An empirical study on the improvement of students’ physical fitness and health in college physical education programmes based on big data

Improving the quality of college physical education classroom teaching, ensuring that the course can achieve the due exercise load, and at the same time ensuring that the content arrangement of the physical education classroom can promote the development of students’s physical fitness and health, has become an important issue facing the physical education teaching in colleges and universities. This paper constructs a sports teaching IOT platform based on heart rate monitoring, which takes the heart rate belt as the sensor and completes the connection and transmission of the user’s heart rate data through the NFC device and the mobile smart device based on the Android system. It realizes the monitoring of the user’s heart rate data. Based on this heart rate monitoring platform, a decision tree algorithm for college students’ physical fitness data analysis is proposed, which analyzes the physical fitness and health status of the overall students in the school through the uploaded heart rate data, thus providing reasonable suggestions for the arrangement of physical education curriculum content. The heart rate monitoring method in this paper can achieve over 85% monitoring accuracy in a shorter execution time, and the decision tree algorithm for college student fitness optimization can control the students’ heart rate within the optimal heart rate index interval of aerobic exercise. Physical fitness test experiments show that they can provide the basis and guarantee for the core literacy of students’ physical education courses, and provide new ideas for promoting the innovative development of physical education.