Yang Ziyi, Guo Shuxiang, Hirata Hideyuki, Kawanishi Masahiko
Graduate School of Engineering, Kagawa University, Takamatsu 761-0396, Japan.
Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, The Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China.
Life (Basel). 2021 Nov 24;11(12):1290. doi: 10.3390/life11121290.
In this paper, a novel mirror visual feedback-based (MVF) bilateral neurorehabilitation system with surface electromyography (sEMG)-based patient active force assessment was proposed for upper limb motor recovery and improvement of limb inter-coordination. A mirror visual feedback-based human-robot interface was designed to facilitate the bilateral isometric force output training task. To achieve patient active participant assessment, an sEMG signals-based elbow joint isometric force estimation method was implemented into the proposed system for real-time affected side force assessment and participation evaluation. To assist the affected side limb efficiently and precisely, a mirror bilateral control framework was presented for bilateral limb coordination. Preliminary experiments were conducted to evaluate the estimation accuracy of force estimation method and force tracking accuracy of system performance. The experimental results show the proposed force estimation method can efficiently calculate the elbow joint force in real-time, and the affected side limb of patients can be assisted to track output force of the non-paretic side limb for better limb coordination by the proposed bilateral rehabilitation system.
本文提出了一种基于镜像视觉反馈（MVF）的双边神经康复系统，该系统基于表面肌电图（sEMG）进行患者主动力评估，用于上肢运动恢复和肢体间协调性的改善。设计了一种基于镜像视觉反馈的人机接口，以促进双边等长力输出训练任务。为了实现患者主动参与评估，将基于sEMG信号的肘关节等长力估计方法应用于所提出的系统中，用于实时患侧力评估和参与度评估。为了高效、精确地辅助患侧肢体，提出了一种镜像双边控制框架用于双边肢体协调。进行了初步实验，以评估力估计方法的估计精度和系统性能的力跟踪精度。实验结果表明，所提出的力估计方法能够实时有效地计算肘关节力，并且所提出的双边康复系统能够辅助患者患侧肢体跟踪健侧肢体的输出力，以实现更好的肢体协调性。
