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.
传统镜像训练中，患肢难以真实运动，通过康复机器人可对患肢实现真实的物理镜像。在此训练过程中，患肢通常处于被动状态。而随着患肢运动能力的逐渐恢复，主动镜像训练则成为更好的选择。为此，本文采用一款自制的结构可调的肩关节康复机器人作为实验平台，提出一种由健肢惯性测量单元（IMU）轨迹获取、闭环变论域模糊自适应比例微分（PD）控制、患肢表面肌电信号（sEMG）肌力估计和外环患肢助力补偿四个环节组成的镜像主动康复训练系统。实验结果表明，此控制系统可根据患肢的恢复情况实时提供助力补偿，充分发挥了患肢的训练主动性，使患肢达到较好的康复训练效果。.
Keywords: adjustable assistance; mirror training; rehabilitation robot; shoulder joint; surface electromyography.