A Multiposture Robot for Full Cycle Rehabilitation of Lower Limbs: Design and Autonomous Training

Previous rehabilitation robots were usually designed for certain stages, which causes relatively low rehabilitation efficiency. In this study, a multiposture robot was designed for full cycle rehabilitation training for the patients with lower limb disfunctions. Functions of the typical rehabilitati...

Full description

Saved in:
Bibliographic Details
Published in:IEEE/ASME transactions on mechatronics Vol. 29; no. 6; pp. 4087 - 4098
Main Authors: Wang, Weiqun, Shi, Weiguo, Xiang, Kexin, Ren, Shixin, Lin, Tianyu, Liu, Shengda, Liang, Xu, Wang, Jiaxing, Hou, Zeng-Guang
Format: Journal Article
Language:English
Published: IEEE 01.12.2024
Subjects:
Assistive robots
Autonomous training based on surface electromyography (sEMG)
Hip
Legged locomotion
mechanism design and optimization
Optimization
rehabilitation robot
Robots
Training
training trajectory optimization
Trajectory
ISSN:1083-4435, 1941-014X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Previous rehabilitation robots were usually designed for certain stages, which causes relatively low rehabilitation efficiency. In this study, a multiposture robot was designed for full cycle rehabilitation training for the patients with lower limb disfunctions. Functions of the typical rehabilitation equipments, including the rehabilitation bicycles, the standing beds for the orthostatic hypotension, and the gait trainers, were realized on the robot. Firstly, in order to implement training in the sitting, lying, and standing postures, a slider-pulley-chute mechanism was designed to obtain zero displacement deviation during the backrest adjustment. Then, the biomimetic gait trajectories were designed based on cooperative control of the leg mechanisms, the center of gravity (CoG), and the body weight supporting system; meanwhile, the key points of CoG trajectories for ascending or descending steps were deliberately designed and the suitable CoG trajectories were regenerated using a fifth-order polynomial, based on which continuously implement of ascending or descending steps on the robot was realized. Moreover, sEMG based motion intention recognition paradigms for variable velocity cycling and multi-mode walking were designed and the associated decoders were developed by combined using the support vector machine and stepwise linear regression algorithms and the minimal redundancy maximal relevance criterion. Finally, the autonomous cycling and multi-mode walking training was successfully realized based on recognizing in real time the subjects' intentions for adjustment of cycling velocities or walking modes. The feasibility of the proposed methods was validated based on simulation and real implement of the sEMG based autonomous cycling and multi-mode walking.
ISSN:1083-4435
1941-014X
DOI:10.1109/TMECH.2024.3362381