基于人工神经网络的表面肌电信号分类器研究进展  

Research Progress of Surface Electromyography Signal Classifier Based on Artificial Neural Network

作　　者：周小波 邹任玲[1] 卢旭华[2] 王海滨[2] 张俊翔 ZHOU Xiaobo;ZOU Renling;LU Xuhua;WANG Haibin;ZHANG Junxiang(School of Medical Instrument and Food Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;The Second Military Medical University,Chang Zheng Hospital,Shanghai 200003,China)

机构地区：[1]上海理工大学医疗器械与食品学院,上海200093 [2]第二军医大学附属长征医院,上海200003

出　　处：《电子科技》2021年第2期62-67,共6页Electronic Science and Technology

基　　金：国家自然科学基金(61803265);国家重点研发计划(2018YFC2002601);上海理工大学医工交叉项目(1019308505)。

摘　　要：表面肌电信号是一种重要的生理电信号。基于表面肌电信号建立人体康复动作识别系统操作方便,对身体无侵入性,对运动无干扰,有广阔的应用前景。肢体康复动作识别系统很大程度上依赖于信号特征提取和分类器的使用。文中对近几年基于人工神经网络建立的表面肌电信号分类器,包括LVQ分类器、ELM分类器、WNN分类器、ANFIS分类器、Alex Net分类器和GRNN分类器进行了回顾和探讨。通过对各种分类器的回顾比较,发现现有的不足之处并针对分类器的未来研究方向和发展趋势进行了分析和展望,为今后相关方面的研究提供一定的参考依据。The surface electromyography signal is an important physiological electrical signal.The human body rehabilitation motion recognition system based on the surface electromyography is easy to operate,hurtless to the body and no interference to motion,and has broad application prospects.The limb rehabilitation motion recognition system heavily relies on signal feature extraction and the use of classifiers.In this paper,the surface electromyography signal based on artificial neural network including LVQ classifier,ELM classifier,WNN classifier,ANFIS classifier,Alex Net classifier,and GRNN classifier are reviewed and discussed.After the review and comparison of various classifiers,some shortcomings are found and the future research directions and development trends of the classifiers are analyzed and prospected,which provides a reference for relevant research in the future.

关 键 词：表面肌电信号 模式识别 信号处理 分类算法 人工神经网络 分类器 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—计算机应用技术]