Chen Sheng, Yan Yizhe, Xu Guozheng, Gao Xiang, Huang Kangjin, Tai Chun
Institute of Robot Information Perception and Control, Nanjing University of Posts and Telecommunications, Nanjing 210023, P.R.China.
Provincial Key Laboratory of Precision and Micro Manufacturing Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2021 Apr 25;38(2):351-360. doi: 10.7507/1001-5515.202001053.
The real physical image of the affected limb, which is difficult to move in the traditional mirror training, can be realized easily by the rehabilitation robots. During this training, the affected limb is often in a passive state. However, with the gradual recovery of the movement ability, active mirror training becomes a better choice. Consequently, this paper took the self-developed shoulder joint rehabilitation robot with an adjustable structure as an experimental platform, and proposed a mirror training system completed by next four parts. First, the motion trajectory of the healthy limb was obtained by the Inertial Measurement Units (IMU). Then the variable universe fuzzy adaptive proportion differentiation (PD) control was adopted for inner loop, meanwhile, the muscle strength of the affected limb was estimated by the surface electromyography (sEMG). The compensation force for an assisted limb of outer loop was calculated. According to the experimental results, the control system can provide real-time assistance compensation according to the recovery of the affected limb, fully exert the training initiative of the affected limb, and make the affected limb achieve better rehabilitation training effect.
在传统镜像训练中难以移动的患肢真实物理图像，康复机器人能够轻松实现。在这种训练过程中，患肢通常处于被动状态。然而，随着运动能力的逐渐恢复，主动镜像训练成为更好的选择。因此，本文以自主研发的结构可调的肩关节康复机器人为实验平台，提出了一个由以下四个部分组成的镜像训练系统。首先，通过惯性测量单元（IMU）获取健侧肢体的运动轨迹。然后，内环采用变论域模糊自适应比例微分（PD）控制，同时，通过表面肌电图（sEMG）估计患肢的肌肉力量。计算外环辅助肢体的补偿力。根据实验结果，该控制系统能够根据患肢的恢复情况提供实时辅助补偿，充分发挥患肢的训练主动性，使患肢获得更好的康复训练效果。
