A Multi-Channel Reinforcement Learning Framework for Robotic Mirror Therapy

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

4 Scopus Citations
View graph of relations

Author(s)

  • Linsen Xu
  • Gaoxin Cheng
  • Jia Shi
  • Jinfu Liu
  • Shouqi Chen

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number9134883
Pages (from-to)5385-5392
Journal / PublicationIEEE Robotics and Automation Letters
Volume5
Issue number4
Online published7 Jul 2020
Publication statusPublished - Oct 2020

Abstract

In the letter, a robotic framework is proposed for hemiparesis rehabilitation. Mirror therapy is applied to transfer therapeutic training from the patient's function limb (FL) to the impaired limb (IL). The IL mimics the action prescribed by the FL with the assistance of the wearable robot, stimulating and strengthening the injured muscles through repetitive exercise. A master-slave robotic system is presented to implement the mirror therapy. Especially, the reinforcement learning is involved in the human-robot interaction control to enhance the rehabilitation efficacy and guarantee safety. Multi-channel sensed information, including the motion trajectory, muscle activation and the user's emotion, are incorporated in the learning algorithm. The muscle activation is expressed via the skin surface electromyography (EMG) signals, and the emotion is shown as the facial expression. The reinforcement learning approach is realized by the normalized advantage functions (NAF) algorithm. Then, a lower extremity rehabilitation robot with magnetorheological (MR) actuators is specially developed. The clinical experiments are carried out using the robot to verify the performance of the framework.

Research Area(s)

  • physical human-robot interaction, Rehabilitation robotics, reinforcement learning

Citation Format(s)

A Multi-Channel Reinforcement Learning Framework for Robotic Mirror Therapy. / Xu, Jiajun; Xu, Linsen; Li, Youfu; Cheng, Gaoxin; Shi, Jia; Liu, Jinfu; Chen, Shouqi.

In: IEEE Robotics and Automation Letters, Vol. 5, No. 4, 9134883, 10.2020, p. 5385-5392.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review