Simulation of Gymnastics Performance Based on MEMS Sensor

Abstract

The development and progress of multi-sensor data fusion theory and method also lay the foundation for the research of human posture tracking system based on inertial sensor. This paper mainly studies the simulation of gymnastic performance based on MEMS sensors. In the preprocessing of reducing noise interference, this paper mainly uses median filter to remove signal burr. In this paper, the use of virtual character model for gymnastics performance. The computer receives sensor data from the sink node of the motion capture device through a Bluetooth communication module. The unit calculates the quaternion output from the dynamic link library of sensor data processing, calculates the rotation and coordinate offset of the limb where each sensor node is located, and realizes the real-time rendering of the virtual human model by using the driver of the human model. At the same time, it controls the storage of sensor data, the driving of model and the display of graphical interface. When the gesture action is about to happen, a trigger signal is given to the system to mark the beginning of the action, so as to obtain the initial data of each axis signal of MEMS sensor. When the gesture action is completed, a signal to end the action is given to the system to mark the end of the action, so that the original signal data between the beginning and end of the gesture action can be captured. In order to ensure the normal communication between PS and PL, it is necessary to test the key interface. Because the data received by the SPI acquisition module is irregular, it is unable to verify whether the data is wrong. Therefore, the SPI acquisition module is replaced with an automatic incremental data module, and it is generated into an IP core to build a test platform for testing. The data show that the average measurement errors of x-axis displacement, Y-axis displacement, z-axis displacement and three-dimensional displacement are 8.17%, 7.51%, 9.72% and 8.7%, respectively. The results show that the MEMS sensor can accurately identify the action with high accuracy.