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As an important part of the education system, college physical education directly affects the comprehensive development of college students’ physical and mental quality. It is necessary to build a scientific and efficient evaluation index of college physical education teaching quality. Physical fitness monitoring is an important indicator for the quality evaluation of physical education. However, how to achieve lightweight, portable, and high-accuracy quantitative physical fitness monitoring is currently a major challenge. In order to solve the above challenges, this paper proposes a method of constructing a physical education quality evaluation index based on wearable devices. The wearable device collects human ECG signals, calculates the exercise intensity of participating students, and realizes quantitative evaluation of the quality of physical education teaching. Aiming at the problems of complex equipment and low accuracy of the existing exercise intensity detection methods, this paper proposes an ECG signal wave group detection algorithm based on a one-dimensional convolutional neural network (1D-CNN) to obtain the heart rate variability signal more accurately. After obtaining the ECG feature vector, the SVM classifier is used to predict the exercise intensity. In order to verify the effectiveness of the method in this paper, the real data collected from students of one university and a public available dataset are selected for experiments. The experimental results show that the method proposed in this paper achieves a good performance.

At present, the fast-paced work and life make people under great pressure, and people’s enthusiasm for fitness is getting higher and higher, which is in contradiction with the shortage of existing stadiums. So it is considerably significant to open shared stadiums near where citizens live for booking. Therefore, how to allow citizens to book a suitable stadium according to their own needs through mobile phones or computers is an urgent problem to be solved. The booking of the shared stadium can be regarded as a mobile edge computing (MEC) scenario, and the problem can be transformed into task scheduling research under MEC through intelligent scheduling method. When using edge computing (EC) technology for service calculation, the mobile terminal needs to offload the service to the edge computing server. After the server completes the calculation, the calculation results will be sent back to the mobile terminal. Therefore, the calculation time and system energy consumption in the calculation process can be further reduced through task scheduling to improve user satisfaction. In this study, joint scheduling of service caching and task algorithm is proposed to reduce the latency of booking shared stadium request and improve user experience. The simulation results show that the proposed algorithm with edge cooperation idea can achieve lower average system latency at lower load level and can significantly reduce the cloud offloading ratio under low and middle pressure. In addition, the proposed algorithm uses the secondary transfer of more tasks to reduce the pressure of local task running. Finally, the quality of experience (QoE) satisfaction rate under low pressure is guaranteed.

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