• CN:11-2187/TH
  • ISSN:0577-6686

›› 2008, Vol. 44 ›› Issue (9): 169-176.

• 论文 • 上一篇    下一篇

5-DOF上肢康复机械臂交互式康复训练控制策略

李庆玲;孔民秀;杜志江;孙立宁;王东岩   

  1. 哈尔滨工业大学机器人技术与系统国家重点实验室;黑龙江中医药大学附属第二医院
  • 发布日期:2008-09-15

Interactive Rehabilitation Exercise Control Strategy for 5-DOF Upper Limb Rehabilitation Arm

LI Qingling;KONG Minxiu;DU Zhijiang;SUN Lining;WANG Dongyan   

  1. State Key Laboratory of Robotics and System, Harbin Institute of Technology The Second Affiliated Hospital, Heilongjiang University of Chinese Medicine
  • Published:2008-09-15

摘要: 设计一种5-DOF穿戴式偏瘫上肢康复机械臂系统,提出被动和主动两阶段的交互式康复训练控制策略。在被动训练中,根据偏瘫患者上肢单侧受损的特点,提取偏瘫患者的健侧上肢4块肌肉的表面肌电信号(Surface electromyogram, sEMG) 作为康复机械臂的控制信号,利用AR参数模型和BP神经网络来理解患者的运动意图,驱动机械臂辅助患侧上肢实现特定的康复训练动作。在主动训练中,通过实时获取各关节力矩信号来判断人体上肢的运动所产生的作用力方向与大小,并利用比例控制器和运动学雅可比逆矩阵控制康复臂末端速度、驱动各关节运动。试验结果证明了提出方法的正确性和有效性。

关键词: AR模型, BP神经网络, 表面肌电信号, 康复策略, 康复机械臂, 力辅助控制

Abstract: A 5-DOF exoskeletal rehabilitation arm for hemiplegic upper limbs is developed with passive and active interactive exercise control strategy. In passive exercise, surface electromyogram (sEMG) of hemiplegic patients’ healthy limbs are extracted to control the arm because they are impaired unilaterally in general. AR model and BP neural network are used to understand the patient’s motion intention in order to actuate the arm. Then, it can assist disabled arm to implement preprogrammed motions. In active exercise, the torque of each joint is used to estimate the force caused by limbs’ movement in real-time. Terminal velocity is controlled with proportional controller and Jacobian inverse matrix to drive each joint. Test results prove the correctness and effectiveness of the proposed method.

Key words: AR model, BP neural network, Force assistant control, Rehabilitation arm, Rehabilitation strategy, Surface electromyogram

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