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A robotic system with reinforcement learning for lower extremity hemiparesis rehabilitation

Jiajun Xu (University of Science and Technology of China, Hefei, China and Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China)
Linsen Xu (Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, China)
Gaoxin Cheng (Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Changzhou, China and University of Science and Technology of China, Hefei, China)
Jia Shi (University of Science and Technology of China, Hefei, China)
Jinfu Liu (School of Automation, Southeast University, Nanjing, China)
Xingcan Liang (Hefei Institutes of Physical Science, Hefei, China and University of Science and Technology of China, Hefei, China)
Shengyao Fan (Wuxi Institute of Technology, Wuxi, China)

Industrial Robot

ISSN: 0143-991x

Article publication date: 8 February 2021

Issue publication date: 3 August 2021

184

Abstract

Purpose

This paper aims to propose a bilateral robotic system for lower extremity hemiparesis rehabilitation. The hemiplegic patients can complete rehabilitation exercise voluntarily with the assistance of the robot. The reinforcement learning is included in the robot control system, enhancing the muscle activation of the impaired limbs (ILs) efficiently with ensuring the patients’ safety.

Design/methodology/approach

A bilateral leader–follower robotic system is constructed for lower extremity hemiparesis rehabilitation, where the leader robot interacts with the healthy limb (HL) and the follow robot is worn by the IL. The therapeutic training is transferred from the HL to the IL with the assistance of the robot, and the IL follows the motion trajectory prescribed by the HL, which is called the mirror therapy. The model reference adaptive impedance control is used for the leader robot, and the reinforcement learning controller is designed for the follower robot. The reinforcement learning aims to increase the muscle activation of the IL and ensure that its motion can be mastered by the HL for safety. An asynchronous algorithm is designed by improving experience relay to run in parallel on multiple robotic platforms to reduce learning time.

Findings

Through clinical tests, the lower extremity hemiplegic patients can rehabilitate with high efficiency using the robotic system. Also, the proposed scheme outperforms other state-of-the-art methods in tracking performance, muscle activation, learning efficiency and rehabilitation efficacy.

Originality/value

Using the aimed robotic system, the lower extremity hemiplegic patients with different movement abilities can obtain better rehabilitation efficacy.

Keywords

Acknowledgements

Funding: (1) National Key Research and Development Plan (Grant No.: 2017YFB1303200); and (2) Natural Science Foundation of Jiangsu Higher Education Institution of Jiangsu China (Grant No.: 17KJB460014).

Citation

Xu, J., Xu, L., Cheng, G., Shi, J., Liu, J., Liang, X. and Fan, S. (2021), "A robotic system with reinforcement learning for lower extremity hemiparesis rehabilitation", Industrial Robot, Vol. 48 No. 3, pp. 388-400. https://doi.org/10.1108/IR-10-2020-0230

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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