SYSTEM AND METHOD FOR AI-DRIVEN FITNESS TRAINING

Abstract

The present disclosure pertains to a system (100) and a method (400) for fitness training. The system (100) includes an input unit (102) configured to receive a first set of parameters of an entity, one or more acquisition units (104) configured to acquire one or more postures of the entity during a training session, and a processing unit (106) in communication with the input unit (102), where the processing unit (106) analyzes the data, applies machine learning techniques to generate personalized training plans, and provides real-time feedback to optimize exercise performance. The system (100) dynamically adjusts training plans based on entity progress and performance metrics, ensuring continued improvement

Specification

Description:TECHNICAL FIELD
[0001] The present disclosure relates to the field of fitness training. More particularly, it pertains to a system and method for fitness training using artificial intelligence to enhance user experience and training outcomes.

BACKGROUND
[0002] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed disclosure, or that any publication specifically or implicitly referenced is prior art.
[0003] Gym instructors are essential in helping individuals achieve fitness goals and maintain a healthy lifestyle. Their role involves providing guidance, tailored workout plans, motivation, goal setting, goal monitoring, safety, injury prevention, and flexibility. They offer expertise, encouragement, and personalized support, enhancing the likelihood of individuals achieving and maintaining fitness targets safely and efficiently.
[0004] However, there are potential drawbacks to the client-trainer relationship, such as high costs, potential overreliance, trainers' personalities and training methods not aligning with clients' preferences, limited availability due to gym schedules or operational hours, lack of clear communication, and reliance on generic workout templates. These issues can lead to misunderstandings, ineffective workout sessions, and neglect of specific client goals or considerations. Despite these challenges, gym trainers continue to be a valuable resource for individuals seeking to improve their fitness and overall well-being.
[0005] Patent document WO2024015768A1 discloses a system for tracking and logging an athletic workout of a user, the system including at least one camera and at least one computing device running a tracking application. The system provides the user with tailored dialogue, logging, analysis and feedback based on the video, photo or audio information the camera receives from viewing the user and exercise equipment. The computing device includes a module for generating information via various artificial modalities by processing input data. The system may process visual inputs from the user, area, wearable or fitness-related item including their movement, equipment and utilizes machine learning models to analyze and extract relevant information.
[0006] Patent document IN201911036566A discloses a fitness application called A.I. Personal Trainer that enhances effectiveness and safety of exercises by helping users correct their form in real time. This employs advanced pose estimation to analyze the user's exercise posture, evaluating key body points to offer personalized feedback on improving performance. The system collects a dataset of key point coordinates and timestamps, which are labeled as "Good" or "Bad" based on personal training guidelines, enabling the development of machine learning algorithms for exercise evaluation. In addition, the A.I. Personal Trainer supports four common exercises and operates on Windows with GPU compatibility. Through 3D human detection, it can monitor multiple individuals simultaneously via a camera and provide real-time feedback using voice assistance. This setup allows the system to replicate the presence of a live trainer. Additionally, it includes facial and eye scanning for user verification, replacing traditional login methods. It can assess the user's mood-whether happy, frustrated, or sad-based on facial expressions, offering a more personalized training experience.
[0007] Patent document IN202341043912A discloses a system that utilizes a deep learning model using MediaPipe and BlazePose for real-time pose estimation and analysis of exercise movements, providing corrective feedback on posture. This automated system improves the safety and effectiveness of home workouts by guiding users on proper form, enhancing accessibility, and lowering the costs associated with personal training. Evaluated on an exercise video dataset, the model has demonstrated satisfactory performance, offering an innovative approach to at-home fitness training.
[0008] Though the cited references disclose various systems for personalized fitness. However, these existing systems do not address certain specific needs in personalized fitness that could further optimize training outcomes and user experience.
[0009] Therefore, there is a need to provide a solution for an improved personalized fitness training solution with real-time feedback and adaptive workout plans.

OBJECTS OF THE PRESENT DISCLOSURE
[0010] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0011] A general object of the present disclosure is to provide a system and a method for fitness training, generating feedback on exercise form and technique, improving performance and reducing risk of injuries in the users.
[0012] An object of the present disclosure is to provide a system that offers personalized workout plans tailored to individual fitness goals and preferences, ensuring effective training.
[0013] Another object of the present disclosure is to provide a system that can adapt workout plans in response to user progress and feedback, ensuring that users are continuously challenged and motivated.
[0014] Yet another object of the present disclosure is to provide a system that makes fitness training more accessible and effective for a wide range of users.

SUMMARY
[0015] Various aspects of the present disclosure relate to fitness training. More particularly, it pertains to a system and method for fitness training that utilizes real-time feedback to enhance exercise form, reduce injury risk, and improve performance. Also, offers personalized, adaptable workout plans aligned with individual goals, making effective training accessible to a broad range of users.
[0016] An aspect of the present disclosure pertains to a system for fitness training. The system includes an input unit configured to receive first set of parameters of an entity, one or more acquisition units configured to acquire one or more postures of the entity during a training session and a processing unit in communication with the input unit and configured to receive acquired first set of parameters from the input unit and a second set of parameters from one or more wearables associated with the entity, extract information from the first set of parameters and the second set of parameters, apply machine learning techniques on the extracted information and correspondingly determine a personalized training plan for the entity. Further, the processing unit generates feedback on activities being performed by the entity during the training session, taking into consideration the acquired one or more postures, and the feedback is transmitted to an audio unit that emits auditory prompts to enable the entity to adjust the one or more postures accordingly and dynamically adjust the personalized training plan of the entity based on performance metrics and progress during the training session.
[0017] In an aspect, the first set of parameters is selected from a group consisting of biometric data, height, weight, age, gender, fitness level, fitness goal, and pre-existing health conditions.
[0018] In an aspect, the second set of parameters is selected from a group consisting of heart rate, step count, caloric burn, and activity duration.
[0019] In an aspect, the extracted information encompasses identification of correlations between the first and second sets of parameters, thereby enabling an assessment of the physical condition of the entity and suitability for the one or more activities being performed during the training session.
[0020] In an aspect, the performance metrics are selected from a group consisting of measurements of intensity and volume of the activities and adherence to the determined training plan, and the training plan is continuously monitored by the acquisitions units and analyzed by the processing unit during the training session.
[0021] In an aspect, the processing unit is further configured to analyze the identified correlations to detect patterns including strengths and weaknesses in performance of the entity, upon application of the machine learning techniques on the extracted information. In addition, utilizes predictive analytics to predict outcomes based on historical data and the performance metrics of the entity, dynamically adjust the training plan of the entity and generate the feedback pertaining to at least one activity recommendation.
[0022] Another aspect of the present disclosure pertains to, a method for fitness training. The method includes the steps of receiving, by a processing unit, a first set of parameters acquired from an input unit and a second set of parameters from one or more wearables associated with an entity during a training session. The method further includes the steps of extracting information from the first set of parameters and the second set of parameters. Further, applying one or more machine learning techniques to the extracted information and correspondingly determining a personalized training plan for the entity. The method further includes the steps of, generating feedback on one or more activities being performed by the entity during the training session, taking into consideration the acquired one or more postures, where the feedback is transmitted to an audio unit that emits auditory prompts to enable the entity to adjust the one or more postures accordingly. Further, dynamically adjust the personalized training plan of the entity based on performance metrics and progress during the training session.
[0023] Various objects, features, aspects, and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF DRAWINGS
[0024] The accompanying drawings are included to provide a further understanding of the present invention and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present invention and, together with the description, serve to explain the principles of the present invention.
[0025] FIG. 1 illustrates an exemplary network architecture of the proposed system for fitness training, in accordance with an embodiment of the present disclosure.
[0026] FIG. 2 illustrates an exemplary block diagram of a processing unit associated with the system, in accordance with an embodiment of the present disclosure.
[0027] FIGs. 3A and 3B illustrate exemplary views of a user performing exercise, in accordance with an embodiment of the present disclosure.
[0028] FIG. 4 illustrates an exemplary flow diagram representing a method for fitness training to the user, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION
[0029] The following is a detailed description of embodiments of the system for fitness training depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the system for fitness training. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments, on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of present disclosure as defined by the appended claims.
[0030] Embodiments explained herein relate to fitness training. More particularly, it pertains to a system and method for fitness training using artificial intelligence, for fitness training using artificial intelligence, to enhance user experience and training outcomes.
[0031] An embodiment of the present disclosure pertains to a system for fitness training. The system includes an input unit configured to receive a first set of parameters of an entity, one or more acquisition units configured to acquire one or more postures of the entity during a training session and a processing unit in communication with the input unit including one or more processors, where the one or more processors are operatively coupled with a memory, the memory storing instruction executable by one or more processors. The processing unit is configured to receive the acquired first set of parameters from the input unit and a second set of parameters from one or more wearables associated with the entity, extract information from the first set of parameters and the second set of parameters, apply one or more machine learning techniques on the extracted information and correspondingly determine a personalized training plan for the entity, generate feedback on one or more activities being performed by the entity during the training session, taking into consideration the acquired one or more postures, and the feedback is transmitted to an audio unit that emits auditory prompts to enable the entity to adjust the one or more postures accordingly and dynamically adjust the personalized training plan of the entity based on performance metrics and progress during the training session.
[0032] In an embodiment, the first set of parameters is selected from a group consisting of biometric data, height, weight, age, gender, fitness level, fitness goal, and pre-existing health conditions.
[0033] In an embodiment, the second set of parameters is selected from a group consisting of heart rate, step count, caloric burn, and activity duration.
[0034] In an embodiment, the extracted information encompasses identification of correlations between the first and second sets of parameters, thereby enabling an assessment of the physical condition of the entity and suitability for the one or more activities being performed during the training session.
[0035] In an embodiment, the performance metrics are selected from a group consisting of measurements of intensity and volume of the e activities and adherence to the determined training plan, and the training plan is continuously monitored by the acquisitions units and analyzed by the processing unit during the training session.
[0036] In an embodiment, upon application of the machine learning techniques on the extracted information, the processing unit is further configured to analyze the identified correlations to detect patterns including strengths and weaknesses in performance of the entity, utilize predictive analytics to predict outcomes based on historical data and the performance metrics of the entity, utilize predictive analytics to predict outcomes based on historical data and the performance metrics of the entity, dynamically adjust the training plan of the entity and generate the feedback pertaining to at least one activity recommendation.
[0037] In an embodiment, a method for fitness training includes the steps of receiving, by a processing unit, acquired a first set of parameters from an input unit and a second set of parameters from one or more wearables associated with an entity during a training session. Further, extracting, information from the first set of parameters and the second set of parameters. Further, applying one or more machine learning techniques to the extracted information and correspondingly determining a personalized training plan for the entity. Further, generating, feedback on one or more activities being performed by the entity during the training session, taking into consideration the acquired one or more postures, where the feedback is transmitted to an audio unit that emits auditory prompts to enable the entity to adjust the one or more postures accordingly. Further, dynamically adjusting, the personalized training plan of the entity based on performance metrics and progress during the training session.
[0038] Referring to FIG. 1, a system (100) for fitness training is disclosed. The system (100) includes an input unit (102) configured to receive the first set of parameters of an entity (interchangeably referred to as user, herein). This input unit (102) can be integrated with or attached to at least one exercise equipment in a gym, or other designated training areas. For instance, multiple input units can be installed across different equipment in the gym to gather first set of parameters from various sources, providing comprehensive data on the user's activity and performance. The first set of parameters is selected from a group consisting of biometric data, height, weight, age, gender, fitness level, fitness goal, and pre-existing health conditions. The first set of parameters assists in generating a customized workout plan for efficient exercise routines to improve fitness levels of the entities.
[0039] The input unit (102) can include a user interface that allows for interaction with the system (100), and it can be any device such as, but not limited to, a display attached to the exercise equipment, a smartphone, laptop, tablet, or desktop computer. The input unit (102) collects information of the entity, to create a training plan to meet the entity's specific needs and capabilities. The versatility of the input unit (102) ensures that users can access and utilize the system (100) from a wide range of devices, accommodating various user preferences and technological environments.
[0040] In an embodiment, the system (100) includes one or more acquisition units (104) configured to acquire one or more postures of the entity during a training session. The acquisition units (104) can be wearable devices such as smartwatches, fitness trackers or the like to monitor heart rate, steps, calories burned, and other relevant metrics. The acquisition units (104) can also incorporate cameras or other sensors that can track posture of the entity, movement, and performance metrics during the training session.
[0041] In an embodiment, the system (100) includes a processing unit (106) in communication with the input unit (102) and the acquisition units (104) through a communication unit (110). The communication unit (110) may be wired communication means, or wireless communication means, or a combination thereof.
[0042] In some embodiments, the wired communication means may include, but not limited to, wires, cables, data buses, optical fibre cables, and the like. In some embodiments, the wireless communication means may include, but not be limited to, telecommunication, Near Field Communication (NFC), Bluetooth, Internet, Local Area Networks (LAN), Wide Area Networks (WAN), Light Fidelity (Li-FI) networks, a carrier network, and the like. In some embodiments, format of the data transmitted through the communication means may be any one or combination of including, but not limited to, analogue signals, electrical signals, digital signals, radio signals, infrared signals, data packets, and the like.
[0043] The processing unit (106) processes the collected data including the first set of parameters and the second set of parameters, and applies machine learning techniques to analyze performance of the entity and identify areas for improvement. In addition, the processing unit (106), generates personalized training plans based on entity data and goals, and consequently generates real-time feedback on exercise form and technique through an audio unit (108) (e.g., speakers or headphones) and displays feedback on the display (102). The audio unit (108) can be positioned in proximity to the equipment. Further, the processing unit (106) dynamically adjusts training plans based on entity progress and performance metrics.
[0044] Referring to FIG. 2, an exemplary block diagram of the processing unit (106) associated with the system (100) is disclosed. The processing unit (106) includes one or more processor(s) (202) that may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the one or more processor(s) (202) may be configured to fetch and execute computer-readable instructions stored in a memory (204) of the processing unit (106). The memory (204) may store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory (204) may include any non-transitory storage device including, for example, volatile memory such as Random Access Memory (RAM), or non-volatile memory such as an Erasable Programmable Read-Only Memory (EPROM), flash memory, and the like.
[0045] In an embodiment, the processing unit (106) may also include an interface(s) (206). The interface(s) (206) may include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) (206) may provide a communication pathway for one or more components of the processing unit (106). Examples of such components include, but are not limited to, a processing engine (s) (208) and a database (210).
[0046] In an embodiment, the processing engine (208) is implemented as a combination of hardware and programming to implement one or more functionalities of the processing engine (208). Such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine (208) is processor-executable instructions stored on a non-transitory machine-readable storage medium, and the hardware for the processing engine (208) comprises a processing resource (for example, one or more processors (202)), to execute such instructions.
[0047] In an embodiment, the processing engine (208) can include a data acquisition module (212), a data extraction module (214), a machine learning analysis module (216), a feedback generation module (218), a dynamic training plan adjustment module (220), a predictive analytics and recommendation module (222) and other module(s) (224). The other module(s) (224) implements functionalities that supplement applications or functions performed by the system (100) or the processing engine (208). The database (210) serves, amongst other things, as a repository for storing data processed, received, and generated by one or more of the modules.
[0048] In an embodiment, the data acquisition module (212) is configured to receive the acquired first set of parameters from the input unit (102), and the second set of parameters from the wearables. The first set of parameters includes biometric data, height, weight, age, gender, fitness level, fitness goal, and pre-existing health conditions. In addition, the second set of parameters is selected from a group consisting of heart rate, step count, caloric burn, and activity duration. The second set of parameters can be used for immediate adjustments to the intensity, volume, or exercises of the workout plan to guarantee ongoing challenge and enhancement.
[0049] In an embodiment, the data extraction module (214) is configured to extract information from the first set of parameters and the second set of parameters. The extracted information may include key data points or attributes necessary for processing the training sessions, allowing for optimized performance and accurate results based on both parameters. In addition, the extracted information encompasses identification of correlations between the first and second sets of parameters, thereby enabling an assessment of the physical condition of the entity and suitability for the one or more activities being performed during the training session. The supports real-time adjustments, enhances training outcomes, and ensures that the selected activities align with the current physical capabilities and progress of the entity.
[0050] In an embodiment, the machine learning analysis module (216) is configured to apply one or more machine learning techniques to the extracted information to develop a personalized fitness training plan. Initially, the machine learning analysis module (216) processes the information, such as biometric information and performance metrics, by extracting relevant features and identifying patterns that inform personalized training needs. This information may include classifications of exercises best suited for the user's goals, allowing the model to categorize workout types, such as strength or cardio, based on the user's fitness level. Techniques like decision trees and support vector machines help with classification, while convolutional neural networks may analyze any visual data related to user movement or posture.
[0051] In addition, the machine learning analysis module (216) also performs predictive modeling, forecasting outcomes based on historical data-like anticipated improvements in strength or endurance-using regression algorithms or time-series analysis. This forecasting helps optimize workout routines by selecting exercise sequences, intensities, and durations tailored to maximize progress. Techniques like genetic algorithms or clustering are used to group exercises for efficiency and impact, ensuring workouts are as effective as possible. Additionally, the system adjusts the training plan dynamically using continuous performance feedback. For example, if a user demonstrates faster progress than expected, the system might increase the workout's difficulty or intensity to maintain optimal challenge levels. Reinforcement learning and adaptive learning techniques enable this real-time adjustment, allowing the model to refine the plan based on ongoing performance data, ultimately creating a responsive, personalized fitness experience that aligns with the user's goals and capabilities.
[0052] Further, upon application of machine learning techniques to the extracted information, the machine learning analysis module (216) analyzes the identified correlations to detect patterns including strengths and weaknesses of the entity. The patterns help to understand the strengths and weaknesses of the entity during the training session and enhance training approach by focusing on areas of improvement. Further, the machine learning analysis module (216) also utilizes predictive analytics to predict outcomes based on historical data and real-time performance metrics of the entity. This allows the system to anticipate future performance trends and continuously adapt training plans. Dynamic adjustments based on these predictions help optimize results, ensuring that the workout plan remains challenging and effective as the user progresses. Additionally, the machine learning analysis module (216) generates feedback customized to specific activity recommendations, which is communicated to the user to improve their performance in real-time. This feedback is essential for refining exercise techniques, enhancing the effectiveness of each workout session, and fostering long-term improvement.
[0053] In an exemplary embodiment, the machine learning techniques used by the system (100) allow the system (100) to automatically learn and improve from experience without being explicitly programmed. These techniques rely on statistical techniques to identify patterns and relationships within collected data, enabling the system (100) to make predictions or decisions based on this information. During fitness training, the system (100) utilizes these learning techniques or algorithms to analyze data collected about the user, such as biometric parameters, exercise performance, and progress metrics. The algorithms are specifically trained to recognize patterns and features in the data that correspond to the user's fitness profile and training needs. Once trained, these machine learning algorithms can process incoming data, identifying relevant patterns that help tailor feedback and workout adjustments. This continual learning process ensures that feedback remains accurate and adaptive, allowing the system to respond dynamically to changes in the user's performance and adjust training recommendations accordingly.
[0054] In an embodiment, the feedback generation module (218) is configured to generate feedback on one or more activities being performed by the entity during the training session, taking into consideration the acquired one or more postures. The feedback is transmitted to the audio unit (108) that emits auditory prompts, guiding the user to make adjustments to their postures. The audio cues may include details on positioning, movement speed, and stability, enhancing the user to make immediate corrections and optimize the effectiveness of each activity or exercise. By providing real-time feedback, the system enables a highly interactive training experience, allowing the user to focus on areas needing improvement and refine their technique as they perform the exercises. In addition to audio feedback, visual feedback is also displayed on a screen (i.e. input unit (104)), providing an additional layer of guidance to enhance the user's training experience. This combination of auditory and visual feedback supports an effective, user-centered approach to fitness training by promoting proper form, technique, and performance quality.
[0055] In an embodiment, the dynamic training plan adjustment module (220), is configured to dynamically adjust the personalized training plan of the entity based on performance metrics and progress throughout the training session. This dynamic training plan adjustment module (220) tracks the user's progress and incorporates ongoing feedback to adjust key aspects of the training plan. Specifically, it can modify exercise intensity and duration, as well as change the selection and sequence of exercises, introducing new activities as needed to challenge the user based on their current performance.
[0056] In addition, performance metrics are essential in these adjustments and are selected from measurements of exercise intensity, volume, and adherence to the training plan. These performance metrics, captured by acquisition units (104) and analyzed by the dynamic training plan adjustment module (220), provide real-time insight into the user's engagement with the plan and their performance. By continuously monitoring these metrics, the system ensures that the training plan remains appropriately challenging and aligned with the user's capabilities and fitness goals. This fosters an adaptable training experience that dynamically evolves to support the user's progress.
[0057] In an embodiment, the predictive analytics and recommendation module (222) may be configured to anticipate the user's future training needs and recommend activities that align with their fitness goals. This module uses predictive analytics to analyze historical data, performance metrics, and trends observed in the user's progress to make informed forecasts about the user's likely development path. By identifying these trends, the module can predict aspects such as potential strength gains, endurance improvements, or areas where the user may face challenges. The historical data refers to the collection of past information and metrics related to the user's fitness activities, progress, and performance over time. This data includes previous workout details such as exercise types, duration, intensity, repetitions, and any metrics tracked by wearable devices, like heart rate, calories burned, and steps. It can also encompass longer-term progress indicators, such as improvements in strength, endurance, flexibility, or body composition.
[0058] Additionally, based on these predictions, the predictive analytics and recommendation module (222) suggests specific exercises or modifications to the training plan. This might include recommending increased intensity, new exercise types, or adjustments in training frequency to optimize results. This approach enables a more tailored fitness experience by proactively guiding the user's training journey, ensuring that each exercise recommendation supports continued growth and aligns with the user's evolving fitness goals.
[0059] In an exemplary embodiment, the system (100) also includes rollers or nodes (not shown) strategically placed within benches, seats, or backrests of the exercise equipment to provide targeted messages to specific muscle groups. The user can select various massage modes, such as kneading, tapping, and rolling, from the input unit (102) to address different needs that can help to reduce muscle soreness and tension, improve blood circulation, enhance relaxation, reduce stress, and speed up recovery time. Additionally, the system (100) offers vibration therapy, which generates vibrations that stimulate muscle fibres and improve blood flow. The entity can adjust the intensity and frequency of the vibration to suit their preferences and needs. This vibration therapy assists to reduce muscle soreness, improve flexibility and range of motion, enhance athletic performance, and promote faster recovery from injuries.
[0060] Referring to FIGs. 3A and 3B, a new user begins their fitness session by using a weighing machine equipped with a start button, located nearby for easy access. This start button initiates user registration process. The screen (104) attached in proximity to the weighing machine allows the user to manually input their height and body measurements, such as waist and chest circumference, during the initial setup. The weighing machine tracks the user's weight and standing time, collecting essential baseline data. In addition, the acquisition unit (104) i.e. a 3D body scanner, monitors various health parameters and body posture. This scanner captures the user's body posture and analyzes their form during exercises. The system is configured to provide real-time feedback; as the user performs exercises, the audio unit (108) and the screen (104) deliver immediate corrective cues. For example, if the user's posture needs adjustment, the audio unit might provide spoken guidance, while the screen displays visual tips on improving form. Further, the screen (104) shows information such as workout duration, current body weight, measurements, and calories burned. A visual representation of the user may also appear, offering a clear, personalized view of their progress. This 3D body scanning technology enhances the training experience by giving the user precise, interactive feedback on their performance and ensuring accurate tracking of their physical changes over time.
[0061] Referring to FIG. 4, a method (400) for fitness training is disclosed. At block (402), the method (400) includes receiving, by a processing unit (106), acquiring a first set of parameters from the input unit (102) and a second set of parameters from one or more wearables associated with an entity during a training session. This ensures that there is a comprehensive dataset capturing both manually entered data and real-time biometric data from the wearables. This enables a more accurate assessment of the entity's condition and activity, allowing for more precise analysis and feedback tailored to the entity's performance throughout the training session. The first set of parameters can include information like age, weight, height, fitness goals, and any pre-existing health conditions. Additionally, the second set of parameters can be heart rate, step count, and calorie burn associated with an entity during a training session.
[0062] At block (404), the method (400) includes extracting by the processing unit (106), information from the first set of parameters and the second set of parameters. It processes the received data to extract relevant information. This may include analyzing the entity's fitness goals, identifying their current fitness level, and assessing their physical limitations.
[0063] At block (406), the method (400) includes applying the processing unit (106), one or more machine learning techniques to the extracted information and correspondingly determining a personalized training plan for the entity. The extracted information is processed by machine learning algorithms to generate a personalized training plan. These algorithms analyze the entity data and identify patterns to create a personalized workout routine.
[0064] At block (408), the method (400) includes generating, by the processing unit (106), feedback on one or more activities being performed by the entity during the training session, taking into consideration the acquired one or more postures, where the feedback is transmitted to an audio unit (108) that emits auditory prompts to enable the entity to adjust postures accordingly.
[0065] At block (410), the method (400) includes dynamically adjusting by the processing unit (106), the personalized training plan of the entity based on performance metrics and progress during the training session. It continuously monitors the entity progress and adjusts the training plan accordingly. This may include increasing or decreasing the intensity of the workout, modifying the exercise selection, or adjusting the duration of the session. The goal is to optimize the training plan to ensure that the entity is continually challenged and making progress.
[0066] Further, the performance metrics are selected from a group consisting of measurements of intensity and volume of the one or more activities, and adherence to the determined training plan, wherein the training plan being continuously monitored by the one or more acquisitions units and analyzed by the processing unit during the training session.
[0067] Thus, the present disclosure introduces the system and method for fitness training that utilizes advanced technologies to optimize workout routines. By analyzing entity data and performance metrics, the system generates tailored training plans and delivers real-time feedback, ensuring effective and efficient workouts.
[0068] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions, or examples, which are comprised to enable those having ordinary skills in the art to make and use the invention when combined with information and knowledge available to those having ordinary skills in the art.

ADVANTAGES OF THE PRESENT DISCLOSURE
[0069] The present disclosure provides a system and method that generates customized workout plans based on individual biometric data and preferences, ensuring that users are training effectively and efficiently.
[0070] The present disclosure provides a system and method with immediate feedback on exercise form and technique, helping users to improve their performance and reduce the risk of injuries.
[0071] The present disclosure provides a system and method that can adapt workout plans in response to user progress and feedback, ensuring that users are continuously challenged and motivated.
[0072] The present disclosure provides a system and method that can offer a more affordable and accessible option for fitness training compared to traditional personal trainers.
, Claims:1. A system (100) for fitness training, the system (100) comprising:
an input unit (102) configured to receive a first set of parameters of an entity;
one or more acquisition units (104) configured to acquire one or more postures of the entity, during a training session; and
a processing unit (106) in communication with the input unit (102), comprising one or more processors (202), wherein the one or more processors (202) operatively coupled with a memory (204), the memory (204) storing instruction executable by one or more processors (202) to:
receive the acquired first set of parameters from the input unit (102) and a second set of parameters from one or more wearables associated with the entity;
extract information from the first set of parameters and the second set of parameters;
apply one or more machine learning techniques on the extracted information and correspondingly determine a personalized training plan for the entity;
generate feedback on one or more activities being performed by the entity during the training session, taking into consideration the acquired one or more postures, wherein the feedback is transmitted to an audio unit (108) that emits auditory prompts to enable the entity to adjust the one or more postures accordingly; and
dynamically adjust the personalized training plan of the entity based on performance metrics and progress during the training session.
2. The system (100) as claimed in claim 1, wherein the first set of parameters is selected from a group consisting of biometric data, height, weight, age, gender, fitness level, fitness goal, and pre-existing health conditions.

3. The system (100) as claimed in claim 1, wherein the second set of parameters is selected from a group consisting of heart rate, step count, caloric burn, and activity duration.
4. The system (100) as claimed in claim 1, wherein the extracted information encompasses identification of correlations between the first and second sets of parameters, thereby enabling an assessment of physical condition of the entity and suitability for the one or more activities being performed during the training session.
5. The system (100) as claimed in claim 1, wherein the performance metrics are selected from a group consisting of measurements of intensity and volume of the one or more activities, and adherence to the determined training plan, wherein the training plan being continuously monitored by the one or more acquisitions units (104) and analyzed by the processing unit (106) during the training session.
6. The system (100) as claimed in claim 4, wherein upon application of the one or more machine learning techniques on the extracted information, the processing unit (106) is further configured to:
analyse the identified correlations to detect patterns comprising strengths and weaknesses in performance of the entity;
utilize predictive analytics to predict outcomes based on historical data and the performance metrics of the entity;
dynamically adjust the training plan of the entity; and
generate the feedback pertaining to at least one activity recommendation.
7. A method (400) for fitness training, comprising the steps of:
receiving (402), by a processing unit, acquired a first set of parameters from an input unit and a second set of parameters from one or more wearables associated with an entity during a training session;
extracting (404), by the processing unit, information from the first set of parameters and the second set of parameters;
applying (406), by the processing unit, one or more machine learning techniques on the extracted information and correspondingly determining a personalized training plan for the entity;
generating (408), by the processing unit, feedback on one or more activities being performed by the entity during the training session, taking into consideration the acquired one or more postures, wherein the feedback is transmitted to an audio unit that emits auditory prompts to enable the entity to adjust the one or more postures accordingly; and
dynamically adjusting (410), by the processing unit, the personalized training plan of the entity based on performance metrics and progress during the training session.
8. The method (400) as claimed in claim 7, wherein the first set of parameters is selected from a group consisting of biometric data, height, weight, age, gender, fitness level, fitness goal, and pre-existing health conditions.
9. The method (400) as claimed in claim 7, wherein the second set of parameters is selected from a group consisting of heart rate, step count, caloric burn, and activity duration.
10. The method (400) as claimed in claim 7, wherein the performance metrics are selected from a group consisting of measurements of intensity and volume of the one or more activities, and adherence to the determined training plan, wherein the training plan being continuously monitored by the one or more acquisitions units and analyzed by the processing unit during the training session.

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