适用不同Brunnstrom等级患者基于表面肌电信号的动作识别方法  

Method for sEMG-based Motion Recognition for Patients at Different Brunnstrom Stages

作　　者：王丰焱 张道辉[1,2] 李自由 赵新刚[1,2] WANG Fengyan;ZHANG Daohui;LI Ziyou;ZHAO Xingang(State Key Laboratory of Robotics,Shenyang Instiute of Automation.Chinese Academy of Sciences,Shenyang 110016.China;Institutes for Robotics and Intelligent Manufacturing,Chinmese Academy of Sciences,Shenyang 110169,China;University of Chinese Acadeny of Scieces,Beijing 100049,China)

机构地区：[1]中国科学院沈阳自动化研究所机器人学国家重点实验室,辽宁沈阳110016 [2]中国科学院机器人与智能制造创新研究院,辽宁沈阳110169 [3]中国科学院大学,北京100049

出　　处：《机器人》2020年第6期661-671,685,共12页Robot

基　　金：国家自然科学基金(U1813214,61903360,61773369,61821005);国家重点实验室自主课题(2020-Z12);中国博士后科学基金(2019M661155).

摘　　要：针对不同患病程度的脑卒中患者运动意图识别率低的问题,提出了一种适用于不同Brunnstrom等级患者基于表面肌电信号(sEMG)的动作识别方法.首先将所有等级患者s EMG数据进行融合,使用tsfresh库提取特征,然后基于随机森林(random forest,RF)模型筛选特征,并利用筛选的特征训练动作分类模型.进一步,通过研究动作和康复等级的关系,确定了康复评估动作并设计了康复等级自动评估算法.为了验证所提方法的有效性,在24例患者sEMG数据上进行了测试,实验结果表明所提方法能够将9种动作和6类康复等级的平均识别精度分别提升至89.81%和94%.基于所提方法构建的手部康复机器人系统能够实现康复等级自动评估.Aiming at the problem of a low accuracy of motion intention recognition for stroke patients at different disease degrees,a motion recognition method based on surface electromyography(sEMG)for patients at different Brunnstrom stages is proposed.Firstly,sEMG data of patients at different Brunnstrom stages are fused,and tsfresh library is used for feature extraction.Then,the features are selected based on random forest(RF)model,and the selected features are used for training action classification model.Furthermore,the rehabilitation evaluation actions are determined by studying the relationships between actions and rehabilitation grades,and an automatic evaluation algorithm of rehabilitation grade is designed.In order to verify the effectiveness of the proposed method,the sEMG data of 24 patients are tested.The experimental results show that the proposed method can improve the average recognition accuracy of 9 kinds of actions and 6 types of rehabilitation stages to 89.81%and 94%respectively.The hand rehabilitation robot system based on the proposed method can realize the automatic evaluation of rehabilitation grade.

关 键 词：脑卒中患者 表面肌电信号(sEMG) Brunnstrom等级 模式识别 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]