An Artificial Intelligence Fitness Coach to Aid in Injury Prevention While Squatting Using Machine Learning and Mediapipe
Year: 2025
Type: conference-paper
Abstract: 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.
Authors: Matthew Olen, Jonathan Sahagun
Institution: California Polytechnic State University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4414475948