Research on the Influence of Progressive Strength Training Based on Deep Learning on Athletes’ Upper Limbs and Circumference

At present, progressive strength training in sports training is mainly based on free strength training equipment and combination equipment, and the load intensity depends on the events, seasons and individual conditions of athletes. High-quality training is the key to improving sports performance and training efficiency, and a better grasp of movement rhythm can effectively improve maximum strength. This paper studies the influence of progressive strength training based on deep learning (DL) on athletes’ upper limbs and circumference. Making full use of the existing 2D pose estimation is mature, and applying it to athletes’ 3D pose estimation can effectively improve the accuracy of athletes’ 3D pose estimation, the last layer outputs 3D attitude prediction. The results show that the circumference of left arm in group A, group B and common movement rhythm is also significantly different from those before training ([Formula: see text]), with the increase in the values of 3.36%, 6.43% and 2.01%, respectively. It can be seen that after eight weeks of maximum strength of bench press, the three movement rhythm groups also have a significant impact on the arm circumference of the subjects. In this paper, the classification error of 3DPoseNet network is reduced by at least 6.38% on average, which shows the effectiveness of this method.