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Online gait learning with Assist-As-Needed control strategy for post-stroke rehabilitation exoskeletons

Published online by Cambridge University Press:  28 November 2023

Chaobin Zou ,
Chao Zeng ,
Rui Huang ,
Zhinan Peng Open the ORCID record for Zhinan Peng [Opens in a new window] ,
Jianwei Zhang  and
Hong Cheng
Chaobin Zou
Affiliation:
Center for Robotics, School of Automation and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
Chao Zeng
Affiliation:
Technical Aspects of Multimodal Systems, Department of Informatics, Universitat Hamburg, Hamburg, Germany
Rui Huang
Affiliation:
Center for Robotics, School of Automation and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
Zhinan Peng*
Affiliation:
Center for Robotics, School of Automation and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China Institute of Electronic and Information Engineering of University of Electronic Science and Technology of China in Guangdong, Dongguan, 523808, China
Jianwei Zhang
Affiliation:
Technical Aspects of Multimodal Systems, Department of Informatics, Universitat Hamburg, Hamburg, Germany
Hong Cheng
Affiliation:
Center for Robotics, School of Automation and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
*
Corresponding author: Zhinan Peng; Email: zhinanpeng@uestc.edu.cn
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Abstract

Lower limb exoskeletons (LLEs) have demonstrated their potential in delivering quantified repetitive gait training for individuals afflicted with gait impairments. A critical concern in robotic gait training pertains to fostering active patient engagement, and a viable solution entails harnessing the patient’s intrinsic effort to govern the control of LLEs. To address these challenges, this study presents an innovative online gait learning approach with an appropriate control strategy for rehabilitation exoskeletons based on dynamic movement primitives (DMP) and an Assist-As-Needed (AAN) control strategy, denoted as DMP-AAN. Specifically tailored for post-stroke patients, this approach aims to acquire the gait trajectory from the unaffected leg and subsequently generate the reference gait trajectory for the affected leg, leveraging the acquired model and the patient’s personal exertion. Compared to conventional AAN methodologies, the proposed DMP-AAN approach exhibits adaptability to diverse scenarios encompassing varying gait patterns. Experimental validation has been performed using the lower limb rehabilitation exoskeleton HemiGo. The findings highlight the ability to generate suitable control efforts for LLEs with reduced human-robot interactive force, thereby enabling highly patient-controlled gait training sessions to be achieved.

Keywords

exoskeletonsgait learningdynamic movement primitivesAssist-As-Needed

Information

Type
Research Article
Information
Robotica , Volume 42 , Issue 2 , February 2024 , pp. 319 - 331
Copyright
© The Author(s), 2023. Published by Cambridge University Press.

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