College of Electrical Engineering, Guangxi University, Nanning Guangxi, China.
CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen, China.
PLoS One. 2020 Aug 27;15(8):e0238247. doi: 10.1371/journal.pone.0238247. eCollection 2020.
Switching different gait according to different movements is an important direction in the study of exoskeleton robot. Identifying the movement intention of the wearer to control the gait planning of the exoskeleton robot can effectively improve the man-machine interaction experience after the exoskeleton. This paper uses a support vector machine (SVM) to realize wearer's motion posture recognition by collecting sEMG signals on the human surface. The moving gait of the exoskeleton is planned according to the recognition results, and the decoding intention signal controls gait switching. Meanwhile, the stability of the planned gait during the movement was analyzed. Experimental results show that the sEMG signal decoding human motion intentional, and control exoskeleton robot gait switching has good accuracy and real-time performance. It helps patients to complete rehabilitation training more safely and quickly.
根据不同的动作切换不同的步态是外骨骼机器人研究的一个重要方向。通过采集人体表面肌电信号，利用支持向量机（SVM）对佩戴者的运动姿势进行识别，从而控制外骨骼机器人的步态规划，可有效提升外骨骼后的人机交互体验。本文对外骨骼的运动步态进行规划，根据识别结果进行解码意图信号控制步态切换，同时分析规划步态在运动过程中的稳定性。实验结果表明，利用 sEMG 信号解码人体运动意图，控制外骨骼机器人步态切换具有较好的准确性和实时性，有助于患者更安全、快速地完成康复训练。
