Aerobics is one of the main contents of physical education, which has a positive role in promoting the health of young people. This paper mainly studies the parallel processing method of inertial aerobics multisensor data fusion. In this paper, an aerobics exercise system is designed, which uses digital filter to remove the noise generated in the process of exercise. In this paper, Kalman filter is used to filter the pulse error of accelerometer, and the data structure of unidirectional link is used to achieve the effect of sliding window, which can reduce the memory cost to the greatest extent. In this paper, the region of moving object is determined by horizontal and vertical projection of binary symmetric difference image. At the same time, the optimal feature combination is selected from the reduced features by feature subset selection, and the classification algorithm is used as the evaluation function in the optimization process. Finally, the collected data are tested, analyzed, and sorted out. The experimental data show that, after calibrating the sensor data, the static x-axis and y-axis data are about 0 g, and the z-axis data are about 1 g, which is closer to the real value. The results show that the attitude data collected by the inertial sensor can be stably transmitted to the software of the computer wirelessly for attitude reconstruction, and the recognition of each attitude and parameter has reached a high accuracy.

中文翻译：

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惯性有氧运动多传感器数据融合的并行处理方法

有氧运动是体育教育的主要内容之一，对促进年轻人的健康具有积极作用。本文主要研究惯性健美操多传感器数据融合的并行处理方法。本文设计了一种健美操运动系统，该运动系统使用数字滤波器消除运动过程中产生的噪音。本文采用卡尔曼滤波器对加速度计的脉冲误差进行滤波，并采用单向链路的数据结构来达到滑动窗口的效果，从而最大程度地降低了存储成本。在本文中，移动物体的区域由二进制对称差分图像的水平和垂直投影确定。同时，通过特征子集选择从精简特征中选择最佳特征组合，在优化过程中将分类算法作为评估函数。最后，对收集到的数据进行测试，分析和整理。实验数据表明，在校准传感器数据后，静态x轴数据和y轴数据大约为0 g，z轴数据大约为1 g，这更接近于实际值。结果表明，惯性传感器采集到的姿态数据可以稳定地无线传输到计算机软件中进行姿态重构，对每个姿态和参数的识别都达到了很高的精度。