Li Peng, Zhou Jihe
College of Physical Education and Health, Zunyi Medical University, Zunyi, 563000 Guizhou, China.
College of Sports Medicine and Health, Chengdu Sports University, Chengdu, 610041 Sichuan, China.
Appl Bionics Biomech. 2022 Apr 27;2022:5292454. doi: 10.1155/2022/5292454. eCollection 2022.
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 g2.2 g, -1 g2.8 g, and -1.8 g1 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.
为了跟踪体操运动员的肢体运动轨迹，提出了一种基于MEMS惯性传感器的方法。该系统主要通过构建传感器网络来采集体操过程中11个位置的加速度和角速度数据。基于这两种预处理后的数据，计算样本均值、标准差、信息熵和均方误差等参数作为分类特征，建立支持向量机（SVM）分类模型，有效识别六种体操动作。实验结果表明，人体做体操时，测得的三轴加速度值分别在-0.5g2.2g、-1g2.8g和-1.8g1g之间，静态误差范围仅占实际测量数据范围的1.6%2%。因此，认为这种静态误差对数据特征提取和动作识别的准确性影响较小，可以忽略不计。证明了MEMS惯性传感器能够有效跟踪体操运动员肢体的运动轨迹。
