Real Time Powerlifting Form Assessment using Yolov5 and Mediapipe

Abstract

This study introduces an on-device, real-time AI posture correction system tailored for the three core
powerlifting exercises: bench press, back squat, and conventional deadlift. We employ YOLOv5 for efficient person
detection, combined with an HSV-based background subtraction mask to dynamically crop the region of interest
(ROI), ensuring streamlined processing. To enable accurate, user-tailored feedback, we present the PolyView
Kinematic Corpus (PKC), a novel multi-camera 3D landmark dataset recorded from front, side, and oblique angles,
capturing concentric and eccentric phases from 150 lifters with diverse body types. Utilizing PKC data, we train only
machine learning classification algorithms (e.g., decision trees, LightGBM) to detect subtle joint alignment deviations
at rep “bottom” and “lockout” phases, with evaluations showing these methods achieve high accuracy in lift phase
classification. Our phase-sensitive feedback system, based on user-calibrated angle bands and temporal smoothing,
delivers precise visual overlays and concise audio prompts to guide lifters toward safer, more effective techniques.
Deployed via a low-latency web interface, the system provides exercise-specific cues in under 50 ms per frame, helping
lifters minimize injury risk and optimize performance without external sensors or complex setup.