Improved Deep Learning Method for Intelligent Analysis of Sports Training Posture

Abstract

Motion recognition based on human bones has attracted extensive attention in recent years because of its simplicity and robustness. Considering the causality of human movement, this paper proposes an improved deep learning method for posture analysis in sports training. In order to deal with the complex situation of calculating joint torques as weights, the edge weights and convolution weights of bone maps are used as auxiliary information networks according to the causality of joint distribution. Thus, the stronger driving force of joint weights in the neural network is improved, the low importance of joint attention is reduced, and the high importance of joint attention is enhanced. Experiments on three public motion recognition datasets show that the proposed method can distinguish similar motions effectively compared with the mainstream methods. Besides, experiments on a challenging UCF (University of Central Florida) sports dataset show that the proposed method can effectively enhance the motion features and improve the accuracy of recognition.