基于MEMS传感器的体操动作识别

针对目前视觉动作识别方法中普遍存在的背景复杂、活动范围有限、个人隐私泄露等问题,设计了一套基于MEMS惯性传感器的体操动作识别系统。该系统主要通过构建传感器网络,采集人体进行体操运动时11个位置的加速度和角速度数据。基于预处理后的两类数据,计算样本均值、标准差、信息熵、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,并对6种体操运动的动作进行了有效识别。实验结果表明,SVM算法较K-近邻、朴素贝叶斯、决策树等机器学习算法有更好的识别效果,平均识别率可达97%以上。

Gymnastics motion recognition based on MEMS sensor

In this paper, a set of gymnastics motion recognition system based on MEMS inertial sensor is designed to solve the problems of complex background, limited range of activities and personal privacy leakage. The system mainly collects acceleration and angular velocity data of 11 positions when the human body performs gymnastics by constructing a sensor network. Based on the pre processed two types of data, the parameters such as 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 actions of six gymnastics movements are effectively identified. The experimental results show that the SVM algorithm has better recognition effect than the machine learning algorithms such as K nearest neighbor, naive bayes and decision tree. The average recognition rate can reach over 97%.