Recognition of Wrong Sports Movements Based on Deep Neural Network

Abstract

During physical education (PE), the teaching quality is severely affected by problems like nonstandard technical movements or wrong demonstrative movements. High-speed photography can capture instantaneous movements that cannot be recognized with naked eyes. Therefore, this technology has been widely used to judge the sprint movements in track and field competitions, and assess the quality of artistic gymnastics. Inspired by three-dimensional (3D) image analysis, this paper proposes a method to recognize the standard and wrong demonstrative sports movements, based on 3D convolutional neural network (CNN) and graph theory. Firstly, a 3D posture perception strategy for demonstrative sports movements was constructed based on video sequence. Next, the authors provided the framework of the recognition system for standard and wrong demonstrative sports movements. After that, a 3D CNN was stablished to distinguish between standard and wrong demonstrative sports movements. The proposed method was proved effective and superior through experiments. The research results provide a good reference for the application of 3D image analysis in the recognition of other body behaviors and movements.