下肢康复机器人的自适应人机交互控制策略

杜义浩; 邱石; 谢平; 郭子晖; 吴晓光; 李小俚

doi:10.16383/j.aas.2017.c160128

下肢康复机器人的自适应人机交互控制策略

doi: 10.16383/j.aas.2017.c160128

杜义浩^1,,
邱石^1,,
谢平^1, ,,
郭子晖^1,,
吴晓光^1,,
李小俚^1,

1.
燕山大学电气工程学院河北省测试计量技术及仪器重点实验室秦皇岛 066004

基金项目:

中国博士后科学基金项目 2015M581316

国家自然科学基金项目 61503325

国家自然科学基金项目 61673336

河北省教育厅高等学校科技计划项目 QN2016094

详细信息

作者简介:
杜义浩  燕山大学电气工程学院讲师.2012年获得燕山大学电路与系统专业博士学位.主要研究方向为康复机器人生物反馈控制, 神经生理信息特征提取.E-mail:duyihao@126.com

邱石  燕山大学电气工程学院硕士研究生.主要研究方向为机器学习与模式识别, 康复机器人控制.E-mail:qiushiqiu123@sina.com

郭子晖  燕山大学电气工程学院硕士研究生.主要研究方向为信号处理, 康复机器人控制.E-mail:gzh5090817@163.com

吴晓光  燕山大学电气工程学院副教授.2012年获得哈尔滨工业大学博士学位.主要研究方向为双足步行机器人的控制, 计算机视觉和机器学习, 特种机器人研究技术.E-mail:wuxiaoguang@ysu.edu.cn

李小俚  燕山大学教授.1998年获得哈尔滨工业大学机械工程博士学位.主要研究方向为脑调控与脑成像技术及在神经性疾病的应用.E-mail:xiaoli@bnu.edu.cn

通讯作者:
谢平燕山大学电气工程学院教授.2006年获得燕山大学燕山大学电路与系统专业工学博士学位.主要研究方向为脑机接口技术, 智能机器人控制, 虚拟康复技术.本文通信作者.E-mail:pingx@ysu.edu.cn

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出版历程
- 收稿日期: 2016-02-18
- 录用日期: 2017-04-07
- 刊出日期: 2018-04-20

Adaptive Interaction Control for Lower Limb Rehabilitation Robots

DU Yi-Hao^1
,,
QIU Shi^1
,,
XIE Ping^{1
, ,},
GUO Zi-Hui^1
,,
WU Xiao-Guang^1
,,
LI Xiao-Li^1
,

1.
Key Lab of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao 066004

Funds:

China Postdoctoral Science Foundation 2015M581316

National Natural Science Foundation of China 61503325

National Natural Science Foundation of China 61673336

the Science and Technology Research Project of Higher Education Institutions in Hebei Province QN2016094

More Information

Author Bio:
Lecturer at the Institute of Electric Engineering, Yanshan University. He received his Ph. D. degree from Yanshan University in 2012. His research interest covers biometric feedback control of the rehabilitation robot, and feature extraction of the neurophysiological information

Master student at the Institute of Electric Engineering, Yanshan University. His research interest covers machine learning and pattern recognition, and control of rehabilitation robot

Master student at the Institute of Electric Engineering, Yanshan University. His research interest covers signal processing and the control of rehabilitation robot

Associate professor at the Institute of Electric Engineering, Yanshan University. He received his Ph. D. degree in Harbin Institute of Technology in 2012. His research interest covers control of biped walking robot, computer vision and machine learning, and the research technology of the special robot

Professor at the Institute of Electric Engineering, Yanshan University. He received his Ph. D. degree in mechanical engineering from Harbin Institute of Technology in 1998. His research interest covers brain regulation, brain imaging techniques, and their applications in neurological disorders

Corresponding author: XIE Ping Professor at the Institute of Electric Engineering, Yanshan University. She received her Ph. D. degree from Yanshan University in 2006. Her research interest covers brain and machine interface technology, control based on intelligent robot, and virtual rehabilitation technology. Corresponding author of this paper

摘要

摘要: 针对康复机器人运动过程中的人机交互性问题，提出一种下肢康复机器人自适应人机交互控制策略.提取伸屈运动中下肢表面肌电信号（Surface electromyography，sEMG）和足底压力特征，分别用于表征下肢运动意图和人机交互力（Interaction force，IF）信息，建立基于sEMG-IF的人机交互信息融合模型，实现下肢康复机器人运动轨迹的在线规划；考虑主动康复运动过程中的人机交互作用，建立具有时变动态特性的人机系统动力学模型，设计间接模糊自适应控制器对期望轨迹进行跟踪控制，实现下肢康复机器人自适应人机交互控制.通过对5名被试者进行下肢康复机器人运动控制实验研究，验证所提方法的可行性和有效性.
- 下肢康复机器人 /
- 运动意图 /
- 人机交互 /
- 自适应控制
Abstract: Aiming at the problem of human-machine interaction in rehabilitation robot's movement, we propose an adaptive control strategy for lower limb rehabilitation robots. During flexion and extension, the surface electromyography (sEMG) of lower limbs and plantar pressure features are extracted respectively to represent lower limbs' motion intention and interaction force (IF). An sEMG-IF based human-machine interaction and information fusion model is established to program the motion trails of the rehabilitation robot online. Considering the human-machine interaction in active rehabilitation, a man-machine system dynamic model with time-varying dynamic characteristics is established. An indirect fuzzy adaptive controller is designed to trace and control the desired trajectory, and achieve adaptive interactive control of the lower limb rehabilitation robot. Validity and feasibility of the proposed strategy are verified by analysis of the data from 5 subjects under limb movement with the rehabilitation robot.
- Lower limb rehabilitation robot /
- motion intention /
- human-machine interaction /
- adaptive control
注释:

1) 本文责任编委王卫群

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图 1 人机系统机构

Fig. 1 Human-machine system

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图 2 sEMG预处理过程

Fig. 2 sEMG preprocessing process

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图 3 实验过程

Fig. 3 Experimental process

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图 4 原始sEMG

Fig. 4 Original sEMG

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图 5 归一化后sEMG

Fig. 5 Normalized sEMG

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图 6 sEMG包络提取

Fig. 6 sEMG envelope extraction

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图 7 股二头肌与股外侧肌包络差值

Fig. 7 The envelope difference of biceps and lateral muscles of femoral head

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图 8 人机交互力和运动意图

Fig. 8 Human-machine interaction and motion intention

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图 9 下肢康复机器人运动速度

Fig. 9 Movement speed of lower limb rehabilitation robot

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图 10 髋关节角度跟踪曲线

Fig. 10 Tracking curve of hip angle

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图 11 跟踪误差

Fig. 11 Tracking error

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表 1 人体运动意图识别结果

Table 1 Results of human motion intent recognition

被试者识别总数(个) 识别正确数(个) 识别率(%)

A 348 332 95.40

B 315 301 95.55

C 296 285 96.28

D 308 291 94.48

E 286 277 96.85

下载: 导出CSV

参考文献(14)

[1]	Lo A C, Guarino P D, Richards L G, Haselkorn J K, Wittenberg G F, Federman D G, Ringer R J, Wagner T H, Krebs H I, Volpe B T, Bever C T, Bravata D M, Duncan P W, Corn B H, Maffucci A D, Nadeau S E, Conroy S S, Powell J M, Huang G D, Peduzzi P. Robot-assisted therapy for long-term upper-limb impairment after stroke. New England Journal of Medicine, 2010, 362(19):1772-1783 doi: 10.1056/NEJMoa0911341
[2]	Klamroth-Marganska V, Blanco J, Campen K, Curt A, Dietz V, Ettlin T, Felder M, Fellinghauer B, Guidali M, Kollmar A, Luft A, Nef T, Schuster-Amft C, Stahel W, Riener R. Three-dimensional, task-specific robot therapy of the arm after stroke:a multicentre, parallel-group randomised trial. The Lancet Neurology, 2014, 13(2):159-166 doi: 10.1016/S1474-4422(13)70305-3
[3]	Meng W, Liu Q, Zhou Z D, Ai Q S, Sheng B, Xie S Q. Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation. Mechatronics, 2015, 31:132-145 doi: 10.1016/j.mechatronics.2015.04.005
[4]	彭亮, 侯增广, 王卫群.康复机器人的同步主动交互控制与实现.自动化学报, 2015, 41(11):1837-1846 http://www.aas.net.cn/CN/abstract/abstract18759.shtml Peng Liang, Hou Zeng-Guang, Wang Wei-Qun. Synchronous active interaction control and its implementation for a rehabilitation robot. Acta Automatica Sinica, 2015, 41(11):1837-1846 http://www.aas.net.cn/CN/abstract/abstract18759.shtml
[5]	佟丽娜, 侯增广, 彭亮, 王卫群, 陈翼雄, 谭民.基于多路sEMG时序分析的人体运动模式识别方法.自动化学报, 2014, 40(5):810-821 http://www.aas.net.cn/CN/abstract/abstract18349.shtml Tong Li-Na, Hou Zeng-Guang, Peng Liang, Wang Wei-Qun, Chen Yi-Xiong, Tan Min. Multi-channel sEMG time series analysis based human motion recognition method. Acta Automatica Sinica, 2014, 40(5):810-821 http://www.aas.net.cn/CN/abstract/abstract18349.shtml
[6]	Pittaccio S, Viscuso S. An EMG-controlled SMA device for the rehabilitation of the ankle joint in post-acute stroke. Journal of Materials Engineering and Performance, 2011, 20(4-5):666-670 doi: 10.1007/s11665-010-9826-7
[7]	Song R, Tong K Y, Hu X L, Zhou W. Myoelectrically controlled wrist robot for stroke rehabilitation. Journal of Neuroengineering and Rehabilitation, 2013, 10(1):Article No.52 doi: 10.1186/1743-0003-10-52
[8]	胡进, 侯增广, 陈翼雄, 张峰, 王卫群.下肢康复机器人及其交互控制方法.自动化学报, 2014, 40(11):2377-2390 http://www.aas.net.cn/CN/abstract/abstract18514.shtml Hu Jin, Hou Zeng-Guang, Chen Yi-Xiong, Zhang Feng, Wang Wei-Qun. Lower limb rehabilitation robots and interactive control methods. Acta Automatica Sinica, 2014, 40(11):2377-2390 http://www.aas.net.cn/CN/abstract/abstract18514.shtml
[9]	Saglia J A, Tsagarakis N G, Dai J S, Caldwell D G. Control strategies for patient-assisted training using the ankle rehabilitation robot (ARBOT). IEEE/ASME Transactions on Mechatronics, 2013, 18(6):1799-1808 doi: 10.1109/TMECH.2012.2214228
[10]	Duschau-Wicke A, Von Zitzewitz J, Caprez A, Lunenburger L, Riener R. Path control:a method for patient-cooperative robot-aided gait rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2010, 18(1):38-48 doi: 10.1109/TNSRE.2009.2033061
[11]	Koopman B, van Asseldonk E H F, van der Kooij H. Selective control of gait subtasks in robotic gait training:foot clearance support in stroke survivors with a powered exoskeleton. Journal of Neuroengineering and Rehabilitation, 2013, 10(1):Article No.3 doi: 10.1186/1743-0003-10-3
[12]	刘金琨.机器人控制系统的设计与MATLAB仿真.北京:清华大学出版社, 2008. 169-172 Liu Jin-Kun. Design and Simulation of Robot Control System Based on MATLAB. Beijing:Tsinghua University Press, 2008. 169-172
[13]	谢平, 宋妍, 郭子晖, 陈晓玲, 吴晓光, 苏玉萍, 杜义浩.中风康复运动中肌肉异常耦合分析.生物医学工程学杂志, 2016, 33(2):244-254 doi: 10.7507/1001-5515.20160043 Xie Ping, Song Yan, Guo Zi-Hui, Chen Xiao-Ling, Wu Xiao-Guang, Su Yu-Ping, Du Yi-Hao. Analysis of abnormal muscular coupling during rehabilitation after stroke. Journal of Biomedical Engineering, 2016, 33(2):244-254 doi: 10.7507/1001-5515.20160043
[14]	Hu J, Hou Z G, Peng L, Peng L, Gu N. sEMG-Based single-joint active training with iLeg-a horizontal exoskeleton for lower limb rehabilitation. In: Proceedings of the 21st International Conference. Kuching, Malaysia: Springer, 2014. 535-542

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下肢康复机器人的自适应人机交互控制策略

doi: 10.16383/j.aas.2017.c160128

通讯作者:
谢平燕山大学电气工程学院教授.2006年获得燕山大学燕山大学电路与系统专业工学博士学位.主要研究方向为脑机接口技术, 智能机器人控制, 虚拟康复技术.本文通信作者.E-mail:pingx@ysu.edu.cn

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Adaptive Interaction Control for Lower Limb Rehabilitation Robots

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目录

被试者	识别总数(个)	识别正确数(个)	识别率(%)
A	348	332	95.40
B	315	301	95.55
C	296	285	96.28
D	308	291	94.48
E	286	277	96.85

留言板

下肢康复机器人的自适应人机交互控制策略

doi: 10.16383/j.aas.2017.c160128

通讯作者: 谢平 燕山大学电气工程学院教授.2006年获得燕山大学燕山大学电路与系统专业工学博士学位.主要研究方向为脑机接口技术, 智能机器人控制, 虚拟康复技术.本文通信作者.E-mail:pingx@ysu.edu.cn

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出版历程

Adaptive Interaction Control for Lower Limb Rehabilitation Robots

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出版历程

目录

通讯作者:
谢平燕山大学电气工程学院教授.2006年获得燕山大学燕山大学电路与系统专业工学博士学位.主要研究方向为脑机接口技术, 智能机器人控制, 虚拟康复技术.本文通信作者.E-mail:pingx@ysu.edu.cn