The development and progress of multi-sensor data fusion theory and methods have also laid the foundation for the research of human body posture tracking system based on inertial sensing. The main research in this paper is the simulation of gymnastics performance based on MEMS sensors. In the preprocessing to reduce noise interference, this paper mainly uses median filtering to remove signal glitches. This article uses virtual character models for gymnastics performances. 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 amount and coordinate offset of each sensor node’s limb, and uses the character model to realize the real-time rendering of the virtual character model. At the same time, it controls the storage of sensor data, the drive of the model, and the display of the graphical interface. When a gesture action is about to occur, 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 the MEMS sensor. When the gesture action is completed, give the system a signal to end the action. Mark the end of the action, so that you can capture the original signal data during the beginning and end of the gesture action. In order to ensure the normal communication between PS and PL, it is necessary to test the key interfaces involved. Because the data received by the SPI acquisition module is irregular, it is impossible to verify whether the data is wrong, so the SPI acquisition module is replaced with a module that automatically increments data, and the IP core is generated, and a test platform is built for testing. The data shows that the average measurement error of X-axis displacement of the space tracking system is 8.17%, the average measurement error of Y-axis displacement is 7.51%, the average measurement error of Z-axis displacement is 9.72%, and the average error of three-dimensional space measurement is 8.7%. The results show that the MEMS sensor can accurately recognize the action with high accuracy.

多传感器数据融合理论和方法的发展和进步，也为基于惯性感知的人体姿态跟踪系统的研究奠定了基础。本文的主要研究是基于MEMS传感器的体操表演仿真。在降低噪声干扰的预处理中，本文主要采用中值滤波去除信号毛刺。本文使用虚拟人物模型进行体操表演。计算机通过蓝牙通信模块从动作捕捉设备的sink节点接收传感器数据。该单元计算传感器数据处理的动态链接库输出的四元数，计算每个传感器节点肢体的旋转量和坐标偏移量，并使用角色模型实现虚拟角色模型的实时渲染。同时控制传感器数据的存储、模型的驱动、图形界面的显示。当手势动作即将发生时，向系统发出触发信号，标记动作的开始，从而获取MEMS传感器各轴信号的初始数据。当手势动作完成时，给系统一个结束动作的信号。标记动作的结束，以便您可以在手势动作的开始和结束时捕获原始信号数据。为了保证PS和PL之间的正常通信，需要对涉及的关键接口进行测试。由于SPI采集模块接收到的数据是不规则的，无法验证数据是否有误，于是将SPI采集模块替换为自动递增数据的模块，并生成IP核，搭建测试平台进行测试。数据显示，空间跟踪系统X轴位移平均测量误差为8.17%，Y轴位移平均测量误差为7.51%，Z轴位移平均测量误差为9.72%，三维空间测量的平均误差为8.7%。结果表明，MEMS传感器可以准确识别动作，准确度高。数据显示，空间跟踪系统X轴位移平均测量误差为8.17%，Y轴位移平均测量误差为7.51%，Z轴位移平均测量误差为9.72%，三维空间测量的平均误差为8.7%。结果表明，MEMS传感器可以准确识别动作，准确度高。数据显示，空间跟踪系统X轴位移平均测量误差为8.17%，Y轴位移平均测量误差为7.51%，Z轴位移平均测量误差为9.72%，三维空间测量的平均误差为8.7%。结果表明，MEMS传感器可以准确识别动作，准确度高。