The role of deep learning-based variable speed running training method in agility improvement of badminton players

Variable speed running training method is an efficient training method targeting the improvement of athletes' agility.In this paper, 30 male students from two badminton special classes of physical education majoring in a college of 2021 were selected as experimental subjects, and hexagonal ball reaction test, hexagonal jump test, repeated straddle test, standing bench press test, closed-eye in-situ step test, and low gravity center of gravity quadrangular running test were chosen as the evaluation indexes of agility quality.A new high-resolution multi-scale feature fusion network was designed for running stance estimation, and the effects of variable speed running training method and conventional agility training on the agility quality of young badminton players were analyzed.The performance curve of the RHPNet designed in this paper has low convergence difficulty and high recognition accuracy, which tends to 0.83, and performs much better than the LSTM network.The intergroup data after the experiments of the experimental group and the control group show that there are significant differences in the performance of the hexagonal jump test, the 20s repeated straddle test, the hexagonal ball reaction test, and the closed-eye in-situ step test.It verifies the effectiveness of the network designed in this paper in the estimation of athletes' movements during running, and also shows that the training effect of variable speed running training is better than that of conventional agility training.