基于特征工程与级联森林的中风患者手部运动肌电识别方法  

An sEMG-based Hand Motion Recognition Method for Stroke Patients with Feature Engineering and Cascade Forest

作　　者：胡少康 张道辉[1,2] 赵新刚 褚亚奇[1,2,3] 张立新 赵利娜 HU Shaokang;ZHANG Daohui;ZHAO Xingang;CHU Yaqi;ZHANG Lixin;ZHAO Lina(State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China;Institutes for Robotics and Intelligent Manufacturing,Chinese Academy of Sciences,Shenyang 110169,China;University of Chinese Academy of Sciences,Beijing 100049,China;Rehabilitation Center,Shengjing Hospital of China Medical University,Shenyang 110134,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 [4]中国医科大学附属盛京医院康复中心,辽宁沈阳110134

出　　处：《机器人》2021年第5期526-538,共13页Robot

基　　金：国家自然科学基金(U1813214,61903360,61773369);中国博士后科学基金(2019M661155);辽宁省自然科学基金(2019-KF-01-06);辽宁省“兴辽英才计划”高水平创新创业团队(XLYC1908030).

摘　　要：针对基于表面肌电(sEMG)信号的中风患者运动意图识别率低的问题,提出了一种高识别率且适用于不同康复等级患者的手部运动意图识别方法。首先,使用30名不同康复等级患者的表面肌电数据进行了基于tsfresh库的特征提取和基于Feature-Selector库的特征选择,确定了最合适的滑动窗参数及适合患者运动识别任务的特征。然后,使用该方法进行动作识别实验,并和随机森林、卷积神经网络等方法比较,实验结果表明该方法对9种常用手部康复动作的平均识别精度为97.94%。最后,基于该方法开发了手部康复系统,通过在线识别实验分析了系统的实时性,并设计了患者跟踪实验以验证系统对患者手部康复的有效性。A hand motion intention recognition method with a high recognition rate and suitable for patients of different rehabilitation levels is proposed to solve the problem of low recognition rate of motion intention of stroke patients based on surface electromyography(sEMG) signal. Firstly, the sEMG data from 30 patients of different rehabilitation levels are used to carry out feature extraction based on tsfresh library and feature selection based on Feature-Selector library, and the most suitable sliding window parameters and the features suitable for the patient motion recognition task are determined. Then, some action recognition experiments are conducted with the proposed method, which is compared with other methods, including random forest, convolutional neural network, etc. The experimental results show that the average recognition accuracy of the proposed method for 9 commonly used hand rehabilitation actions is 97.94%. Finally, a hand rehabilitation system is developed based on the proposed method, its real-time performance is analyzed based on online recognition experiments, and a patient tracking experiment is designed to verify the effectiveness of the system for patient hand rehabilitation.

关 键 词：表面肌电信号 脑卒中 动作分类 人机交互 深度森林 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]