西安理工大学学报2023，Vol.39Issue(4)：513-520,8.DOI:10.19322/j.cnki.issn.1006-4710.2023.04.007
用于装配动作识别的肌电信号特征优化选择方法
Optimal selection method of electromyographic signal features for assembly gesture recognition
摘要
Abstract
In the research on gesture recognition by using machine learning methods,the recogni-tion accuracy largely depends on the characteristics of the input data.A feature analysis and opti-mization selection method are proposed for operating gesture recognition with the surface EMG signal.Based on the arm EMG signal which was acquired and smoothed,15 feature parameters are defined and extracted in the time domain,frequency domain and time-frequency domain;120 feature values are calculated for each frame data of 8 channels EMG signal and normalized to char-acterize a certain gesture;the extreme gradient boosting(XGBoost)algorithm and the univariate feature selection(UFS)algorithm are used to analyze the recognition contribution degree of the features from the two perspectives of feature parameters and feature value.The analytical results show that the two methods can not only greatly reduce redundant features,but also effectively improve the final recognition accuracy.The features selected by the UFS algorithm have more ad-vantages in recognition speed and accuracy.关键词
动作识别/表面肌电信号/特征选择/极限梯度提升算法/单变量特征选择算法Key words
gesture recognition/surface EMG signal/feature selection/XGBoost/UFS分类
信息技术与安全科学引用本文复制引用
刘永,宁蕊,李言,杨明顺,高新勤..用于装配动作识别的肌电信号特征优化选择方法[J].西安理工大学学报,2023,39(4):513-520,8.基金项目
陕西省重点研发计划项目(2021SF-421,2021SF-422) （2021SF-421,2021SF-422）
陕西省现代装备绿色制造协同创新中心自主研发基金项目(102-451421003) （102-451421003）
