PDF(Pubmed)
Abstract：
Upper-limb paralysis requires extensive rehabilitation to recover functionality for everyday living, and such assistance can be supported with robot technology. Against such a background, we have proposed an electromyography (EMG)-driven hybrid rehabilitation system based on motion estimation using a probabilistic neural network. The system controls a robot and functional electrical stimulation (FES) from movement estimation using EMG signals based on the user\'s intention, enabling intuitive learning of joint motion and muscle contraction capacity even for multiple motions. In this study, hybrid and visual-feedback training were conducted with pointing movements involving the non-dominant wrist, and the motor learning effect was examined via quantitative evaluation of accuracy, stability, and smoothness. The results show that hybrid instruction was as effective as visual feedback training in all aspects. Accordingly, passive hybrid instruction using the proposed system can be considered effective in promoting motor learning and rehabilitation for paralysis with inability to perform voluntary movements.
摘要：
上肢瘫痪需要广泛的康复才能恢复日常生活的功能，机器人技术可以支持这种援助。在这样的背景下,我们提出了一种肌电图（EMG）驱动的混合康复系统，该系统基于使用概率神经网络的运动估计。该系统控制机器人和功能性电刺激（FES）的运动估计使用EMG信号根据用户的意图，使关节运动和肌肉收缩能力的直观学习，即使对于多个运动。在这项研究中,混合和视觉反馈训练是通过涉及非优势手腕的指向运动进行的，并通过对准确性的定量评估来检查运动学习效果，稳定性,和平滑度。结果表明，混合教学在各个方面都与视觉反馈训练一样有效。因此，使用所提出的系统的被动混合指令可以被认为是有效的促进运动学习和康复的瘫痪，无法进行自愿运动。
