王以飞, 王伟, 田姗姗, 李孟轩, 李鹏斐, 陈曦. 基于静电信号的人体动作识别[J]. 机器人, 2018, 40(4): 423-430.DOI: 10.13973/j.cnki.robot.180170.
WANG Yifei, WANG Wei, TIAN Shanshan, LI Mengxuan, LI Pengfei, CHEN Xi. Human Motion Recognition Based on Electrostatic Signals. ROBOT, 2018, 40(4): 423-430. DOI: 10.13973/j.cnki.robot.180170.
Abstract:A human motion recognition method by detecting electrostatic signals generated by human behaviors is proposed. Based on the analysis of the charge characteristics of human body, a static electricity detection system is designed to collect the electrostatic induction signals of 5 typical actions of the tested persons, i.e. walking, stepping, sitting down, taking the goods, and waving hand. The characteristic parameters of the collected 5 kinds of human body electrostatic signals are extracted, their significant differences are analyzed, and the characteristic parameters for classification are optimized. 3 kinds of classification algorithms including support vector machine, decision tree-C4.5 and random forest, are used based on Weka platform to classify the 250 collected signal samples by 10-fold cross-validation. The results show that the random forest algorithm obtains the best recognition effect with the accuracy of 99.6%. The research shows that the proposed action classification method based on human electrostatic signals for single environment can effectively identify typical human actions.
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