sEMG Pattern Recognition of Muscle Force of Upper Arm for Intelligent Bionic Limb Control

作     者：Xu Zhuojun Tian Yantao Li Yang 

作者机构：School of Communication Engineering Jilin University Changchunl30000 China Key Laboratory of Bionic Engineering Ministry of Education Jilin University Changchun 130000 China 

出 版 物：《Journal of Bionic Engineering》 (仿生工程学报（英文版）)

年 卷 期：2015年第12卷第2期

页      面：316-323页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 12[管理学] 1004[医学-公共卫生与预防医学(可授医学、理学学位)] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：the Jilin University “985 Project" Engineering Bionic Sci. & Tech. Innovation Platform Doctoral Interdisciplinary Scientific Research Projects Fund of Jilin University supported by the Key Project of Science and Technology Development Plan for Jilin Province Chinese College Doctor Special Scientific Research Fund 

主　　题：intelligent bionic limb sEMG muscle force window sample entropy window kurtosis 

摘      要：Two new feature extraction methods, window sample entropy and window kurtosis, were proposed, which mainly aims to complete the surface Elcctromyography （sEMG）-muscle force pattern recognition for intelligent bionic limb. The inspiration is drawn from physiological process of muscle force generation. Five hand movement tasks were implemented for sEMG-muscle force data record. With two classical features： Integrated Electromyography （IEMG） and Root Mean Square （RMS）, two new features were fed into the wavelet neural network to predict the muscle force. To solve the issues that amputates＇ residual limb couldn＇t provide full train data for pattern recognition, it is proposed that force was predicted by neural network which is trained by contralateral data in this paper. The feasibility of the proposed features extraction methods was demonstrated by both ipsi- lateral and contralateral experimental results. The ipsilateral experimental results give very promising pattern classification accuracy with normalized mean square 0.58 ± 0.05. In addition, unilateral transradial amputees will benefit from the proposed method in the contralateral experiment, which probably helps them to train the intelligent bionic limb by their own sEMG.