Research on the Path of Enhancing Physical Education Teaching in Colleges and Universities Based on the Background of Deep Learning

Abstract

Abstract This paper firstly summarizes the factors affecting the ability of physical education teaching from the four points of society, family, school and teachers, and puts forward the path of physical education teaching enhancement in colleges and universities in view of the current dilemma of physical education teaching. Secondly, in the context of deep learning, the use of deep learning for the calculation of the human center of gravity information can improve the efficiency of human center of gravity vector angle feature extraction while using the vector pinch angle cosine body force to obtain the human limb pinch angle features, followed by posture estimation method to obtain the body upper and lower body orientation features, and then through the improved Open Pose network model will be converted from video stream data to the human body key point coordinates data, and then the ALSTM-LSTM model will be applied to the video stream data. Then, the ALSTM-LSTM model was applied to the sports teaching movement analysis and empirically analyzed for sports teaching in colleges and universities. According to the findings, the ALSTM-LSTM model has the best performance in all indexes, while the SVM has the worst performance. In physical education, through quality practice in the classroom, the reinforcement of the sports club and the maintenance of the extracurricular group “Gudong” running, the students’ physical indicators have been improved. This study is conducive to improving the current decline in college students’ physical health and is also of great practical significance in effectively improving the overall level of college students’ physical fitness.