An applied study of using deep learning technology to develop training programs for athletes in college physical education

Abstract

This paper proposes the integration method of athlete training volume monitoring information based on deep learning, preprocessing the monitoring information using wavelet transform, obtaining the athlete training volume information after denoising and dimensionality reduction, combining with the theory of deep learning, utilizing convolutional neural network for feature extraction and integration of the processed information, and performing simulation test and analysis. The average Gini coefficient is 84.926, which proves the effectiveness of the method used in this paper. After exercising, the athletes’ weight and body fat rate were monitored to reduce a certain degree, and their lung capacity was improved. A deep learning algorithm generated personalized exercise program training achieved a heart rate interval of 94% for safe and effective workouts. The three stages of the test subjects’ training were tracked and analyzed, and the use of training monitoring can effectively promote the efficiency of training. The physical fitness program (P < 0.01) and physical fitness test (P < 0.05) in the first stage compared with the third stage showed significant improvement.

Keywords

• deep learning
• wavelet transform
• convolutional neural network
• monitoring information
• motion training
• 97m50