YOLOvll-Powered Real-Time Multi-User Fitness Tracking with Voice Interaction

This study introduces a real-time multi-user fitness tracking system that integrates voice interaction to improve exercise monitoring and is driven by YOLOvll. Accurately tracking the physical activity of several users without wearing sensors while maintaining hands-free operation is the main problem addressed. YOLOvll is used by the system to accurately estimate poses, identify and track important body joints, evaluate arm motions, and categorize exercises into normal and combined lifting modes. For reliable multi-user tracking, it assigns unique IDs. It also uses a speech recognition module to interpret spoken commands for calorie data retrieval, workout mode control, and session termination. A text-to-speech engine also improves user engagement by offering real-time audio feedback. The results show that the system maintains real-time performance while accurately estimating the number of repetitions and calories burned. In addition to removing the requirement for wearable technology, this method improves accessibility and interaction, offering a practical and userfriendly way to track fitness in a variety of settings, including gyms, rehab facilities, and at-home workouts.