An Adaptive Deep Learning Framework for Automated Fitness Coaching with Motion Feedback

Abstract

The rapid development of the use of AI in healthcare and fitness has generated potential for the creation of intelligent & automated personal coaching solutions that enhance user engagement as well as performance. This study introduces an adaptable deep learning framework for automated fitness coaching with motion feedback, emphasizing the identification of exercise activities and the provision of tailored workout recommendations. Conventional fitness systems frequently rely on manual oversight, wearable devices, or comprehensive user-specific data, including body metrics and posture analysis, so constraining their accessibility & scalability. Certain existing systems utilize machine learning methodologies for activity recognition; nevertheless, they exhibit only modest efficacy and lack quick adaptability & individualized feedback. Moreover, numerous methodologies prioritize model comparison over enhancing user-centric recommendations & system usability. The proposed method employs a custom-built YOLOv11 convolution neural networks, to identify and categorize 36 distinct exercise postures from photographs given by users. The system automatically creates tailored workout plans and offers posture correction recommendations based on identified activity as well as fundamental user input, such as age. The framework is executed via a web-based interface featuring modules for user identification, model training as well as loading, visualisation of training results through accuracy as well as loss graphs, including real-time activity detection including recommendation capabilities. The suggested system enhances accuracy, adaptability, & practical usefulness by integrating deep learning along with an interactive learning platform, rendering it an excellent solution for individualized fitness instruction performance optimization. Keywords— Deep Learning, Personal Fitness Coaching, Motion Feedback, YOLOv11, Exercise Recognition, Convolutional Neural Networks (CNN), Activity Classification, Personalized Workout Recommendation, Human Pose Detection, Web-Based Application, Real-Time Monitoring, Performance Optimization