Affiliations: [a] Physical Education Department, Hebei University of Technology, Tianjin, China | [b] Physical Education Department, Hebei University of Economics and Business, Shijiazhuang, Hebei, China
Correspondence:
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Corresponding author: Pei Zhang, Physical Education Department, Hebei University of Technology, Tianjin 300401, China. E-mail: wangyee@heuet.edu.cn.
Abstract: Human posture detection is easily affected by the external environment, resulting in blurred results of limb feature extraction. In order to improve the accuracy and speed of human motion detection, this paper proposes a deep learning-based motion detection method in competitive sports training. The double parallel convolution network algorithm in the depth learning algorithm is used to process the collected action information, extract the body action features, and greatly reduce the operation scale; With the help of the theory of motion mechanics, the mechanical parameters in the motion process are calculated to eliminate outliers and reduce feature dimensions; With the help of motion inertial sensors and joint degrees of freedom, the limb motion detection results are obtained. The experimental results show that the average recognition rate of the method for different motion actions is 99.5%, and the average detection time is 148 ms, with good application performance.
Keywords: Deep learning, convolutional neural network, body motion recognition, kinematic mechanics theory, joint degrees of freedom, inertial sensor
