A Machine Learning Approach to Accurate Gym Exercise Form Detection Using MediaPipe

This paper introduces an automated system for gym exercise form detection, leveraging MediaPipe1 for real-time pose estimation and OpenCV2 for computer vision processing. The system analyzes key body landmarks during exercises like squats, deadlifts, and bicep curls, providing immediate feedback on form accuracy. By detecting incorrect postures, such as improper knee alignment or back curvature, the system aims to reduce the risk of injury and enhance workout effectiveness. The proposed approach is designed to be lightweight, accessible, and capable of running on consumer-grade hardware, making it practical for widespread use. Experimental results demonstrate high accuracy in detecting common form errors, showcasing the potential of this system as a cost-effective alternative to traditional personal training. This work contributes to the growing field of automated fitness monitoring and highlights the role of computer vision in improving exercise safety and performance.