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.

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.

To solve the problem of large error of motion MEMS sensor in motion trajectory detection, a motion trajectory tracking and detection system based on motion sensor is proposed. The principle of trajectory tracking is that the three-dimensional velocity and displacement can be obtained by integrating the acceleration. In this paper, acceleration sensor is used to obtain acceleration data of moving object. In order to reduce the data measurement error, a Kalman filter is designed and implemented to eliminate random noise. Aiming at the system nonlinear error, a two speed sampling compensation algorithm is designed and implemented by using the random characteristics of kernel process scheduling algorithm. The system accuracy is significantly improved without increasing the computational burden. According to the characteristics of floating-point instruction system on Advanced RISC Machine (ARM) platform, the algorithms of process core modules such as square root and matrix multiplication are optimized and improved, which greatly improves the computing performance of the system. According to the results of the study, the average error of measurement of X-line displacement measurement of the space control system is 8.06%, the average error of measurement of Y-line displacement measurement is 7.41%, the average error of measurement of Z-line displacement measurement is 9.61%, and the average error of 3D dimensional measurement is 7.6%. The effectiveness and feasibility of the system are verified.