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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

杜义浩, 邱石, 谢平, 郭子晖, 吴晓光, 李小俚. 下肢康复机器人的自适应人机交互控制策略. 自动化学报, 2018, 44(4): 743-750. doi: 10.16383/j.aas.2017.c160128
引用本文: 杜义浩, 邱石, 谢平, 郭子晖, 吴晓光, 李小俚. 下肢康复机器人的自适应人机交互控制策略. 自动化学报, 2018, 44(4): 743-750. doi: 10.16383/j.aas.2017.c160128
DU Yi-Hao, QIU Shi, XIE Ping, GUO Zi-Hui, WU Xiao-Guang, LI Xiao-Li. Adaptive Interaction Control for Lower Limb Rehabilitation Robots. ACTA AUTOMATICA SINICA, 2018, 44(4): 743-750. doi: 10.16383/j.aas.2017.c160128
Citation: DU Yi-Hao, QIU Shi, XIE Ping, GUO Zi-Hui, WU Xiao-Guang, LI Xiao-Li. Adaptive Interaction Control for Lower Limb Rehabilitation Robots. ACTA AUTOMATICA SINICA, 2018, 44(4): 743-750. 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
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

Adaptive Interaction Control for Lower Limb Rehabilitation Robots

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: 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.
    1)  本文责任编委 王卫群
  • 图  1  人机系统机构

    Fig.  1  Human-machine system

    图  2  sEMG预处理过程

    Fig.  2  sEMG preprocessing process

    图  3  实验过程

    Fig.  3  Experimental process

    图  4  原始sEMG

    Fig.  4  Original sEMG

    图  5  归一化后sEMG

    Fig.  5  Normalized sEMG

    图  6  sEMG包络提取

    Fig.  6  sEMG envelope extraction

    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

    图  8  人机交互力和运动意图

    Fig.  8  Human-machine interaction and motion intention

    图  9  下肢康复机器人运动速度

    Fig.  9  Movement speed of lower limb rehabilitation robot

    图  10  髋关节角度跟踪曲线

    Fig.  10  Tracking curve of hip angle

    图  11  跟踪误差

    Fig.  11  Tracking error

    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(%)
    A34833295.40
    B31530195.55
    C29628596.28
    D30829194.48
    E28627796.85
    下载: 导出CSV
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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

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