The evaluation method for the completion degree of sports training action based on deep residual network

Article type: Research Article

Authors: Quyang, ^{; *}

Affiliations: Jiaozuo University Taiji Martial Arts Institute, Jiaozuo, China

Correspondence: [*] Corresponding author. Quyang, Jiaozuo University Taiji Martial Arts Institute, Jiaozuo, China. E-mail: [email protected].

Abstract: The completion degree of sports training can not reach the corresponding standard, and the training effect will be greatly weakened. In order to improve the effect of sports training, the evaluation method of sports training completion degree based on deep residual network is studied. The image collector based on ARM is used to collect the action images of athletes in sports training, and the collected action images are preprocessed based on spatial scale filtering and regression factors. Construct a depth residual network, learn the implicit relationship between athletes’ state and the dynamic change process of sports training actions through off-line training, and train the model; In the online application process, the preprocessed action images will be input into the trained evaluation model to evaluate the athletes’ sports training action completion in real time. At the same time, residual shrinkage unit and attention mechanism are used to optimize the depth residual network, which improves the training efficiency and evaluation performance of the network. The experimental results show that this method has good evaluation performance under the condition of setting parameters, and can effectively improve the effect of physical training.

Keywords: Deep residual network, sports training, action completion degree, image acquisition, image denoising

DOI: 10.3233/JIFS-233773

Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 677-691, 2024