1. A system (100) for real-time athlete performance monitoring and recommendation generation, the system (100) comprising: a wearable sensor suite (102) comprising an inertial measurement unit (104), a surface electromyography sensor (106), a heart activity sensor (108), an oxygen saturation sensor (110), a skin temperature sensor (112), and a location sensor (114) configured to capture biometric data associated with an athlete; and an edge processing unit (116) communicatively connected to the wearable sensor suite (102), characterized in that the edge processing unit (116) is configured to: receive the biometric data associated with the athlete from the wearable sensor suite (102); synchronize timestamps of the biometric data received from the wearable sensor suite (102) to obtain temporally aligned sensor signals; preprocess the temporally aligned sensor signals through filtering, segmentation, and feature extraction to generate physiological feature parameters and biomechanical feature parameters; and execute a local inference model to evaluate outputs selected from fatigue level, biomechanical deviation, physiological stress, or a combination thereof based on the combined performance indicators.

2. The system (100) as claimed in claim 1, comprising a communication network (118) configured to transmit session data and evaluation outputs to a cloud processing unit (120).

3. The system (100) as claimed in claim 1, comprising a cloud processing unit (120) configured to update athlete performance analytics and recommendation parameters based on historical training data and learnings of the local inference model.

4. The system (100) as claimed in claim 1, wherein the edge processing unit (116) is configured to synchronize timestamps by compensating clock drift and sampling mismatch between the inertial measurement unit (104), the surface electromyography sensor (106), the heart activity sensor (108), the oxygen saturation sensor (110), the skin temperature sensor (112), and the location sensor (114).

5. The system (100) as claimed in claim 1, wherein the edge processing unit (116) is configured to preprocess sensor signals by performing artifact removal, bandpass filtering, and segmentation to derive time-domain features and frequency-domain features.

6. The system (100) as claimed in claim 1, wherein the edge processing unit (116) is configured to generate cross-sensor relationships between muscle activation timing obtained from the surface electromyography sensor (106) and motion parameters obtained from the inertial measurement unit (104).

7. The system (100) as claimed in claim 1, wherein the local inference model executed by the edge processing unit (116) evaluates a fatigue index, a movement asymmetry score, and a cardiovascular stress indicator.

8. The system (100) as claimed in claim 1, wherein the edge processing unit (116) is configured to generate training recommendations comprising notifications that instruct reduction of activity intensity, modification of exercise technique, introduction of a recovery interval, or a combination thereof.

9. The system (100) as claimed in claim 1, comprising a user interface (124) installed on a computing device (122) adapted to present synchronized sensor streams, performance indicators, generated recommendations, and maintain records comprising sensor origin information, preprocessing operations, and model version information associated with generated recommendations.

10. A method (300) for real-time athlete performance monitoring and recommendation generation, the method (300) is characterized by steps of comprising: receiving biometric data from a wearable sensor suite (102) comprising an inertial measurement unit (104), a surface electromyography sensor (106), a heart activity sensor (108), an oxygen saturation sensor (110), a skin temperature sensor (112), and a location sensor (114) associated with an athlete; synchronizing timestamps of the biometric data received from the wearable sensor suite (102) using an edge processing unit (116) to obtain temporally aligned sensor signals; pre-processing the temporally aligned sensor signals through filtering, segmentation, and feature extraction to generate physiological feature parameters and biomechanical feature parameters; performing multimodal feature fusion by correlating feature parameters obtained from different sensors to generate combined performance indicators; executing a local inference model within the edge processing unit (116) to evaluate fatigue level, biomechanical deviation, and physiological stress based on the combined performance indicators; generating training recommendations or alerts based on evaluation results produced by the local inference model; transmitting session data and evaluation outputs to a cloud processing unit (120) through a communication network (118); and updating athlete performance analytics and recommendation parameters within the cloud processing unit (120) based on historical training data and learnings of the local inference model. Date: April 08, 2026 Place: Noida Nainsi Rastogi Patent Agent (IN/PA-2372) Agent for the Applicant

Description:BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to an athletic performance analyzer and particularly to a system for real-time athlete performance monitoring and recommendation generation.
Description of Related Art
[002] Athletic training environments demand accurate assessment of physiological condition, biomechanical posture, and workload during practice sessions and competitive events. Athletes and coaches require reliable information regarding heart activity, muscular response, body movement, oxygen saturation, and environmental conditions to guide training decisions and prevent physical injury. Conventional observation methods depend heavily on manual evaluation, subjective judgement, and delayed review of recorded performance data. Such approaches often fail to provide immediate insight into physiological stress, fatigue state, or biomechanical deviation. As a result, athletes experience excessive workload, inefficient training adaptation, and elevated injury risk.
[003] Current technological approaches employ wearable fitness devices, motion trackers, heart rate monitors, global positioning modules, and bio signal measurement instruments to collect athlete performance data. These devices record physiological signals such as heart rate variability, muscle activation signals, location coordinates, and movement acceleration. Data collected from these devices typically travel to cloud-based analytics platforms or external software systems where statistical evaluation or machine learning models derive performance metrics. Coaches and athletes review analytical reports after completion of a training session or competition in order to evaluate performance trends and training outcomes.
[004] Despite availability of such technological tools, several technical limitations persist in current solutions. Many commercial devices operate with isolated sensor architecture that prevents coordinated interpretation of multiple physiological signals. Analytical processes often rely on remote cloud infrastructure that introduces communication delay and restricts immediate decision support during training sessions. Generic population-based evaluation models fail to represent individual athlete physiology with sufficient accuracy. Additionally, lack of synchronized data correlation across diverse biometric sources reduces reliability of performance interpretation and limits effective injury risk assessment. Consequently, existing systems provide incomplete situational awareness and restricted responsiveness in dynamic athletic environments.
[005] There is thus a need for an improved and advanced system for real-time athlete performance monitoring and recommendation generation that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[006] Embodiments in accordance with the present invention provide a system for real-time athlete performance monitoring and recommendation generation. The system comprising a wearable sensor suite comprising an inertial measurement unit, a surface electromyography sensor, a heart activity sensor, an oxygen saturation sensor, a skin temperature sensor, and a location sensor configured to capture biometric data associated with an athlete. The system further comprising an edge processing unit communicatively connected to the wearable sensor suite. The edge processing unit is configured to receive the biometric data associated with the athlete from the wearable sensor suite; synchronize timestamps of the biometric data received from the wearable sensor suite to obtain temporally aligned sensor signals; preprocess the temporally aligned sensor signals through filtering, segmentation, and feature extraction to generate physiological feature parameters and biomechanical feature parameters; and execute a local inference model to evaluate fatigue level, biomechanical deviation, and physiological stress based on the combined performance indicators.
[007] Embodiments in accordance with the present invention further provide a method for real-time athlete performance monitoring and recommendation generation. The method comprising steps of receiving biometric data from a wearable sensor suite comprising an inertial measurement unit, a surface electromyography sensor, a heart activity sensor, an oxygen saturation sensor, a skin temperature sensor, and a location sensor associated with an athlete; synchronizing timestamps of the biometric data received from the wearable sensor suite using an edge processing unit to obtain temporally aligned sensor signals; pre-processing the temporally aligned sensor signals through filtering, segmentation, and feature extraction to generate physiological feature parameters and biomechanical feature parameters; performing multimodal feature fusion by correlating feature parameters obtained from different sensors to generate combined performance indicators; executing a local inference model within the edge processing unit to evaluate fatigue level, biomechanical deviation, and physiological stress based on the combined performance indicators; generating training recommendations or alerts based on evaluation results produced by the local inference model; and transmitting session data and evaluation outputs to a cloud processing unit through a communication network; and updating athlete performance analytics and recommendation parameters within the cloud processing unit based on historical training data and learnings of the local inference model.
[008] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide a system for real-time athlete performance monitoring and recommendation generation.
[009] Next, embodiments of the present application may provide a system that allows immediate analytical interpretation of physiological and biomechanical parameters, thereby enabling athletes and coaches to obtain actionable performance insight during training sessions without reliance on delayed post-session analysis.
[0010] Next, embodiments of the present application may provide a system that features early identification of fatigue indicators, abnormal biomechanical patterns, and physiological stress signals, thereby enabling preventive intervention before occurrence of training-related injuries.
[0011] Next, embodiments of the present application may provide a system that provides coordinated interpretation of diverse biometric parameters obtained from multiple sensing modalities, thereby enabling a more holistic assessment of athlete condition and performance capability.
[0012] Next, embodiments of the present application may provide a system that features adaptive analytical outputs based on athlete-specific physiological characteristics and performance history, thereby enabling training guidance that aligns with individual capability and recovery capacity.
[0013] Next, embodiments of the present application may provide a system that provides structured analytical feedback and contextual performance indicators that assist coaching personnel in making informed training adjustments and strategy decisions.
[0014] These and other advantages will be apparent from the present application of the embodiments described herein.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] FIG. 1 illustrates a schematic diagram of a system for real-time athlete performance monitoring and recommendation generation, according to an embodiment of the present invention;
[0016] FIG. 2 illustrates a block diagram of an edge processing unit of the system for real-time athlete performance monitoring and recommendation generation, according to an embodiment of the present invention; and
[0017] FIG. 3 depicts a flowchart of a method for real-time athlete performance monitoring and recommendation generation, according to an embodiment of the present invention.
DETAILED DESCRIPTION
[0018] As used herein, the singular forms “a”, “an”, and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.
[0019] As used herein, the term “athlete” may refer to a person engaged in a physical activity, sport, exercise, or training session that involves bodily movement, physical exertion, or performance evaluation. In an embodiment of the present invention, the athlete may include a professional sportsperson, amateur participant, trainee, fitness enthusiast, rehabilitation patient, or any individual performing a monitored physical activity in which physiological parameters and biomechanical characteristics may be analyzed for performance assessment or health monitoring purposes.
[0020] FIG. 1 illustrates a schematic diagram of a system 100 for real-time athlete performance monitoring and recommendation generation, according to an embodiment of the present invention. In an embodiment of the present invention, the system 100 may be adapted to acquire biometric information associated with an athlete during an athletic activity and analyze the acquired biometric information to derive performance-related insights. In an embodiment of the present invention, the system 100 may be configured to process the biometric information through computational analysis techniques to generate indicators related to fatigue level, biomechanical behavior, and physiological stress experienced by the athlete.
[0021] In an embodiment of the present invention, the system 100 may be adapted to perform synchronization and analytical processing of the acquired biometric information in order to generate performance indicators representing physical condition and movement characteristics of the athlete. In an embodiment of the present invention, the system 100 may utilize analytical models to interpret the generated performance indicators and determine training conditions, fatigue patterns, and biomechanical deviations that may occur during athletic activity.
[0022] In an embodiment of the present invention, based on the analytical interpretation of the biometric information, the system 100 may be adapted to generate training recommendations or alerts associated with athletic activity. In an embodiment of the present invention, the system 100 may further maintain performance analytics derived from training sessions and may update analytical models based on historical information to improve accuracy of future recommendations. Accordingly, the system 100 may facilitate continuous monitoring, analytical interpretation, and adaptive recommendation generation for athlete performance enhancement in athletic training environments.
[0023] According to the embodiments of the present invention, the system 100 may incorporate non-limiting hardware components to enhance a processing speed and an efficiency such as the system 100 may comprise a wearable sensor suite 102, an edge processing unit 116, a communication network 118, a cloud processing unit 120, a computing device 122, and a user interface 124. In an embodiment of the present invention, the hardware components of the system 100 may be integrated with computer-executable instructions for overcoming the challenges and the limitations of the existing systems.
[0024] In an embodiment of the present invention, the wearable sensor suite 102 may be adapted to be worn on the body of the athlete during athletic activity. The wearable sensor suite 102 may be adapted to capture biometric data associated with the athlete. In an embodiment of the present invention, the wearable sensor suite 102 may be adapted to operate as an integrated wearable platform that may be physically coupled to the athlete in a non-intrusive manner. The wearable sensor suite 102 may be adapted to remain attached to the athlete during motion so that biometric information and movement-related characteristics of the athlete may be captured continuously during training sessions or sporting events.
[0025] In an embodiment of the present invention, the wearable sensor suite 102 may be adapted to be secured to the athlete through wearable accessories that may include straps, bands, clips, or flexible attachments. The wearable sensor suite 102 may be adapted to be positioned on different body regions of the athlete and may maintain physical contact with the body surface to facilitate stable acquisition of biometric information while the athlete performs dynamic movements.
[0026] In an embodiment of the present invention, the wearable sensor suite 102 may be adapted to be integrated within athletic apparel or smart garments worn by the athlete. The wearable sensor suite 102 may be embedded within textile structures or flexible materials of the apparel such that the sensing platform may remain distributed across body regions of the athlete while maintaining comfort and mobility during athletic activity. Integration of wearable monitoring technology within garments may enable continuous monitoring without restricting body movement.
[0027] In an embodiment of the present invention, the wearable sensor suite 102 may be adapted to be mounted within protective sports equipment worn by the athlete. The wearable sensor suite 102 may be incorporated into equipment such as helmets, headgear, protective pads, or footwear so that biometric information and motion characteristics may be captured during high-impact or high-intensity sports activities. Integration of wearable sensing platforms within protective equipment may enable monitoring of athletic conditions without introducing additional wearable burden.
[0028] In an embodiment of the present invention, the wearable sensor suite 102 may be adapted to be implemented as modular wearable units distributed across different body regions of the athlete. The modular wearable units may be adapted to be attached at predetermined anatomical positions of the athlete in order to capture motion and physiological characteristics associated with different parts of the body. The distributed wearable configuration may facilitate comprehensive monitoring of the athlete while maintaining flexibility in placement depending on the sport or training environment.
[0029] In an embodiment of the present invention, the wearable sensor suite 102 may be adapted to maintain continuous contact with the athlete and may be adapted to operate wirelessly so that biometric information may be generated during active movement without restricting athletic performance. The wearable sensor suite 102 may further be adapted to operate in outdoor or indoor sporting environments while remaining securely attached to the athlete during rapid motion, impact, or prolonged training activity. The wearable sensor suite 102 may be, but not limited to, body-mounted wearable platforms, smart garments, modular wearable sensing assemblies, integrated wearable monitoring systems, wearable sensing architectures, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the wearable sensor suite 102, including known, related art, and/or later developed technologies.
[0030] The wearable sensor suite 102 may comprise sensors such as, but not limited to, an inertial measurement unit 104, a surface electromyography sensor 106, a heart activity sensor 108, an oxygen saturation sensor 110, a skin temperature sensor 112, a location sensor 114, and so forth.
[0031] In an embodiment of the present invention, the inertial measurement unit 104 may be adapted to measure motion characteristics of the athlete during athletic activity. The inertial measurement unit 104 may be adapted to detect linear acceleration and angular motion associated with body movement of the athlete and may generate motion data representing orientation, acceleration, and rotational behavior of body segments during physical activity. The inertial measurement unit 104 may be adapted to continuously generate motion signals corresponding to dynamic body movements and transmit the motion signals as biometric data for further processing.
[0032] In an embodiment of the present invention, the inertial measurement unit 104 may be adapted to operate based on inertial sensing principles in which variations in acceleration and angular velocity associated with body movement of the athlete may be detected and converted into digital motion signals. The generated motion signals may represent kinematic characteristics such as stride pattern, motion trajectory, posture variation, and movement symmetry of the athlete during athletic activity. The motion signals generated by the inertial measurement unit 104 may thereafter be transmitted to the edge processing unit 116 for synchronization and analytical evaluation of athlete performance parameters.
[0033] The inertial measurement unit 104 may be, but not limited to, accelerometer-based motion sensors, gyroscope-based motion sensors, magnetometer-assisted orientation sensors, multi-axis inertial sensing modules, inertial sensing technologies, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the inertial measurement unit 104, including known, related art, and/or later developed technologies.
[0034] In an embodiment of the present invention, the surface electromyography sensor 106 may be adapted to detect electrical activity associated with muscular contraction of the athlete during athletic activity. The surface electromyography sensor 106 may be adapted to measure bioelectrical signals generated by muscle fibers when the muscles undergo contraction or relaxation. The detected electrical activity may be converted into electromyographic signals representing muscle activation patterns of the athlete.
[0035] In an embodiment of the present invention, the surface electromyography sensor 106 may be adapted to operate by establishing electrical contact with a skin surface of the athlete positioned above a target muscle region. The surface electromyography sensor 106 may detect variations in electrical potential generated by neuromuscular activation and may convert the detected electrical potential variations into digital signal data representing muscle activation timing and contraction intensity. The generated electromyographic signals may thereafter be transmitted as biometric data for further synchronization and analytical processing to evaluate neuromuscular behavior of the athlete during athletic activity.
[0036] The surface electromyography sensor 106 may be, but not limited to, surface bioelectrical sensing electrodes, dry electrode electromyography sensors, wet electrode electromyography sensors, textile-based electromyography sensors, wearable neuromuscular sensing modules, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the surface electromyography sensor 106, including known, related art, and/or later developed technologies.
[0037] In an embodiment of the present invention, the heart activity sensor 108 may be adapted to monitor cardiovascular activity of the athlete during athletic activity. The heart activity sensor 108 may be adapted to detect cardiac signals associated with heartbeat cycles of the athlete and generate physiological data representing heart rate and cardiac rhythm characteristics during physical exertion. The generated cardiac data may represent variations in cardiovascular activity corresponding to exercise intensity experienced by the athlete.
[0038] In an embodiment of the present invention, the heart activity sensor 108 may be adapted to operate by detecting variations in cardiac electrical activity or blood flow signals associated with heartbeat events. The detected signals may be converted into digital physiological signals representing heart rate patterns and heart rate variability of the athlete during athletic activity. The generated physiological signals may thereafter be transmitted as biometric data for synchronization and analytical evaluation in order to assess cardiovascular load and exertion conditions of the athlete.
[0039] The heart activity sensor 108 may be, but not limited to, photoplethysmography sensors, electrocardiography sensors, optical cardiac monitoring sensors, pulse detection sensors, wearable cardiovascular monitoring modules, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the heart activity sensor 108, including known, related art, and/or later developed technologies.
[0040] In an embodiment of the present invention, the oxygen saturation sensor 110 may be adapted to monitor oxygen saturation levels associated with the blood circulation of the athlete during athletic activity. The oxygen saturation sensor 110 may be adapted to detect variations in oxygenated and deoxygenated blood characteristics and generate physiological data representing oxygen saturation levels corresponding to respiratory and cardiovascular activity of the athlete.
[0041] In an embodiment of the present invention, the oxygen saturation sensor 110 may be adapted to operate by emitting light signals toward a skin surface of the athlete and detecting variations in light absorption associated with oxygenated and deoxygenated hemoglobin present in blood vessels beneath the skin surface. The detected variations in light absorption may be converted into digital physiological signals representing blood oxygen saturation levels of the athlete. The generated physiological signals may thereafter be transmitted as biometric data for synchronization and analytical evaluation in order to assess oxygen utilization efficiency and physiological exertion conditions during athletic activity.
[0042] The oxygen saturation sensor 110 may be, but not limited to, pulse oximetry sensors, optical blood oxygen monitoring sensors, reflectance-based oxygen saturation sensors, transmissive oxygen saturation sensors, wearable blood oxygen sensing modules, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the oxygen saturation sensor 110, including known, related art, and/or later developed technologies.
[0043] In an embodiment of the present invention, the skin temperature sensor 112 may be adapted to monitor thermal characteristics associated with the body of the athlete during athletic activity. The skin temperature sensor 112 may be adapted to detect variations in temperature at a skin surface of the athlete and generate physiological data representing thermal conditions corresponding to metabolic activity and physical exertion experienced by the athlete.
[0044] In an embodiment of the present invention, the skin temperature sensor 112 may be adapted to operate by detecting heat variations present at the skin surface and converting the detected thermal variations into digital temperature signals. The generated temperature signals may represent changes in body surface temperature associated with exercise intensity, environmental exposure, or physiological stress experienced by the athlete. The generated temperature signals may thereafter be transmitted as biometric data for synchronization and analytical evaluation in order to assess thermal strain and physiological response during athletic activity.
[0045] The skin temperature sensor 112 may be, but not limited to, thermistor-based temperature sensors, thermocouple temperature sensors, semiconductor temperature sensors, infrared temperature sensors, wearable thermal sensing modules, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the skin temperature sensor 112, including known, related art, and/or later developed technologies.
[0046] In an embodiment of the present invention, the location sensor 114 may be adapted to determine spatial position and movement trajectory associated with the athlete during athletic activity. The location sensor 114 may be adapted to detect geographic positioning signals corresponding to the physical location of the athlete and generate positional data representing movement path, displacement, and travel distance during training or sporting activity.
[0047] In an embodiment of the present invention, the location sensor 114 may be adapted to operate by receiving satellite-based positioning signals and computing geographic coordinates corresponding to the position of the athlete. The computed geographic coordinates may be converted into digital positional signals representing velocity, displacement, and movement trajectory of the athlete over time. The generated positional signals may thereafter be transmitted as biometric data for synchronization and analytical evaluation in order to correlate spatial movement patterns with physiological and biomechanical characteristics of the athlete during athletic activity.
[0048] The location sensor 114 may be, but not limited to, satellite positioning sensors, global navigation satellite system sensors, global positioning system sensors, indoor positioning sensors, motion-assisted localization modules, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the location sensor 114, including known, related art, and/or later developed technologies.
[0049] In an embodiment of the present invention, the edge processing unit 116 may be communicatively connected to the wearable sensor suite 102. In turn, the edge processing unit 116 may operatively be coupled to the inertial measurement unit 104, the surface electromyography sensor 106, the heart activity sensor 108, the oxygen saturation sensor 110, the skin temperature sensor 112, and to the location sensor 114. The operative communication may be, but not limited to, receiving, transmitting, processing, synchronizing, querying, updating, encrypting, decrypting, storing, retrieving, validating, logging, monitoring, alerting, authenticating, authorizing, compressing, decompressing, streaming, and rendering data or commands between the wearable sensor suite 102 and the edge processing unit 116.
[0050] The edge processing unit 116 may be, but not limited to, embedded processing devices, wearable processing modules, edge computing processors, mobile processing platforms, distributed edge analytics systems, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the edge processing unit 116, including known, related art, and/or later developed technologies. The edge processing unit 116 may further be explained in detail in conjunction with FIG. 2.
[0051] In an embodiment of the present invention, the communication network 118 may be configured to transmit session data and evaluation outputs to the cloud processing unit 120. In an embodiment of the present invention, the communication network 118 may be adapted to facilitate data communication between the edge processing unit 116 and the cloud processing unit 120. The communication network 118 may be adapted to transmit the session data and the evaluation outputs generated during athletic activity to the cloud processing unit 120 through a wireless or wired communication infrastructure. The session data may include synchronized biometric data streams, derived feature parameters, and generated performance indicators obtained during monitoring of the athlete.
[0052] In an embodiment of the present invention, the communication network 118 may be adapted to operate using network communication protocols that may enable reliable and secure transmission of data packets between the edge processing unit 116 and the cloud processing unit 120. The communication network 118 may be adapted to perform packet routing, data encapsulation, and transmission control so that the session data and the evaluation outputs generated by the edge processing unit 116 may be transferred for storage, advanced analytics, and model learning within the cloud processing unit 120.
[0053] In an embodiment of the present invention, the communication network 118 may be adapted to implement a store-and-forward communication mechanism for maintaining continuity of data transmission during intermittent network connectivity conditions. The communication network 118 may be adapted to temporarily store the session data and the evaluation outputs generated by the edge processing unit 116 within local storage buffers and subsequently transmit the stored data to the cloud processing unit 120 upon restoration of network connectivity. The store-and-forward mechanism may thereby enable reliable synchronization of athlete training data with the cloud processing unit 120 even during temporary communication disruptions.
[0054] In an embodiment of the present invention, the communication network 118 may further be adapted to support continuous or periodic transmission of data during athletic sessions so that the cloud processing unit 120 may update athlete performance analytics and recommendation parameters based on the received session data. The communication network 118 may thereby enable synchronization of the locally generated evaluation outputs with remote analytical systems for long-term performance assessment and adaptive recommendation generation.
[0055] The communication network 118 may be, but not limited to, wireless communication networks, wired communication networks, short-range communication networks, internet-based communication infrastructure, hybrid communication architectures, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the communication network 118, including known, related art, and/or later developed technologies.
[0056] In an embodiment of the present invention, the cloud processing unit 120 may be configured to update the athlete performance analytics and the recommendation parameters based on historical training data and learnings of a local inference model. In an embodiment of the present invention, the cloud processing unit 120 may be adapted to receive the session data and the evaluation outputs transmitted through the communication network 118 from the edge processing unit 116. The received session data may comprise synchronized biometric data streams, extracted feature parameters, and performance indicators generated during monitoring of the athlete. The cloud processing unit 120 may be adapted to store the received session data within a cloud-based data repository so that historical training information associated with the athlete may be accumulated over multiple training sessions.
[0057] In an embodiment of the present invention, the cloud processing unit 120 may be adapted to perform analytical processing on the accumulated historical training data in order to update the athlete performance analytics and the recommendation parameters. The cloud processing unit 120 may utilize machine learning models and analytical algorithms that may evaluate historical physiological patterns, biomechanical behavior, and the previously generated evaluation outputs to identify trends associated with fatigue progression, performance improvement, and injury risk indicators. The analytical models may thereby derive updated parameters representing individualized performance baselines of the athlete.
[0058] In an embodiment of the present invention, the cloud processing unit 120 may be adapted to implement an ensemble analytical architecture comprising multiple machine learning models and rule-based physiological evaluation logic. The cloud processing unit 120 may be adapted to combine outputs generated by supervised learning models with predefined physiological safety rules in order to generate recommendation parameters that satisfy both statistical prediction accuracy and physiological safety constraints. The rule-based evaluation logic may analyze thresholds associated with cardiovascular activity, muscular activation patterns, and biomechanical movement characteristics of the athlete to prevent generation of potentially unsafe training recommendations.
[0059] In an embodiment of the present invention, the cloud processing unit 120 may further be adapted to incorporate learnings obtained from the local inference model executed within the edge processing unit 116. The evaluation outputs generated during athletic sessions may be aggregated and utilized to refine analytical models within the cloud processing unit 120 through model retraining, parameter optimization, or adaptive learning procedures. The updated recommendation parameters generated by the cloud processing unit 120 may thereafter be utilized to improve accuracy of the subsequent training recommendations and the athlete performance analytics generated by the system 100.
[0060] In an embodiment of the present invention, the cloud processing unit 120 may be adapted to maintain provenance metadata associated with biometric data streams, preprocessing operations, and analytical model outputs generated during athlete performance monitoring. The provenance metadata may include timestamp information, sensor origin identifiers, preprocessing operation identifiers, and model version parameters associated with generation of the training recommendations. The cloud processing unit 120 may thereby maintain an auditable record of data lineage and analytical processing history for verification, compliance monitoring, and performance review.
[0061] In an embodiment of the present invention, the cloud processing unit 120 may be adapted to support federated learning mechanisms for improving analytical models while preserving privacy of athlete training data. The cloud processing unit 120 may be adapted to receive model parameter updates generated from the edge processing unit(s) 116 associated with different athletes or training environments and aggregate the model parameter updates to refine global analytical models without transferring raw biometric data from the respective edge processing unit(s) 116.
[0062] The cloud processing unit 120 may be, but not limited to, cloud computing servers, distributed cloud processing platforms, remote data processing infrastructures, virtualized computing environments, scalable cloud analytics systems, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the cloud processing unit 120, including known, related art, and/or later developed technologies.
[0063] In an embodiment of the present invention, the computing device 122 may be an electronic device adapted to be used by a coaching personnel of the athlete and/or the athlete(s) themselves. The computing device 122 may be installed with the user interface 124. The computing device 122 may be, but not limited to, mobile computing devices, portable computing devices, desktop computing devices, tablet computing devices, wearable computing platforms, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the computing device 122, including known, related art, and/or later developed technologies.
[0064] The user interface 124 may be adapted to present synchronized sensor streams, performance indicators, generated recommendations, and so forth. The user interface 124 may be adapted to present and maintain records comprising sensor origin information, preprocessing operations, and model version information associated with generated recommendations. The user interface 124 may be, but not limited to, graphical user interfaces, touch-based interfaces, application-based interfaces, web-based interfaces, interactive display interfaces, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the user interface 124, including known, related art, and/or later developed technologies.
[0065] In an embodiment of the present invention, the user interface 124 installed on the computing device 122 may be adapted to enable feedback input from the coaching personnel associated with the athlete. The coaching personnel may provide annotations, training adjustments, and evaluation feedback corresponding to generated training recommendations and observed athlete performance. The feedback information may be transmitted to the cloud processing unit 120 and incorporated into analytical model updates in order to refine future recommendation parameters.
[0066] In an embodiment of the present invention, the user interface 124 installed on the computing device 122 may be adapted to present synchronized time-series visualization of biometric data streams generated by the inertial measurement unit 104, the surface electromyography sensor 106, the heart activity sensor 108, the oxygen saturation sensor 110, the skin temperature sensor 112, and the location sensor 114. The synchronized visualization may enable playback of sensor signals with corresponding training events and generated recommendations for detailed performance analysis.
[0067] In an embodiment of the present invention, the wearable sensor suite 102 may be adapted to support sensor reliability monitoring and fault tolerance operations. The edge processing unit 116 may be adapted to evaluate signal integrity parameters associated with the inertial measurement unit 104, the surface electromyography sensor 106, the heart activity sensor 108, the oxygen saturation sensor 110, the skin temperature sensor 112, and the location sensor 114 in order to identify abnormal signal behavior or sensor malfunction. Upon detection of unreliable sensor signals, the edge processing unit 116 may be adapted to exclude the affected sensor signals from analytical processing and continue generation of performance indicators using available biometric data streams.
[0068] In an embodiment of the present invention, the wearable sensor suite 102 may be adapted to operate using energy-efficient sensing and communication mechanisms to extend operational duration during athletic training sessions. The wearable sensor suite 102 and the edge processing unit 116 may be adapted to implement duty-cycling operations in which sensing and data transmission activities are selectively activated based on athlete activity intervals in order to reduce power consumption.
[0069] In an exemplary embodiment of the present invention, the system 100 may be adapted to monitor performance characteristics of a footballer during a training session or competitive match. The wearable sensor suite 102 worn by the footballer may capture motion data, muscle activation signals, heart activity, oxygen saturation levels, skin temperature variations, and location coordinates while the footballer performs activities such as sprinting, passing, or directional movement. The edge processing unit 116 may analyze the biometric data to determine fatigue level, movement asymmetry, and cardiovascular stress of the footballer and may generate training recommendations such as reducing sprint intensity, correcting movement technique, or introducing recovery intervals in order to prevent excessive fatigue or potential injury.
[0070] In an exemplary embodiment of the present invention, the system 100 may be adapted to monitor physiological and biomechanical performance of a runner during endurance training or competitive running events. The wearable sensor suite 102 worn by the runner may capture stride motion parameters, muscle activation timing, heart rate patterns, oxygen saturation levels, skin temperature variations, and positional movement data while the runner performs continuous running activity. The edge processing unit 116 may analyze the biometric data to evaluate fatigue index, cardiovascular stress, and stride symmetry of the runner and may generate training recommendations such as adjusting running pace, modifying stride technique, or introducing rest intervals in order to maintain optimal performance and reduce injury risk.
[0071] FIG. 2 illustrates a block diagram of the edge processing unit 116 of the system 100 for real-time athlete performance monitoring and recommendation generation, according to an embodiment of the present invention. The edge processing unit 116 may comprise the computer-executable instructions in form of programming modules such as a data receiving module 200, a data timing module 202, a data processing module 204, a data execution module 206, and a data generation module 208.
[0072] In an embodiment of the present invention, the data receiving module 200 may be configured to receive the biometric data associated with the athlete from the wearable sensor suite 102. The data receiving module 200 may be configured to receive the biometric data associated with the athlete from the wearable sensor suite 102 through a communication interface established between the wearable sensor suite 102 and the edge processing unit 116. The data receiving module 200 may be configured to accept incoming digital signal streams representing physiological measurements and motion parameters generated during athletic activity and may perform operations such as packet reception, buffering, validation, and formatting of the received biometric data. The formatted biometric data may thereafter be relayed to the data timing module 202 for synchronization and further analytical processing within the edge processing unit 116. The data receiving module 200 may be configured to transmit the received biometric data to the data timing module 202.
[0073] In an embodiment of the present invention, the data timing module 202 may be activated upon receipt of the biometric data from the data receiving module 200. The data timing module 202 may be configured to synchronize timestamps of the biometric data received from the wearable sensor suite 102 to obtain temporally aligned sensor signals. The data timing module 202 may be configured to synchronize timestamps in order to obtain temporally aligned sensor signals. The data timing module 202 may be configured to analyze timestamp information associated with incoming biometric data streams and perform time-alignment operations by compensating variations in sampling intervals and clock offsets across different sensors. The data timing module 202 may thereby generate temporally aligned sensor signals that represent synchronized biometric measurements corresponding to the same time instances during athletic activity and transmit the temporally aligned sensor signals to the data processing module 204 for further preprocessing and feature extraction.
[0074] The data timing module 202 may be configured to synchronize timestamps by compensating clock drift and sampling mismatch between the inertial measurement unit 104, the surface electromyography sensor 106, the heart activity sensor 108, the oxygen saturation sensor 110, the skin temperature sensor 112, and the location sensor 114. the data timing module 202 may be configured to analyze timestamp metadata associated with incoming biometric data streams and perform temporal alignment operations such as interpolation, buffering, and time-offset correction so that biometric measurements generated by different sensors correspond to common time instances. The synchronized biometric data may thereby form temporally aligned sensor signals suitable for subsequent preprocessing and analytical processing within the edge processing unit 116. The data timing module 202 may be configured to relay the temporally aligned sensor signals to the data processing module 204.
[0075] In an embodiment of the present invention, the data processing module 204 may be activated upon receipt of the temporally aligned sensor signals from the data timing module 202. The data processing module 204 may be configured to preprocess the temporally aligned sensor signals through filtering, segmentation, and feature extraction to generate physiological feature parameters and biomechanical feature parameters. The data processing module 204 may be configured to apply signal conditioning operations including noise filtering and artifact removal to improve signal quality, followed by segmentation of the temporally aligned sensor signals into analysis windows corresponding to athletic activity intervals. The data processing module 204 may thereafter perform feature extraction on the segmented signals to derive physiological feature parameters representing physiological responses of the athlete and biomechanical feature parameters representing motion characteristics of the athlete. The generated feature parameters may be transmitted to the data execution module 206 for further evaluation.
[0076] The processing module 204 may be configured to preprocess the temporally aligned sensor signals by performing artifact removal, bandpass filtering, and segmentation to derive time-domain features and frequency-domain features. The processing module 204 may be configured to remove motion artifacts and noise components from the temporally aligned sensor signals and apply bandpass filtering to isolate signal frequencies relevant to physiological and biomechanical measurements. The processing module 204 may further segment the filtered temporally aligned sensor signals into analytical windows and compute statistical and spectral characteristics to obtain the time-domain features and the frequency-domain features for subsequent evaluation by the data execution module 206. The data processing module 204 may be configured to transmit the physiological feature parameters and the biomechanical feature parameters to the data execution module 206.
[0077] In an embodiment of the present invention, the data execution module 206 may be activated upon receipt of the physiological feature parameters and the biomechanical feature parameters from the data processing module 204. The data execution module 206 may be configured to execute the local inference model to evaluate outputs based on the combined performance indicators. The outputs may be, but not limited to, fatigue level, biomechanical deviation, physiological stress, and so forth. The data execution module 206 may be configured to apply the local inference model on the physiological feature parameters and the biomechanical feature parameters generated by the data processing module 204 in order to compute evaluation outputs representing physiological and biomechanical conditions of the athlete. The local inference model may process the combined performance indicators using model parameters stored within the edge processing unit 116 and generate evaluation outputs corresponding to performance states of the athlete. The evaluation outputs may thereafter be transmitted to the data generation module 208 for generation of training recommendations.
[0078] In an embodiment of the present invention, the data execution module 206 may be adapted to implement safety override evaluation logic associated with the local inference model executed within the edge processing unit 116. The safety override evaluation logic may analyze the evaluation outputs including the fatigue level, the biomechanical deviation, and the physiological stress in comparison with predefined safety thresholds. Upon detection of conditions exceeding the safety thresholds, the data execution module 206 may be adapted to override automatically generated recommendations and trigger safety alert notifications for immediate intervention.
[0079] The local inference model executed by the edge processing unit 116 may be configured to evaluate a fatigue index, a movement asymmetry score, and a cardiovascular stress indicator. In an embodiment of the present invention, the local inference model executed by the edge processing unit 116 may be configured to evaluate the fatigue index, the movement asymmetry score, and the cardiovascular stress indicator based on the physiological feature parameters and the biomechanical feature parameters derived from the temporally aligned sensor signals. The local inference model may process the combined performance indicators using trained model parameters to estimate fatigue conditions, detect imbalance in movement patterns, and assess cardiovascular exertion levels of the athlete during athletic activity. The evaluated fatigue index, the movement asymmetry score, and the cardiovascular stress indicator may thereby represent real-time performance assessment outputs used for subsequent generation of training recommendations.
[0080] In an embodiment of the present invention, the local inference model executed by the edge processing unit 116 may be adapted to compute an injury risk index representing probability of musculoskeletal injury associated with detected biomechanical deviations and physiological stress conditions. The injury risk index may be derived by analyzing correlations between muscular activation timing obtained from the surface electromyography sensor 106, motion parameters obtained from the inertial measurement unit 104, cardiovascular activity derived from the heart activity sensor 108, and physiological stress indicators derived from the oxygen saturation sensor 110 and the skin temperature sensor 112.
[0081] The local inference model may be configured to perform multimodal feature fusion by correlating feature parameters obtained from different sensors to generate combined performance indicators. In an embodiment of the present invention, the local inference model may be configured to perform multimodal feature fusion by correlating feature parameters obtained from different sensors to generate combined performance indicators. The local inference model may be configured to analyze relationships between physiological feature parameters and biomechanical feature parameters derived from multiple sensor signals and integrate the correlated feature parameters through computational fusion techniques to obtain the combined performance indicators representing overall athletic performance conditions of the athlete during athletic activity. The data execution module 206 may be configured to relay the evaluated outputs and the multimodal feature fusion to the data generation module 208.
[0082] In an embodiment of the present invention, the data generation module 208 may be activated upon receipt of the evaluated outputs and the multimodal feature fusion from the data execution module 206. The data generation module 208 may be configured to generate the training recommendations comprising notifications that instruct reduction of activity intensity, modification of exercise technique, introduction of a recovery interval, and so forth. The data generation module 208 may be configured to analyze the evaluated outputs received from the data execution module 206 and determine recommendation instructions corresponding to detected physiological and biomechanical conditions of the athlete. The data generation module 208 may thereby generate notification messages representing the training recommendations that may be transmitted to the computing device 122 for presentation through the user interface 124.
[0083] The data generation module 208 may further be configured to generate cross-sensor relationships between muscle activation timing obtained from the surface electromyography sensor 106 and motion parameters obtained from the inertial measurement unit 104. In an embodiment of the present invention, the data generation module 208 may be configured to generate cross-sensor relationships between muscle activation timing obtained from the surface electromyography sensor 106 and motion parameters obtained from the inertial measurement unit 104. The data generation module 208 may be configured to correlate electromyographic activation signals with corresponding motion characteristics derived from inertial measurements in order to determine relationships between neuromuscular activity and biomechanical movement patterns of the athlete during athletic activity. The generated cross-sensor relationships may thereby provide analytical indicators associated with coordination, movement efficiency, and biomechanical imbalance for subsequent performance evaluation.
[0084] FIG. 3 depicts a flowchart of a method 300 for the real-time athlete performance monitoring and recommendation generation, according to an embodiment of the present invention.
[0085] At step 302, the system 100 may receive the biometric data from the wearable sensor suite 102 comprising the inertial measurement unit 104, the surface electromyography sensor 106, the heart activity sensor 108, the oxygen saturation sensor 110, the skin temperature sensor 112, and the location sensor 114 associated with the athlete.
[0086] At step 304, the system 100 may synchronize the timestamps of the biometric data received from the wearable sensor suite 102 using the edge processing unit 116 to obtain the temporally aligned sensor signals.
[0087] At step 306, the system 100 may pre-process the temporally aligned sensor signals through the filtering, the segmentation, and the feature extraction to generate the physiological feature parameters and the biomechanical feature parameters.
[0088] At step 308, the system 100 may perform the multimodal feature fusion by correlating the feature parameters obtained from different sensors to generate the combined performance indicators.
[0089] At step 310, the system 100 may execute the local inference model within the edge processing unit 116 to evaluate the fatigue level, the biomechanical deviation, and the physiological stress based on the combined performance indicators.
[0090] At step 312, the system 100 may generate the training recommendations or alerts based on the evaluation results produced by the local inference model.
[0091] At step 314, the system 100 may transmit the session data and the evaluation outputs to the cloud processing unit 120 through the communication network 118.
[0092] At step 316, the system 100 may update the athlete performance analytics and the recommendation parameters within the cloud processing unit 120 based on historical training data and learnings of the local inference model. , Claims:CLAIMS
I/We Claim:
1. A system (100) for real-time athlete performance monitoring and recommendation generation, the system (100) comprising:
a wearable sensor suite (102) comprising an inertial measurement unit (104), a surface electromyography sensor (106), a heart activity sensor (108), an oxygen saturation sensor (110), a skin temperature sensor (112), and a location sensor (114) configured to capture biometric data associated with an athlete; and
an edge processing unit (116) communicatively connected to the wearable sensor suite (102), characterized in that the edge processing unit (116) is configured to:
receive the biometric data associated with the athlete from the wearable sensor suite (102);
synchronize timestamps of the biometric data received from the wearable sensor suite (102) to obtain temporally aligned sensor signals;
preprocess the temporally aligned sensor signals through filtering, segmentation, and feature extraction to generate physiological feature parameters and biomechanical feature parameters; and
execute a local inference model to evaluate outputs selected from fatigue level, biomechanical deviation, physiological stress, or a combination thereof based on the combined performance indicators.
2. The system (100) as claimed in claim 1, comprising a communication network (118) configured to transmit session data and evaluation outputs to a cloud processing unit (120).
3. The system (100) as claimed in claim 1, comprising a cloud processing unit (120) configured to update athlete performance analytics and recommendation parameters based on historical training data and learnings of the local inference model.
4. The system (100) as claimed in claim 1, wherein the edge processing unit (116) is configured to synchronize timestamps by compensating clock drift and sampling mismatch between the inertial measurement unit (104), the surface electromyography sensor (106), the heart activity sensor (108), the oxygen saturation sensor (110), the skin temperature sensor (112), and the location sensor (114).
5. The system (100) as claimed in claim 1, wherein the edge processing unit (116) is configured to preprocess sensor signals by performing artifact removal, bandpass filtering, and segmentation to derive time-domain features and frequency-domain features.
6. The system (100) as claimed in claim 1, wherein the edge processing unit (116) is configured to generate cross-sensor relationships between muscle activation timing obtained from the surface electromyography sensor (106) and motion parameters obtained from the inertial measurement unit (104).
7. The system (100) as claimed in claim 1, wherein the local inference model executed by the edge processing unit (116) evaluates a fatigue index, a movement asymmetry score, and a cardiovascular stress indicator.
8. The system (100) as claimed in claim 1, wherein the edge processing unit (116) is configured to generate training recommendations comprising notifications that instruct reduction of activity intensity, modification of exercise technique, introduction of a recovery interval, or a combination thereof.
9. The system (100) as claimed in claim 1, comprising a user interface (124) installed on a computing device (122) adapted to present synchronized sensor streams, performance indicators, generated recommendations, and maintain records comprising sensor origin information, preprocessing operations, and model version information associated with generated recommendations.
10. A method (300) for real-time athlete performance monitoring and recommendation generation, the method (300) is characterized by steps of comprising:
receiving biometric data from a wearable sensor suite (102) comprising an inertial measurement unit (104), a surface electromyography sensor (106), a heart activity sensor (108), an oxygen saturation sensor (110), a skin temperature sensor (112), and a location sensor (114) associated with an athlete;
synchronizing timestamps of the biometric data received from the wearable sensor suite (102) using an edge processing unit (116) to obtain temporally aligned sensor signals;
pre-processing the temporally aligned sensor signals through filtering, segmentation, and feature extraction to generate physiological feature parameters and biomechanical feature parameters;
performing multimodal feature fusion by correlating feature parameters obtained from different sensors to generate combined performance indicators;
executing a local inference model within the edge processing unit (116) to evaluate fatigue level, biomechanical deviation, and physiological stress based on the combined performance indicators;
generating training recommendations or alerts based on evaluation results produced by the local inference model;
transmitting session data and evaluation outputs to a cloud processing unit (120) through a communication network (118); and
updating athlete performance analytics and recommendation parameters within the cloud processing unit (120) based on historical training data and learnings of the local inference model.

Date: April 08, 2026
Place: Noida

Nainsi Rastogi
Patent Agent (IN/PA-2372)
Agent for the Applicant