THE DEVELOPMENT AND CHANGES OF TRIPLE JUMPERS’ BALANCE BOARD COMBINED WITH SPECIAL TRAINING BY DEEP LEARNING APPROACH

Abstract

This study aims to improve the athletic performance of triple jumpers in special physical training. First, the triple jumper’s balance board is explained with the special training method. Second, three-dimensional tracking scan image analysis is used to videotape the training process of triple jumpers. Convolutional Neural Networks (CNNs) perform image analysis of triple jumpers performing specific training with a balance board. Finally, the training of triple jumpers is subjected to image analysis. According to the special training speed, angle, and time of triple jumpers combined with the balance board, the physical fitness changes are analyzed. The results show that triple jumpers combined with the balance board training improved the technique of step jump from the aspects of take-off speed, landing speed, all angles of step jump, and support time. The combination of the balance board and the special training significantly improves the speed of the triple jumpers, and the changes of various angles during the training process are also relatively improved, which improves the balance and coordination of the training limbs of the triple jumpers. The balance board combined with special training can significantly improve the physical fitness of triple jumpers. CNN is used to analyze and validate triple jumpers combined with balance board training images. The experimental training analysis has achieved the ideal effect. This study uses the image analysis method to analyze the training process of athletes based on deep learning and provides direction for athletes’ special physical training.