In one embodiment, the server (160) or the system (100) or the device may include a cloud-based server. In another embodiment, parts of the server (160) may be a local server coupled to a user device (not shown in FIG.l). The processing subsystem (150) is configured to execute on a network (170) to control bidirectional communications among a plurality of modules. In one example, the network (170) may be a private or public local area network (LAN) or Wide Area Network (WAN), such as the Internet. In another embodiment, the network (170) may include both wired and wireless communications according to one or more standards and / or via one or more transport mediums. In one example, the network (170) may include wireless communications according to one of the 802.11 or Bluetooth specification sets, or another standard or proprietary wireless communication protocol. In yet another embodiment, the network (112) may also include communications over a terrestrial cellular network, including, a global system for mobile communications (GSM), code division multiple access (CDMA), and / or enhanced data for global evolution (EDGE) network.

[0030] The system (100) includes a compression pump (120). The compression pump (120) further includes an electrical stimulation unit (130), and a plurality of sensors (140).

[0031] In one embodiment, the system (100) includes a wearable suit. The wearable suit is embedded with the compression pump (120), an electrical stimulation unit (130) and a plurality of sensors (140).

[0032] In one embodiment, the system (100) utilizes acupressure technique to enhance circulation, reduce swelling, and faster recovery in target muscle.The compression pump (120) is embedded in a plurality of elastic bands. In one embodiment, the compression pump (120) is integrated with air pressure (where a grip can tighten with air pressure) and BOA (the BOA is a mechanism that utilizes a wheel to adjust as required to tighten a fit). The compression pump (120) is adapted to regulate a blood flow to a target muscle by applying controlled pressure with predetermined movements. For example, when a user performs a leg extension, such as a squat or a simple stroll, the compression pump (120) tightens the plurality of elastic bands strategically around the user's muscles, applying regulated pressure to enhance blood circulation around the user’s leg. Further, the controlled pressure directs oxygenated and nutrient-rich blood flow to the leg muscles, resulting in faster recovery and the muscle growth. Furthermore, when the user moves, the compression pump (120) adjusts the pressure in real-time. For example, during the squat or calf raise, as the user bends their knees, the compression pump (120) exerts the pressure on the quadriceps and hamstrings. When the user returns to the original position, the pressure exerted by the compression pump (120) reduces, enabling the user's muscles to relax.

[0033] Typically, a method for enhancing muscle growth by regulating the blood flow to the target muscles. The method includes a plurality of techniques to optimize blood circulation to the target muscles during exercise or physical activity, thereby promoting the muscle growth, strength, and endurance. The method for enhancing the muscle growth includes, but is not limited to the following:

[0034] • Engaging in physical activity targeting specific muscles.

[0035] • Applying the plurality of techniques to regulate the blood flow to said specific muscles during said physical activity.

[0036] • Promoting increased blood circulation to said specific muscles to facilitate nutrient delivery and waste removal.

[0037] • Stimulating muscle hypertrophy and strength gains through optimized blood flow regulation.As used herein, the plurality of techniques for regulating the blood flow includes, but is not limited to the following:

[0038] • Utilization of compression garments to enhance venous return and arterial inflow to target muscles.

[0039] • Incorporation of intermittent blood flow restriction during exercise to induce ischemic conditions and subsequent hyperemia.

[0040] • Manipulation of exercise intensity, duration, and rest intervals to optimize blood flow dynamics.

[0041] • Application of heat therapy or thermogenic agents to dilate blood vessels and improve blood flow.

[0042] • Implementation of cold therapy or vasoconstrictive agents to reduce blood flow and enhance metabolic stress during exercise.

[0043] • Integration of breathing techniques to synchronize respiration with muscle contractions and enhance oxygen delivery to working muscles.

[0044] • Incorporation of massage or myofascial release to improve vascular function and tissue perfusion in target muscles

[0045] • Administration of vasodilators or nitric oxide precursors to augment vasodilation and increase blood flow to target muscles during exercise

[0046] • Administration of vasoconstrictors or sympathetic agonists to modulate blood flow distribution and optimize muscle perfusion

[0047] • Monitoring blood flow parameters using non-invasive techniques such as Doppler ultrasound or near-infrared spectroscopy

[0048] • Adjusting exercise protocols based on real-time feedback to maintain optimal blood flow conditions during training sessions

[0049] The compression pump (120) includes the electrical stimulation unit (130) configured to transmit a plurality of electrical impulses to the target muscles thereby causing the target muscles to contract and relax. This mimics natural signals sent by a brain during the training session. Normally, when the user performs a physical activity, the brain transmits electrical signals through a nervous system to stimulate muscle fibers,causing the muscle to contract and relax. The electrical stimulation unit (130) replicates above-mentioned process by directly transmitting electrical impulses to the target muscles by a plurality of electromyography electrodes positioned on the user’s skin. The electrical impulses trigger the muscle contractions. Typically, the electrical stimulation unit (130) causes slow-twitch (used for endurance) and fast-twitch (used for strength and power) muscle fibers to contract, providing benefits to strength, endurance, and recovery from training.

[0050] The compression pump (120) also includes a plurality of sensors (140) configured to capture a plurality of physiological parameters associated with the target muscles during the muscle contractions. The plurality of sensors (140) includes a plurality of electromyography electrodes, heart rate monitors, motion sensors and inertial measurement units.

[0051] Further, the plurality of sensors (140) includes, but is not limited to muscle force sensors, optimal repetition count sensors and a motion sensor.

[0052] The compression pump (120) includes the muscle force sensors configured to measure force exerted by a muscle during physical activities performed by the user.

[0053] The compression pump (120) also includes the optimal repletion count sensors configured to measure repletion count performed by the user.

[0054] Further, the compression pump (120) includes a plurality of electromyography electrodes utilized to measure muscle activity of the target muscles and fatigue levels during the training session.

[0055] Further, utilization of the plurality of electromyography electrodes provides a plurality of benefits, but is not limited to the following:

[0056] • Real-time feedback: The user receives immediate feedback on which the target muscles are being activated during exercise, ensuring that the correct muscles are engaged and reduces the risk of injury.• Improved Technique: By providing insights into muscle activation, EMG electrodes help the user to refine exercise technique. This is particularly useful for exercises requiring precise movements and muscle engagement. Further, the muscle engagement through the band or a mechanical method that wraps around enables muscle fibre recruitment. The muscle fiber recruitment is a process by which the user’s body activates muscle fibers to perform a movement or exert force. When the user exercises, the user’s body recruits various types of muscle fibers depending on intensity, duration, and nature of activity.

[0057] • Personalized Training: Data from the plurality of EMG electrodes allows for customized training programs based on the user muscle activation patterns, ensuring workouts are tailored to the user's specific needs and goals.

[0058] • Injury Prevention: By monitoring the muscle activity, the EMG electrodes help to identify imbalances or overuse of certain muscles, allowing the user to adjust training to prevent injuries.

[0059] • Enhanced Performance: Athletes and fitness enthusiasts utilize the data received from the EMG electrodes to optimize training, focusing on the most effective exercises for their muscle groups leading to better performance and faster progress.

[0060] • Rehabilitation: In rehabilitation, the EMG electrodes integration helps to track muscle recovery and guide exercises to strengthen the target muscles, aiding in the rehabilitation process.

[0061] • Scientific Research: EMG-integrated devices provide valuable data for sports scientists and researchers studying muscle function and exercise physiology. This can lead to new insights and advancements in training techniques.

[0062] The compression pump (120) features recovery and healing. The recovery and healing are enhanced through integration of pressure points, electrical muscle stimulation (EMS), blood flow construction (BFC), and a compression technique, whichcollectively facilitate faster recovery tailored to the target muscles managed by a machine learning model.

[0063] Further, the compression pump (120) also features a Blood flow constriction (BFC). The BFC is a technique used to control the blood flow to the target muscles with strains and constriction, followed by eventual release of the blood flow, which allows for significant muscle engagement and recovery. Further, Blood Flow Constriction through Bands is utilized to restrict the blood flow to the muscles during exercise to promote muscle growth, strength, and recovery by limiting the amount of blood that can leave the muscle while still allowing blood to enter.

[0064] In one embodiment, the compression pump (120) is integrated with a bioelectrical Impedance Analysis (BIA) allows an artificial intelligence (Al) model to monitor and analyze precise changes. The precise changes includes, but is not limited to body composition, such as fat loss, muscle gain, and hydration levels. By measuring electrical impedance across body of the user, the artificial intelligence (Al) model provides real-time insights into the user's physical transformation, enhancing accuracy of fitness assessments. Further, the artificial intelligence model develops a dashboard that allows the user to set personalized fitness goals and automatically tracks the user’s progress. Typically, the BIA includes BIA sensors. The BIA sensors are electrodes positioned on the user’s body that transmits and receives electrical signals, measuring impedance.

[0065] The system (100) includes a processing subsystem (150) hosted on a server (160). The processing subsystem (150) includes a user interface module (180), receiving module (190), and calculation module (200).

[0066] The user interface module ( 180) is configured to allow the user to monitor intensity of the electrical stimulation unit (130), blood flow regulation pressure, and tension of the band. Typically, the intensity of the electrical stimulation unit (130) is a strength of electrical impulses applied to the target muscles, that can be adjusted to suit individualcomfort levels and therapeutic needs, thereby ensuring effective muscle activation without causing pain.

[0067] As used herein, the blood flow regulation pressure pertains to a controlled pressure applied to affected area or the target muscles, which is crucial for enhancing circulation and facilitating recovery.

[0068] In one embodiment, a 'deep learning model is utilized to predict workout intensity and recovery needs, adjusting for consistency and improvements in the muscle activation.

[0069] Yet, in another embodiment, the EMG electrodes are utilized in conjunction with computer vision to analyze the muscle activity and body alignment during the exercise and provide real-time feedback on the target muscles being engaged based on the data received from the plurality of sensors (140).

[0070] Further, implementing the artificial intelligence model to analyze muscle recruitment and a motor unit activation, predicting future muscle growth and fat loss milestones. For example, the Al-powered model predicts when the user achieves specific fitness milestones, such as lifting a weight of 100 kilograms.

[0071] Furthermore, the artificial intelligence model utilizes usage of real-time biometric data of the user to determine optimal recovery time based on muscle fatigue, mitochondria performance, and overall body stress of the user. The artificial intelligence model also customizes recovery recommendations, including nutrition, hydration, and rest, to maximize muscle growth to the user.

[0072] The artificial intelligence model is leveraged to generate muscle composition projections, displaying appearance of the user after achieving specific fitness milestones. Further, integrating predictions into a visual representation, allowing the user to view progress in the real-time.

[0073] The processing subsystem (150) also includes a receiving module (190) operatively coupled to the user interface module (180). The receiving module (190) is configuredto receive the plurality of physiological parameters from the plurality of sensors (140). Typically, the plurality of physiological parameters includes, but is not limited to electromyography signals indicating muscle activation and fatigue, heart rate variability metrics reflecting autonomic nervous system activity and overall physiological stress levels, blood lactate levels indicative of metabolic demand and muscle fatigue and a range of motion measurements assessing muscle flexibility and joint mobility.

[0074] Further, the processing subsystem (150) includes a calculation module (200) operatively coupled to the receiving module (190). The calculation module (200) is configured to calculate an optimal repetition count required to achieve a predetermined percentage in the target muscle performance based on a plurality of parameters. The plurality of parameters includes a target muscle activation level, fatigue thresholds, and a training history. Typically, a method for determining the optimal repetition count during the training session is as follows:

[0075] • Collecting a baseline performance data, including maximum repetition (RM) tests, muscle activation measurements, and fatigue thresholds, from the user engaging in resistance training.

[0076] • Analyzing the baseline performance data to establish individualized performance benchmarks and identify areas for improvement towards a 10% increase in muscle performance.

[0077] • Calculating additional repetitions needed to reach desired performance improvement based on metrics of the baseline performance data and established performance targets.

[0078] • Adjusting repetition count recommendations for subsequent training sessions to progressively challenge the muscles and facilitate continued performance gains.

[0079] Further, the baseline performance data includes, but is not limited to the following:• Maximum repetition (RM) test results indicating maximum number of repetitions the user can perform with a given load before reaching fatigue. • Muscle activation measurements obtained through the plurality of electromyography electrodes or other muscle activity tracking methods, providing insights into muscle recruitment patterns and activation levels during exercise.

[0080] • Fatigue thresholds determined from performance fatigue tests or subjective ratings of perceived exertion (RPE), reflecting individual tolerance to exercise- induced fatigue and recovery capacity.

[0081] Further, analyzing the baseline performance data includes comparing baseline performance metrics to established norms, population averages, or performance benchmarks within a training population or demographic group and identifying areas of improvement and setting specific, measurable, and achievable goals for performance enhancement, such as a 10% increase in maximum repetition capacity or muscle activation levels.

[0082] Further, calculation of additional repetitions needed to achieve a 10% increase in the muscle performance by the user includes, but is not limited to following:

[0083] • Determining current performance level based on metrics of the baseline performance data and establishing a target performance increase of 10% from a baseline.

[0084] • Calculating absolute or relative difference between current performance and target performance to quantify additional repetitions required to bridge performance gap.

[0085] • Considering the user factors such as training history, exercise technique proficiency, and recovery capacity to tailor repetition count recommendations for optimal performance progression.

[0086] Furthermore, adjustment of the optimal repetition count recommendations for subsequent training sessions includes, but is not limited to the following:• Gradually increasing number of repetitions performed per set or per workout session or the training session based on the user progression rates, recovery status, and adherence to previous training protocols;

[0087] • Periodically reassessing performance metrics and adjusting repetition count goals to maintain progression towards the 10% performance improvement target while avoiding overtraining or injury risk.

[0088] Moreover, determining the optimal repetition count for achieving a 10% increase in the muscle performance during resistance training includes, but is not limited to the following:

[0089] • Performance tracking modules for collecting and analyzing the baseline performance data, including maximum repetition (RM) tests, muscle activation measurements, and fatigue thresholds, from the users engaged in resistance training.

[0090] • Calculation techniques programmed to compute the additional repetitions needed to reach a 10% increase in muscle performance based on baseline metrics and established performance targets.

[0091] • User interfaces providing personalized repetition count recommendations and performance tracking tools to guide the user through progressive resistance training programs and monitor performance gains over time.

[0092] FIG. 2 is a block diagram of an exemplary embodiment of system for muscle stimulation during a training session in accordance with an embodiment of the present disclosure. Further, the processing subsystem (150) includes an analysis module (210), a data processing unit (220), a memory unit (230), a tracking module (250), an exercise protocol module (260), and a muscle age calculation module (265).

[0093] The analysis module (210) is operatively coupled to the receiving module (190). The analysis module (210) is configured to analyze blood flow rate of the target muscle using a machine learning model. The machine learning model is configured to classify the target muscle based on a muscle fatigue level and predict one or more optimalparameters for the training session. Typically, adjustment of the one or more optimal parameters includes, but is not limited to the following:

[0094] • Adjustment of exercise intensity based on real-time muscle fatigue indicators to prevent overexertion or underutilization of muscle groups.

[0095] • Modifying exercise or workout or training session duration and rest intervals to optimize muscle recovery and adaptation during training sessions.

[0096] • Providing audio, visual, or haptic feedback cues to guide the users in adjusting exercise parameters according to real-time performance feedback.

[0097] The analysis module (210) is also configured to generate personalized workout recommendations tailored to optimize therapeutic outcomes and user satisfaction in response to a medical practitioner to analyze the sensor data, medical history, the plurality of physiological parameters, and treatment objectives. For example, consider the user recovering from hand surgery. The analysis module (210) first collect data from the plurality of sensors (140) monitoring the user's muscle strength, joint mobility, and pain levels during exercises. Additionally, the analysis module (210) analyze the user’s medical history, such as previous injuries and any underlying conditions. Based on a comprehensive analysis, the analysis module generates customized workout plans that specify exercises suited for the user’s recovery stage, such as low- impact activities to strengthen the hand without putting excessive strain on the hand. Further, the analysis module (210) detects that the user’s range of motion is improving, the analysis module (210) recommends gradually increasing intensity or complexity of the exercises or the training session, ensuring align with treatment objectives set by a medical practitioner.

[0098] Further, the analysis module (210) is configured to record and analyze electromyography signals, muscle activation patterns, and fatigue indicators associated with the muscle contractions.

[0099] The data processing unit (220) is operatively coupled to the analysis module (210). The data processing unit (220) is configured to analyze real-time data. The data processingunit (220) is also configured to generate exercise parameter adjustments based on user preferences. For example, the exercise parameter adjustments includes, but is not limited to duration of the workout session, rest intervals between the workout session, and exercise variation.

[0100] The memory unit (230) operatively coupled to the data processing unit (220). The memory unit (230) is configured to store predefined protocols of the training session, user profile and past training session data. For example, consider that the user is recovering from a knee injury may have a predefined protocol that includes easy mobility exercises followed by strength training at specific intervals. The memory unit (230) also keeps track of the user profile, which encompasses the user information such as age, fitness level, and medical history. For example, the user preferring low impact exercises due to joint issues may have this preference stored for tailored workout recommendations. Additionally, the memory unit (230) archives past training session data, allowing for analysis of the user’s progress over time.

[0101] The tracking module (250) is operatively coupled to the analysis module (210). The tracking module (250) is configured to track calorie intake and expenditure based on individual metabolic parameters and weight loss goals.

[0102] A method for implementing a calorie deficit mechanism for weight management includes, but is not limited to the following:

[0103] • Collecting data on a calorie intake from food consumption records, nutritional labels, or meal tracking applications.

[0104] • Monitoring the calorie expenditure through physical activity tracking, metabolic rate estimation, or wearable sensors measuring energy expenditure.

[0105] • Calculating a personalized calorie deficit target by subtracting a predetermined deficit value from estimated daily energy expenditure, adjusted for individual metabolic factors and weight loss goals.• Providing feedback, recommendations, and behavioral interventions to help the user to achieve and maintain prescribed calorie deficit for effective weight management.

[0106] Further, the calorie intake is collected from the following:

[0107] • Logging food and beverage consumption using mobile applications, dietary journals, or barcode scanning tools to capture detailed nutritional information and portion sizes.

[0108] • Utilizing meal planning platforms or recipe databases to estimate calorie content and macronutrient composition of consumed foods based on ingredient lists and serving sizes

[0109] Further, monitoring expenditure of the calorie includes tracking physical activity levels using wearable fitness devices, pedometers, or mobile application equipped with accelerometers, GPS (global positioning system) tracking, or heart rate monitoring capabilities and estimating basal metabolic rate (BMR) and total daily energy expenditure (TDEE) using predictive equations, metabolic rate monitors, or indirect calorimetry measurements to account for resting metabolic needs and activity -related energy expenditure.

[0110] Furthermore, personalized calorie deficit target is calculated by subtracting a predetermined deficit value, such as 500 to 1000 calories per day, from estimated total daily energy expenditure to create the calorie deficit conducive to gradual and sustainable weight loss. Further, adjusting deficit target based on individual factors, including age, gender, weight, height, body composition, metabolic rate, physical activity level, and weight loss goals, to ensure safety, efficacy, and adherence to the prescribed calorie deficit.

[0111] The tracking module (250) is configured to provide feedback and guidance to the user to achieve and maintain a calorie deficit. The feedback, recommendations, and behavioral interventions includes, but is not limited to the following:• Generating personalized meal plans, dietary guidelines, and portion control strategies to help the user to achieve the calorie deficit while meeting nutritional needs and preferences.

[0112] • Offering real-time feedback on the calorie intake and expenditure through mobile application, wearable devices, or web-based dashboards to promote awareness and accountability in calorie tracking and goal attainment.

[0113] • Implementing behavioral change techniques, such as goal setting, selfmonitoring, stimulus control, and social support, to facilitate adherence to the prescribed calorie deficit and promote sustainable lifestyle modifications for long-term weight management.

[0114] Further, implementing the calorie deficit for a weight management includes, but is not limited to the following:

[0115] • Data logging modules for capturing and aggregating data on calorie intake, physical activity levels, metabolic rate, and weight changes from various sources, including mobile apps, wearable devices, and connected scales.

[0116] • Calculation engines programmed to estimate total daily energy expenditure (TDEE), basal metabolic rate (BMR), and personalized calorie deficit targets based on individual user profiles, goals, and metabolic parameters.

[0117] • User interfaces providing interactive tools, visualizations, and feedback mechanisms to help the user to track progress, monitor adherence to the prescribed calorie deficit, and receive personalized recommendations for behavior modification and goal attainment.

[0118] The exercise protocol module (260) is operatively coupled to the tracking module (250). The exercise protocol module (260) is configured to activate deep muscle groups, trigger endorphin release, and optimize neurobiological response to the training session performed by the user. Further, a method for inducing and managing endorphin release through deep muscle contraction and application integration includes, but is not limited to the following:• Designing exercise protocols focusing on deep muscle contractions and high- intensity movements to stimulate endorphin release through physical exertion and neuromuscular activation, incorporating sensory feedback mechanisms, such as vibration, tactile cues, or audiovisual stimuli, to enhance proprioception and muscle engagement during exercises. Further, the deep muscle contractions not only facilitate muscle engagement and recovery but also induce the release of growth hormones, similar to the release of endorphins during physical exertion.

[0119] • Integrating data logging capabilities within a mobile application (app) to track exercise performance, physiological responses, and endorphin release markers in real time.

[0120] • Providing personalized coaching, guidance, and motivational cues through the app interface to optimize endorphin-inducing workouts and enhance user engagement and adherence.

[0121] The exercise protocols includes, but is not limited to the following:

[0122] • High-intensity interval training (HUT) routines incorporating dynamic movements, compound exercises, and isometric contractions to recruit deep muscle fibers and stimulate endorphin release through physiological stress and adaptation responses.

[0123] Resistance training exercises target major muscle groups with heavy loads or resistance bands to elicit deep muscle contractions and mechanical tension, promoting endorphin release and muscle growth.

[0124] The muscle age calculation module (265) is configured to measure muscle health and functionality, reflecting how well the muscles perform relative to an individual’s biological age. Further, the muscle age calculation module (265) assesses various physiological factors, providing a snapshot of muscle strength, endurance, recovery ability, and metabolic efficiency compared to typical values for different age ranges. Key factors in calculating muscle age includes, but is not limited to the following:• Muscle mass and composition: The amount and quality of muscle tissue, including ratios of muscle to fat.

[0125] • Strength and power output: How much force a muscle can generate and at what speed.

[0126] • Recovery rate: Speed of recovery after exercise, which typically slows with age.

[0127] • Fatigue resistance: The muscle’s ability to sustain activity over time without tiring.

[0128] • Metabolic efficiency: How well muscles utilize oxygen and fuel, which affects endurance.

[0129] • Cellular health markers: Indicators like mitochondrial function and muscle cell regeneration, which decline with age.

[0130] • Using these parameters, the muscle age could be calculated to give individuals an age-equivalent for their muscle health. This measure could motivate people to improve their fitness levels by “reversing” their muscle age and guide targeted interventions for muscle performance and longevity.

[0131] In an example, consider a scenario where user X utilizes the compression pump (120), regulates blood flow to the user X's leg muscles, by applying controlled pressure during the training session or workout. As the training session progresses, the electrical stimulation unit (130) activates, transmitting electrical impulses to the user X leg muscles, triggering contractions and mimicking natural movements, helping to enhance muscle engagement. Throughout the training session, the plurality of sensors (140) embedded in the wearable suit continuously capture real-time physiological data, such as muscle activation signals from the plurality of electromyography electrodes, heart rate variability, and range of motion. The receiving module (190) then collects the physiological data to monitor intensity of the user X leg muscle stimulation, blood flow regulation, and the tension applied to the muscles, and the physiological data is displayed on a user device. Further, the analysis module (210) processes the data received from the plurality of sensors (140) and uses a machine learning model to classify muscle fatigue levels. It then predicts optimal adjustments, such as modifyingexercise intensity and recommending rest periods to prevent overexertion. The data processing unit (220) analyzes the user X's real-time data, adjusting exercise parameters like resistance or repetition count based on X's preferences and physical condition. As the user X completes each set of the training session, the calculation module (200) automatically calculates the optimal repetition count to achieve performance improvements, based on fatigue levels and training history stored in the memory unit (230). After the training session, the data from the plurality of sensors (140) is synced with the user device through the communication interface module (240), allowing for detailed tracking of progress and offering insights to further optimize future workouts.

[0132] In another use case scenario, the system (100) may be utilized in a footwear, wherein the system (100) includes electrical muscle stimulation (EMS) and an acupressure to simultaneously enhance blood flow, provide pain relief, and facilitate muscle recovery. Typically, the EMS is adapted to deliver targeted EMS signals to stimulate muscles while applying dynamic acupressure for improved circulation, reduced swelling, and faster recovery. Further, the EMS mimics natural muscle contractions, enabling passive exercise for the user with limited mobility or as an additional training tool for athletes. The system (100) enables massage, recovery, and exercise stimulation in a single wearable unit, allowing the user to address several health and wellness goals. Further, the system (100) includes programmable or automatic settings to adjust EMS intensity and acupressure levels based on the user needs or specific recovery requirements. Furthermore, the footwear is suitable for use in several scenarios, including postworkout recovery, pain management, and circulatory health improvement during extended periods of inactivity. Moreover, the footwear incorporates metal EMS contact points and customizable light emitting diode (LED) lighting features, enhancing aesthetic appeal while improving functional feedback during use. Additionally, the footwear is a lightweight, portable design with a modern aesthetic that integrates wellness techniques seamlessly into everyday footwear. The footwear is designed with non-invasive EMS electrodes and safe water pressure systems, ensuring user safety and comfort during prolonged use. The footwear features an easy-to-use control system foractivating the EMS and acupressure functions, with visual feedback via built-in LED indicators. Combination of the EMS and the acupressure provides significant improvements in microcirculation, enhancing oxygen delivery and nutrient transport to muscles. The footwear combines mechanical acupressure with electrical stimulation to alleviate pain, reduce muscle tension, and promote relaxation through endorphin release. The footwear uniquely integrates the EMS and the acupressure techniques in a synchronized manner, delivering dual benefits for recovery and wellness in a single device. Further, the system (100) includes smart feedback systems configured to monitor user activity, adjust EMS intensity, and regulate the acupressure dynamically for optimized performance.

[0133] FIG. 3(a) and 3(b) is a schematic representation of front view and rear view of a compression pump of FIG. 1, in accordance with an embodiment of the present disclosure. The compression pump (120) includes a size label (300), connectors (310), the plurality of electromyography electrodes conduction (320), compression zone (330), elbow mark front (340), no jetlag bottom design (350), stud back bonding (360), BFC band holding stud back (370), stud back bonding (380) and elastic binding (390).

[0134] The size label (300) indicates size of the compression pump (120), ensuring that the user selects correct size for optimal fit and performance.

[0135] The connectors (310) link different parts of the compression pump (120) to a main processing unit or external devices. The connectors (310) allow data and power to flow between the plurality of sensors (140, FIG. 2), the electrical stimulation unit (130), and the compression pump, ensuring real-time control and adjustments.

[0136] Further, the plurality of electromyography electrodes (320) is responsible for conducting electrical impulses from the electrical stimulation unit (130). The electrical stimulation unit (130) helps the muscles to contract and relax, while the plurality of electromyography electrodes monitors the muscle activity, measuring the electrical signals produced during the muscle contraction.In one embodiment, the electrical stimulation unit (130) includes a screen.

[0137] The compression zone (330) is an area where the compression pump (120) applies controlled pressure to the target muscles.

[0138] The elbow mark front (340) is a marker indicating alignment of the elbow and the compression pump (120). The elbow mark ensures that the compression pump (120) is worn correctly, so the compression zone and the plurality of sensors (140, FIG 2) are properly positioned for effective functioning.

[0139] The no jetlag bottom design (350) enhances comfort and well-being of the user during air travel. For example, as the user wears the compression pump (120) while flying, the compression pump (120) monitors the plurality of physiological parameters, such as heart rate and motion, and prompts the user to perform simple, guided exercises. Further, the guided exercises, in conjunction with the band’s stimulation, improve blood circulation and promote release of the endorphins, thereby combatting typical symptoms of a jet lag, such as dizziness, fatigue, and sluggishness, leaving the user feeling refreshed and energized upon landing.

[0140] The stud back bonding (360) is a type of attachment to the compression pump (120) to ensure that the compression pump (120) is security attached to the user.

[0141] The stud back bonding (360) is a bonding mechanism that ensures a compression band is securely attached to the target muscle or the body, contributing to consistent contact between the plurality of electromyography electrodes and the skin.

[0142] The elastic binding (390) is positioned on edges of the compression band that provides a snug fit to the user.

[0143] FIG. 4 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure. The server (160) includes processor(s) (430), and memory (410) operatively coupled to the bus (420). The processor(s) (430), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor,a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.

[0144] The memory (410) includes several subsystems stored in the form of executable program which instructs the processor (430) to perform the method steps illustrated in FIG. 1. The memory (410) includes a processing subsystem (150) of FIG.l. The processing subsystem (150) includes a plurality of modules. The plurality of modules includes a user interface module (180), a receiving module (190) and a calculation module (200).

[0145] The user interface module (180) configured to allow the user to monitor intensity of the electrical stimulation unit (130), blood flow regulation pressure, and tension of the plurality of elastic bands. The processing subsystem (150) also includes a receiving module (190) operatively coupled to the user interface module (180) wherein the receiving module (190) is configured to receive the plurality of physiological parameters from the plurality of sensors (140). Further, the processing subsystem (150) also includes a calculation module (200) operatively coupled to the receiving module (190) wherein the calculation module (200) is configured to calculate an optimal repetition count required to achieve a predetermined percentage in the target muscle performance based on a plurality of parameters, wherein the plurality of parameters includes a target muscle activation level, fatigue thresholds, and a training history.

[0146] The bus (420) as used herein refers to be internal memory channels or computer network that is used to connect computer components and transfer data between them. The bus (420) includes a serial bus or a parallel bus, wherein the serial bus transmits data in bit-serial format and the parallel bus transmits data across multiple wires. The bus (420) as used herein, may include but not limited to, a system bus, an internal bus, an external bus, an expansion bus, a frontside bus, a backside bus and the like.FIG. 5 illustrates a flow chart representing the steps involved in a method for muscle stimulation during a training session in accordance with an embodiment of the present disclosure.

[0147] The method (500) includes regulating, by a compression pump, a blood flow to a target muscle by applying controlled pressure with predetermined movements in step 520.

[0148] Further, the method (500) includes transmitting, by an electrical stimulation unit, a plurality of electrical impulses causing the target muscle to contract and relax thereby mimicking a natural muscle movement in step 540.

[0149] The method (500) includes capturing, by a plurality of sensors of the wearable suit, a plurality of physiological parameters associated with the target muscle during the muscle contractions wherein the plurality of sensors includes a plurality of electromyography electrodes, heart rate monitors, motion sensors and inertial measurement units in step 550. In one embodiment, the plurality of physiological parameters includes an electromyography signals, heart rate variability metrics, blood lactate levels and range of motion measurements.

[0150] In one embodiment, the method (500) includes to transmitting the data received from the plurality of sensors to external devices or an internal devices or a fitness platform enabling synchronization of the data, thereby enabling flexibility in data processing for detailed analysis of muscle performance and training metrics either directly on the system or remotely on the external devices, enhancing accessibility and adaptability for the user, and the calculation of metrics for each individual training session is a highly unique aspect. By analyzing each session independently, the system can provide precise feedback on the muscle engagement, fatigue, and performance specific to the training session. This session-by-session analysis allows the user to track progress in the real time, tailor future sessions based on immediate results, and gain more granular insights into their performance and recovery needs. Both features contribute to a highly customized, data-driven training experience that stands out from conventional fitness or rehabilitation systems.The method (500) includes allowing, by a user interface module, the user to monitor intensity of the electrical stimulation unit, blood flow regulation pressure, and tension of the band in step 560.

[0151] In one embodiment, the method (500) includes enabling the user to track performance and recovery of the target muscle in real-time wherein the performance is based on Electromyography signals indicating muscle activation and fatigue, heart rate variability, range of motion measurements assessing muscle flexibility, joint mobility and blood lactate levels.

[0152] In another embodiment, the method (500) includes allowing the user to interact with a virtual coaching service for guidance on improving muscle development through blood flow regulation.

[0153] In yet another embodiment, the method (500) includes providing an audio output to assist the user during the training session and adjust a plurality of parameters during engagement of the target muscles.

[0154] The method (500) includes receiving, by a receiving module, the plurality of physiological parameters from the plurality of sensors in step 570.

[0155] In one embodiment, the method (500) includes analyzing blood flow rate of the target muscle using a machine learning model, wherein the machine learning model is configured to classify the target muscle based on a muscle fatigue level and predict one or more optimal parameters for the training session.

[0156] In another embodiment, the method (500) includes analyzing real-time data and generating exercise parameter adjustments based on user preferences.

[0157] In yet another embodiment, the method (500) includes storing predefined protocols of the training session, user profile and past training session data.

[0158] Further, the method (500) includes predicting a workout intensity and recovery needs for the training session. Furthermore, the method (500) includes generatingpersonalized workout recommendations that are tailored to the user’s requirement using a machine learning model. Personalized workout recommendations are used to optimize therapeutic outcomes and user satisfaction in response to a medical practitioner to analyze the sensor data, medical history, the plurality of physiological parameters, and treatment objectives.

[0159] Furthermore, the method (500) includes recording and analyzing electromyography signals, muscle activation patterns, and fatigue indicators associated with muscle contraction.

[0160] Moreover, the method (500) includes tracking calorie intake and expenditure based on individual metabolic parameters and weight loss goals. Feedback and guidance are provided to the user to achieve and maintain a calorie deficit.

[0161] The method (500) includes calculating, by a calculation module, an optimal repetition count required to achieve a predetermined percentage in the target muscle performance based on a plurality of parameters, wherein the plurality of parameters includes a target muscle activation level, fatigue thresholds, and a training history in step 580.

[0162] V arious embodiments of a system for muscle stimulation during a training session as described above offer various benefits for muscle stimulation during training session performed by the user. Further, the system provides improved blood flow regulation through the compression pump (120), which applies regulated pressure to the user’s target muscles, enhancing recovery and training effectiveness. The electrical stimulation unit (130) delivers targeted impulses that mimic natural muscle contractions, optimizing muscle engagement. Real-time monitoring is enabled by the plurality of sensors (140), thereby capturing valuable physiological data. The user is allowed to customize the training session through the user interface module (180), adjusting stimulation intensity, pressure, and band tension to suit their needs. Additionally, the calculation module (200) determines the optimal repetition count based on muscle activation, fatigue thresholds, and training history, ensuring efficient and safe workouts.The techniques described in this disclosure may be implemented, at least in part, in hardware, software, firmware, or any combination thereof. For example, various aspects of the described techniques may be implemented within one or more processors, including one or more microprocessors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or any other equivalent integrated or discrete logic circuitry, as well as any combinations of such components. The term “processor” or “processing subsystem” may generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry, or any other equivalent circuitry. An electronic control unit including hardware may also perform one or more of the techniques of this disclosure.

[0163] Such hardware, software, and firmware may be implemented within the same device or within separate devices to support the various techniques described in this disclosure. In addition, any of the described units, modules, or components may be implemented together or separately as discrete but interoperable logic devices. Depiction of different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be realized by separate hardware, firmware, or software components. Rather, functionality associated with one or more modules or units may be performed by separate hardware, firmware, or software components, or integrated within common or separate hardware, firmware, or software components.

[0164] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the disclosure and are not intended to be restrictive thereof.

[0165] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.

Claims

1. I CLAIM:

1. A system (100) for muscle stimulation during a training session comprising:characterized in that,a compression pump (120) embedded in a plurality of elastic bands wherein the compression pump (120) is adapted to regulate a blood flow to a target muscle by applying controlled pressure with predetermined movements,wherein the compression pump (120) comprises:an electrical stimulation unit (130) configured to transmit a plurality of electrical impulses causing the target muscle to contract and relax thereby mimicking a natural muscle movement; anda plurality of sensors (140) configured to capture a plurality of physiological parameters associated with the target muscle during muscle contractions wherein the plurality of sensors (140) comprises a plurality of electromyography electrodes, heart rate monitors, motion sensors and inertial measurement units;a processing subsystem (150) hosted on a server (160) wherein the processing subsystem (150) is configured to execute on a network (170) to control bidirectional communications among a plurality of modules comprising:a user interface module (180) configured to allow a user to monitor intensity of the electrical stimulation unit (130), blood flow regulation pressure, and tension of the plurality of elastic bands;a receiving module (190) operatively coupled to the user interface module (180) wherein the receiving module (190) is configured to receive the plurality of physiological parameters from the plurality of sensors (140); and a calculation module (200) operatively coupled to the receiving module (190) wherein the calculation module (200) is configured to calculate anoptimal repetition count required to achieve a predetermined percentage in the target muscle performance based on a plurality of parameters, wherein the plurality of parameters comprises a target muscle activation level, fatigue thresholds, and a training history.

2. The system ( 100) as claimed in claim 1 , wherein the user interface module ( 180) is configured to:enable the user to track performance and recovery of the target muscle in real-time wherein the performance is based on Electromyography signals indicating muscle activation and fatigue, heart rate variability, range of motion measurements assessing muscle flexibility, joint mobility and blood lactate levels;allow the user to interact with a virtual coaching service for guidance on improving muscle development through blood flow regulation;display personalized workout recommendations that are tailored to the user’s requirement; andprovide an audio output to assist the user during a training session and adjust the plurality of parameters during engagement of the target muscles.

3. The system (100) as claimed in claim 1, wherein the processing subsystem (150) comprises:an analysis module (210) operatively coupled to the receiving module (190) wherein the analysis module (210) is configured to analyze blood flow rate of the target muscle using a machine learning model, wherein the machine learning model is configured to classify the target muscle based on a muscle fatigue level and predict one or more optimal parameters for the training session;a data processing unit (220) operatively coupled to the analysis module (210) wherein the data processing unit (220) is configured to:analyze real-time data; andgenerate exercise parameter adjustments based on user preferences; anda memory unit (230) operatively coupled to the data processing unit (220) wherein the memory unit (230) is configured to store predefined protocols of the training session, user profile and past training session data.

4. The system (100) as claimed in claim 3, wherein the analysis module (210) is configured to generate personalized workout recommendations tailored to optimize therapeutic outcomes and user satisfaction in response to a medical practitioner to analyze the sensor data, medical history, the plurality of physiological parameters, and treatment objectives.

5. The system (100) as claimed in claim 3, wherein the analysis module (210) is configured to record and analyze electromyography signals, muscle activation patterns, and fatigue indicators associated with the muscle contraction, power generation by a muscle, and balance calculation from right to left or left to right of the user’s body to ensure balanced muscle growth.

6. The system (100) as claimed in claim 1, wherein the plurality of physiological parameters comprises an electromyography signal, heart rate variability metrics, blood lactate levels and range of motion measurements.

7. The system (100) as claimed in claim 1, comprising a communication interface module (240) operatively coupled to the plurality of sensors (140) wherein the communication interface module (240) is configured to transmit the data received from the plurality of sensors (140) to external devices or internal devices or a fitness platform enabling synchronization of the data.

8. The system (100) as claimed in claim 1, comprising a muscle age calculation module (265) operatively coupled to the receiving module (190) wherein the muscle age calculation module (265) is configured to measure muscle health and functionality.

9. The system (100) as claimed in claim 1, wherein the processing subsystem (150) comprises:a tracking module (250) operatively coupled to the analysis module (210) wherein the tracking module (250) is configured to:track calorie intake and expenditure based on individual metabolic parameters and weight loss goals; andprovide feedback and guidance to the user to achieve and maintain a calorie deficit; andan exercise protocol module (260) operatively coupled to the tracking module (250) wherein the exercise protocol module (260) is configured to activate deep muscle groups, trigger endorphin release, and optimize neurobiological response to the training session performed by the user.

10. A method (500) for muscle stimulation during a training session comprising:characterized in that,regulating, by a compression pump, a blood flow to a target muscle by applying controlled pressure with predetermined movements; (520)transmitting, by an electrical stimulation unit, a plurality of electrical impulses causing the target muscle to contract and relax thereby mimicking a natural muscle movement; (540)capturing, by a plurality of sensors, a plurality of physiological parameters associated with the target muscle during the muscle contractions wherein the plurality of sensors comprises a plurality of electromyography electrodes, heart rate monitors, motion sensors and inertial measurement units; (550)allowing, by a user interface module of a processing subsystem, the user to monitor intensity of the electrical stimulation unit, blood flow regulation pressure, and tension of the plurality of elastic bands; (560)receiving, by a receiving module of the processing subsystem, the plurality of physiological parameters from the plurality of sensors; and (570)calculating, by a calculation module of the processing subsystem, an optimal repetition count required to achieve a predetermined percentage in the target muscle performance based on a plurality of parameters, wherein the plurality of parameters comprises a target muscle activation level, fatigue thresholds, and a training history.

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