Anomaly detection of bicep curl using pose estimation

Abstract

Resistance training exercises can cause adverse effects and even injuries if not executed correctly. The latest pose estimation technologies in computer vision could help provide real-time analysis on exercising motion using on-device cameras. However, to identify whether an individual is performing an exercise correctly, postural deviations or anomalies from the correct patterns must be identified. In this study, a versatile solution is formulated to detect and analyze a specific resistance training exercise – bicep curl using BlazePose and binary tree algorithms in machine learning based on specific pose features. Ten decision tree models are developed to identify ten target pose anomalies including deviated trunk angles and misplaced elbows and wrists. The model sensitivity ranges from 73.7% (external rotated shoulders) to 97.4% (over-flexed trunk). These predicted results would be very useful in giving specific postural advises to learners of fitness exercises. Our research outputs could be extended to other exercises, and be implemented in mobile applications for various purposes such as exergames and sports analysis.

Keywords: Pose estimation, Pose anomaly detection, Artificial intelligence, Exercise analysis, BlazePose

DOI: 10.54941/ahfe1003069