Machine Learning for the Posture Evaluation of Women Snatch Barbell Trajectory

Abstract

Barbell trajectory provides much kinematic information which can indicate the lifter's performance. However, kinematic parameters are not only gathering difficult but also hard to understand. This paper proposes a barbell trajectory evaluation inference that indicates the lifter's snatch performance from the barbell trajectory. We gathered four competitions and obtained the barbell trajectories from each lifter's attempt. Furthermore, five weightlifting experts recruit to indicate the performance categories, which are goodlift-good posture, goodlift-normal posture, nolift-good posture, and nolift-normal posture, as our data label. VGG16 convolution neural network utilize in our trajectory evaluation inference. The accuracy of the proposed inference is approximate 71.11%. From these results, our proposed barbell trajectory inference can provide a high accuracy performance evaluator for athlete self-training and competition performance analysis.