传感技术学报2016，Vol.29Issue(4)：512-518,7.DOI:10.3969/j.issn.1004-1699.2016.04.009
基于小波变换的多特征融合sEMG模式识别
sEMG Pattern Recognition Based on Multi Feature Fusion of Wavelet Transform
摘要
Abstract
In view of the poor characterization of single feature value,multi feature fusion based on different wavelet basis was adopted to extract the surface EMG signal according to multi resolution analysis of wavelet transform. The experiment was conducted on ten testers and collected signals for four basic lower limb movements in daily life. First of all,discrete wavelet transform was used to decompose the surface EMG signals in multi-scale with DB, Dmey and Bior wavelet basis respectively. After that,it was founded that the characterization effects of different muscle vary by different extraction way. In order to combine the characteristics of different features ,features were fused to analyze and compare. At last,the feature values were input to the Elman neural network and BP neural net⁃work for pattern recognition and comparison analysis. Experimental results showed that the recognition rate ob⁃tained by fusing the eigenvalues is higher than single feature with the accuracy up to 98.7%,and the BP neural net⁃work is better than the Elman neural network.关键词
表面肌电/信号处理/模式识别/多特征融合/小波变换Key words
surface sEMG/signal processing/pattern recognition/multi feature fusion/wavelet transform分类
信息技术与安全科学引用本文复制引用
于亚萍,孙立宁,张峰峰,张建法..基于小波变换的多特征融合sEMG模式识别[J].传感技术学报,2016,29(4):512-518,7.基金项目
国家863计划项目 （）
