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一种基于EEG和sEMG的假手控制策略

吴常铖 熊鹏文 曾洪 徐宝国 宋爱国

吴常铖, 熊鹏文, 曾洪, 徐宝国, 宋爱国. 一种基于EEG和sEMG的假手控制策略. 自动化学报, 2018, 44(4): 676-684. doi: 10.16383/j.aas.2017.c160218
引用本文: 吴常铖, 熊鹏文, 曾洪, 徐宝国, 宋爱国. 一种基于EEG和sEMG的假手控制策略. 自动化学报, 2018, 44(4): 676-684. doi: 10.16383/j.aas.2017.c160218
WU Chang-Cheng, XIONG Peng-Wen, ZENG Hong, XU Bao-Guo, SONG Ai-Guo. A Control Strategy for Prosthetic Hand Based on EEG and sEMG. ACTA AUTOMATICA SINICA, 2018, 44(4): 676-684. doi: 10.16383/j.aas.2017.c160218
Citation: WU Chang-Cheng, XIONG Peng-Wen, ZENG Hong, XU Bao-Guo, SONG Ai-Guo. A Control Strategy for Prosthetic Hand Based on EEG and sEMG. ACTA AUTOMATICA SINICA, 2018, 44(4): 676-684. doi: 10.16383/j.aas.2017.c160218

一种基于EEG和sEMG的假手控制策略

doi: 10.16383/j.aas.2017.c160218
基金项目: 

国家自然科学基金 61673105

国家自然科学基金 91648206

南京航空航天大学青年教师启动基金 56YAH17027

国家自然科学基金 61663027

江苏省自然科学基金 BK20170803

国家自然科学基金 61325018

详细信息
    作者简介:

    吴常铖, 南京航空航天大学自动化学院讲师.2016年获东南大学博士学位.主要研究方向为机器人传感与控制技术.E-mail:changchengwu@nuaa.edu.cn

    熊鹏文, 南昌大学信息工程学院讲师.2015年获东南大学博士学位.主要研究方向为机器人传感与控制和遥操作.E-mail:steven.xpw@ncu.edu.cn

    曾洪, 东南大学仪器科学与工程学院副教授.主要研究方向为机器人传感与控制技术.E-mail:hzeng@seu.edu.cn

    徐宝国, 东南大学仪器科学与工程学院副教授.主要研究方向为机器人传感与控制技术.E-mail:xubaoguo@seu.edu.cn

    通讯作者:

    宋爱国, 东南大学教授.主要研究方向为机器人传感与控制技术, 信号处理和遥操作技术.本文通信作者.E-mail:a.g.song@seu.edu.cn

A Control Strategy for Prosthetic Hand Based on EEG and sEMG

Funds: 

Supported by National Natural Science Foundation of China 61673105

Supported by National Natural Science Foundation of China 91648206

Young Teacher Startup Foundation of Nanjing University of Aeronautics and Astronautics 56YAH17027

Supported by National Natural Science Foundation of China 61663027

Jiangsu Natural Science Foundation BK20170803

Supported by National Natural Science Foundation of China 61325018

More Information
    Author Bio:

    Lecturer at the College of Automation Engineering, Nanjing University of Aeronautics and Astronautics. He received his Ph. D. degree from Southeast University in 2016. His research interest covers robotic sensing and control

    Lecturer at the School of Information and Engineering, Nanchang University. He received his Ph. D. degree from Southeast University in 2015. His research interest covers robotic sensing and control and tele-operation

    Associate professor at the School of Instrument Science and Engineering, Southeast University. His research interest covers robotic sensing and control

    Associate professor at the School of Instrument Science and Engineering, Southeast University. His research interest covers robotic sensing and control

    Corresponding author: SONG Ai-Guo Professor at Southeast University. His research interest covers robotic sensing and control, signal processing, and tele-operation. Corresponding author of this paper
  • 摘要: 针对残臂较短或残臂上肌电信号测量点较少的残疾人使用多自由度假手的需求,提出一种基于脑电信号(Electroencephalogram,EEG)和表面肌电信号(Surface electromyogram signal,sEMG)协同处理的假手控制策略.该方法仅用1个肌电传感器和1个脑电传感器实现多自由度假手的控制.采用1个脑电传感器测量人体前额部位的EEG,从测量得到的EEG中提取出眨眼动作信息并将其用于假手动作的编码;采用1个肌电传感器测量手臂上的sEMG,并针对肌电信号存在个体差异和位置差异的问题,采用自适应方法实现手部动作强度的估计;采用振动触觉技术设计触觉编码用于将当前假手的控制指令反馈给佩戴者,从而实现EEG和sEMG对多自由度假手的协同控制.为验证该控制策略的有效性进行了实验研究,结果表明,提出的假手控制策略是有效的.
    1)  本文责任编委 程龙
  • 图  1  控制策略框图

    Fig.  1  Diagram of the control strategy

    图  2  动作编码环节

    Fig.  2  Process of the motion coding

    图  3  MindWave测量得到的EEG信号及其方差

    Fig.  3  EEG signal and its variance

    图  4  肌电传感器贴合位置示意图

    Fig.  4  Sketch of the measurement locations of the sEMG sensors

    图  5  同一块肌肉不同位置测量得到的两路肌电信号

    Fig.  5  Two channels of sEMG captured from different locations of the same muscle

    图  6  基于肌电信号的手部动作强度估计

    Fig.  6  Action strength estimation based on sEMG

    图  7  振动袖带

    Fig.  7  Vibration cuff

    图  8  振动器分布示意图

    Fig.  8  Distribution sketch of the vibrators

    图  9  触觉反馈提示编码

    Fig.  9  Coding of the tactile feedback

    图  10  EEG和sEMG协同控制流程图

    Fig.  10  Flow chart of the coordinated control based on EEG and sEMG

    图  11  实验场景

    Fig.  11  Experimental scene

    图  12  EEG测量设备

    Fig.  12  EEG measuring device

    图  13  表面肌电传感器

    Fig.  13  sEMG sensor

    图  14  两自由度假手

    Fig.  14  Two DOF Prosthetic hand

    图  15  对手爪闭合动作进行眨眼编码时测量得到的EEG

    Fig.  15  EEG captured in the process of blink coding of the hand closing

    图  16  物品抓取实验场景

    Fig.  16  Experimental scene of the grasping objects

    表  1  单位时间内眨眼次数与假手动作类型的关系

    Table  1  The relationship between the blink times and the motion type of the prosthetic hand

    第一环节眨眼次数 第二环节眨眼次数 动作类型
    2 2 手爪张开
    3 手爪闭合
    3 2 手腕顺时针转动
    3 手腕逆时针转动
    下载: 导出CSV

    表  2  手部动作识别结果

    Table  2  Results of the hand motion recognition experiments

    受试者编号 性别 动作编码正确率(%)
    手爪张开 手爪闭合 顺时针旋转 逆时针旋转 平均正确率
    1 96 100 100 96 98
    2 96 92 96 92 94
    3 92 96 96 96 95
    4 96 92 92 96 94
    5 92 100 96 100 97
    6 96 96 92 100 96
    7 92 96 96 96 95
    8 92 92 96 96 94
    9 96 100 92 92 95
    10 92 88 92 96 92
    下载: 导出CSV

    表  3  触觉感知实验结果

    Table  3  Results of the tactile perception experiments

    受试者编号 性别 动作编码正确率(%)
    手爪动作 手腕动作 手爪张开 手爪闭合 顺时针旋转 逆时针旋转 平均正确率
    1 100 100 100 100 100 100 100
    2 100 100 100 100 100 100 100
    3 100 100 100 100 95 95 98.33
    4 100 100 100 100 100 100 100
    5 100 100 100 100 95 100 99.17
    6 100 100 100 100 100 95 99.17
    7 100 100 100 100 100 95 99.17
    8 100 100 100 100 100 100 100
    9 100 100 100 95 95 100 98.33
    10 100 100 95 100 95 95 97.5
    下载: 导出CSV

    表  4  砝码抓取实验结果

    Table  4  Results of the grasping weights

    受试者编号 性别 成功率(%)
    1 95
    2 95
    3 90
    4 100
    5 95
    6 90
    7 100
    8 95
    9 95
    10 100
    下载: 导出CSV

    表  5  纸杯取实验结果

    Table  5  Results of the grasping paper cups

    受试者编号 性别 成功率(%)
    1 95
    2 95
    3 90
    4 100
    5 95
    6 90
    7 100
    8 95
    9 95
    10 100
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-03-01
  • 录用日期:  2017-03-02
  • 刊出日期:  2018-04-20

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