AI-Enabled Real-Time Exercise Monitoring with MediaPipe and OpenCV
J. Vijaya, Ashutosh Singh, Khushdeep Singh, Amit Kumar
Abstract
Physical fitness and exercise play pivotal roles in maintaining a healthy lifestyle, and technology integration has revolutionized how individuals approach their fitness regimes. This study focuses on developing an AI-enabled real-time exercise monitoring system using MediaPipe for pose estimation and OpenCV for angle calculation. By analyzing video inputs, the system accurately identifies key anatomical landmarks and calculates joint angles in real time, providing immediate feedback on exercise form. This technology enhances the precision and reliability of exercise monitoring, supporting fitness enthusiasts and trainers in optimizing workout routines. Operating effectively across various lighting conditions and backgrounds, the system promotes proper exercise execution to improve training outcomes and mitigate injury risks in gym and home settings. Incorporating AI-based pose analysis represents a significant advancement in fitness technology, showcasing AI has potential to enhance exercise monitoring and facilitate safer, more effective workouts. This research underscores the practical application of AI in fitness, offering insights into its role in promoting overall fitness and well-being through innovative real-time exercise analysis.