Matthew Olen¹ and Jonathan Sahagun², ¹USA, ²California State Polytechnic University, USA

Athletes and casual gym-goers often risk injury when performing squats due to poor form. To address this problem, this project proposes an artificial intelligence-based system that uses computer vision and machine learning to monitor squat posture and provide real-time corrective feedback. The system leverages Mediapipe for pose estimation and K-Nearest Neighbors (KNN) for classification of squat form [8]. Major challenges included maintaining model accuracy, processing video data on a Raspberry Pi, and adjusting for different squat variations. Through experimental testing, the AI demonstrated 90-94% accuracy in identifying proper and improper squats, even when adapting to elevated squat styles. Compared to previous methodologies, this system improves by providing immediate feedback rather than post-set evaluations. Ultimately, this project presents a lightweight, affordable, and portable solution to improve exercise safety and performance, reducing the risk of serious injuries in athletic and fitness communities.

Computer Vision, Machine Learning, Squatting Injury Prevention, Artificial Intelligence, Pose Estimation, Fitness