Since the outbreak of Covid-19 infectious pneumonia in Wuhan, China, in January 2019, it has rapidly spread to 31 provinces (autonomous regions and municipalities) across the country within 3 months. Chengdu Sport University is the only physical education institution in Southwest China. During the epidemic period, the university actively responded to the call of the government and issued response measures as soon as possible to achieve the goal of zero infection among teachers and students. This article describes in detail how to deal with the new coronavirus in colleges and universities during the epidemic.

In order to track the limb movement trajectory of gymnasts, a method based on MEMS inertial sensor is proposed. The system mainly collects the acceleration and angular velocity data of 11 positions during gymnastics by constructing sensor network. Based on the two kinds of preprocessed data, the parameters such as sample mean, standard deviation, information entropy, and mean square error are calculated as classification features, the support vector machine (SVM) classification model is established, and the movements of six kinds of gymnastics are effectively recognized. The experimental results show that when the human body is doing gymnastics, the measured three-axis acceleration values are between -0.5 g~2.2 g, -1 g~2.8 g, and -1.8 g~1 g, respectively, and the static error range accounts for only 1.6%~2% of the actual measured data range. Therefore, it is considered that such static error has little effect on the accuracy of data feature extraction and action recognition, which can be ignored. It is proved that MEMS inertial sensor can effectively track the movement trajectory of gymnasts’ limbs.

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In order to explore the problem of human energy consumption in sports, a method based on MEMS sensor is proposed. Firstly, the data of the whole system is analyzed, including acceleration signal preprocessing, data fusion between accelerometer and gyroscope using the Kalman filter method, and feature extraction. Secondly, each module and the whole system are tested, respectively. Finally, the accuracy experiment is compared with other human motion energy consumption measurement devices to verify the feasibility and superiority of the system. The experimental results show that when measuring human motion energy consumption, the average accuracy of a bracelet 1 is 87%, the average accuracy of a bracelet 2 is 88%, the average accuracy of a bracelet 3 is 96%, and the average accuracy of the system is 94%.The system has relatively high accuracy in measuring human motion energy consumption, and its algorithm is more accurate. It is proved that MEMS sensor can effectively detect human energy consumption in sports.