基于EEMD和多域特征融合的手势肌电信号识别研究

Research on gesture EMG signal recognition based on EEMD and multi domain feature fusion

人造假肢技术目前已成为一个研究的热点,手势肌电信号的识别为人造假肢的研究做出了贡献.提出基于总体经验模态分解(EEMD)的信号处理方法,首先将采集到的信号经EEMD分解成若干个固有模态函数分量(IMF),然后在得到的前三阶IMF的基础上提取时域、频域和希尔伯特域特征,融合提取的特征组成多域特征向量组,最后送入支持向量机分类器中来分类6种手势动作,识别率最高可达到98.7%.实验结果表明,通过EEMD分解原始信号提取的多域特征用来识别手势动作是可行的.

Currently,artificial prosthetic technology has become a research hotspot,the identification of gesture surface electromyography signal made a significant contribution to the study of artificial prostheses.This paper proposed a signal processing method based on Ensemble Empirical Mode Decomposition（ EEMD）,which firstly decomposed the EMG signal into several Intrinsic Mode Functions（ IMF） and then extractied the time domain feature,frequency domain feature and Hilbert domain feature from the first three IMF.The extracted features composed a multi domain feature vector. Subsequently,the multi domain feature vector was input to Support Vector Machines（ SVM） to classify six types of gesture actions,the mathod reached highest recognition rate,which was 98.7% .Experimental results showed,using EEMD to decompose the original signal to extract multi domain features,was feasible to identify gestures.