FitGym: An Online Smart GYM Platform

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Abstract

This project presents a Smart Online Fitness Platform, an intelligent web-based solution designed to transform traditional fitness training by integrating artificial intelligence, computer vision, and personalized health management into a single unified platform. The system aims to provide users with a comprehensive digital fitness experience by combining personalized workout planning, nutrition guidance, real-time exercise monitoring, and interactive virtual coaching. Through a user-friendly interface, individuals can access customized fitness programs based on their personal goals, fitness levels, and progress, enabling them to maintain a structured and effective workout routine from any location. The platform incorporates a secure authentication system that allows users, trainers, and administrators to access role-specific functionalities. Certified trainers can create and assign personalized workout schedules, monitor user performance, update exercise plans, and provide professional guidance remotely. A dynamic workout calendar and task management module help users organize their daily exercise routines, set fitness goals, receive reminders, and track completed activities, promoting consistency and long-term engagement. To improve user motivation and retention, the platform integrates a comprehensive gamification framework consisting of achievement badges, experience points, reward levels, progress milestones, and interactive leader boards. Users earn rewards by completing workout sessions, maintaining exercise streaks, and achieving fitness objectives, creating an engaging and competitive environment that encourages continuous participation. In addition, detailed progress analytics enable users to monitor improvements in workout completion, calorie expenditure, body measurements, and overall fitness performance through intuitive graphical dashboards. The platform also provides personalized dietary recommendations by generating nutrition plans that complement individual fitness goals, whether focused on weight loss, muscle gain, endurance improvement, or general wellness. These recommendations are continuously updated according to user progress and workout intensity, ensuring balanced nutritional support throughout the fitness journey. A conversational AI chatbot is integrated into the platform to provide instant assistance and guidance. The chatbot answers fitness-related questions, explains exercise techniques, offers workout recommendations, assists with navigation throughout the application, and provides immediate support whenever users require help. This intelligent assistant enhances accessibility and improves the overall user experience by delivering real-time responses without requiring human intervention. One of the major innovations of the platform is the integration of an AI-powered Transformer-based Yoga Pose Detection and Correction System. The proposed Residual Biomechanical Transformer Network (RBT-Net) combines YOLOv8m-Pose for accurate human pose estimation with Transformer-based temporal reasoning and a ResidualMLP backbone to analyze body movements across sequential frames. Instead of relying solely on individual images, the Transformer encoder captures temporal dependencies between consecutive poses, enabling the system to understand movement progression, pose stability, and posture transitions more effectively. The extracted biomechanical features, including normalized skeletal coordinates, joint angles, limb distances, and confidence scores, are processed through the hybrid architecture to accurately classify yoga poses while identifying posture deviations. Beyond pose recognition, the intelligent correction framework estimates posture severity, detects joint-level errors, and generates personalized corrective feedback based on the user’s current posture and skill level. This allows the platform to function as a virtual yoga instructor capable of providing real-time guidance that helps users improve posture accuracy, reduce injury risks, and perform yoga exercises more safely and effectively. The integration of Transformer-based temporal modeling significantly enhances robustness against body movement variations and improves recognition performance during dynamic yoga sessions compared to conventional frame-based approaches. Overall, the Smart Online Fitness Platform successfully combines personalized fitness management, intelligent workout planning, nutrition guidance, gamification, conversational AI, and advanced Transformer-based yoga posture analysis into a unified digital ecosystem. By integrating modern artificial intelligence techniques with interactive web technologies, the platform delivers a comprehensive, accessible, and engaging fitness solution that bridges the gap between conventional gym environments and next-generation intelligent virtual fitness systems.

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  1. This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/23915746.

    Does the introduction explain the objective of the research presented in the preprint? Yes
    Are the methods well-suited for this research? Somewhat appropriate The software architecture is stated, but more important datasets like the inclusion and exclusion criteria, training and validation of the AI-generated plans, and ground truth labelling procedure are missing or not very clearly stated. The yoga pose detection is the main differentiating factor stated in this program, but it does not differentiate whether a pose is biomechanically correct or not. The study can include expert annotation reference postures
    Are the conclusions supported by the data? Somewhat supported Claims on user engagement, retention, completion of the health goals, personalisation of the health program, overall effectiveness of the program and improved health need more empharical data
    Are the data presentations, including visualizations, well-suited to represent the data? Somewhat appropriate and clear The data presentations are partially appropriate in terms of architecture, functional modules and implementation. But it needs more in-depth visualisation of the AI performance results using statistical information
    How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research? Somewhat clearly
    Is the preprint likely to advance academic knowledge? Moderately likely The integration of AI into a health monitoring platform will add value to the user, but from an academic standpoint, it is more of an integration into a health platform that might not have any novel value. In future based on more detailed results from test users and datasets, this might give us an understanding of how user welcome such intiative and adapt to it to help in achieving their health goals
    Would it benefit from language editing? No
    Would you recommend this preprint to others? Yes, but it needs to be improved Stronger scientific methodology, technical validation, and evidence. Also, users' medical status, like comorbidities and body composition (apart from BMI), is not present
    Is it ready for attention from an editor, publisher or broader audience? Yes, after minor changes

    Competing interests

    The author declares that they have no competing interests.

    Use of Artificial Intelligence (AI)

    The author declares that they did not use generative AI to come up with new ideas for their review.