A Real-Time Stability Control Method Through sEMG Interface for Lower Extremity Rehabilitation Exoskeletons

Herein, we propose a real-time stable control gait switching method for the exoskeleton rehabilitation robot. Exoskeleton rehabilitation robots have been extensively developed during the past decade and are able to offer valuable motor ability to paraplegics. However, achieving stable states of the...

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Main Authors: Can Wang, Ziming Guo, Shengcai Duan, Bailin He, Ye Yuan, Xinyu Wu
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-04-01
Series:Frontiers in Neuroscience
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fnins.2021.645374/full
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language English
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author Can Wang
Can Wang
Can Wang
Ziming Guo
Ziming Guo
Ziming Guo
Shengcai Duan
Shengcai Duan
Shengcai Duan
Bailin He
Bailin He
Bailin He
Ye Yuan
Ye Yuan
Ye Yuan
Xinyu Wu
Xinyu Wu
Xinyu Wu
Xinyu Wu
Xinyu Wu
spellingShingle Can Wang
Can Wang
Can Wang
Ziming Guo
Ziming Guo
Ziming Guo
Shengcai Duan
Shengcai Duan
Shengcai Duan
Bailin He
Bailin He
Bailin He
Ye Yuan
Ye Yuan
Ye Yuan
Xinyu Wu
Xinyu Wu
Xinyu Wu
Xinyu Wu
Xinyu Wu
A Real-Time Stability Control Method Through sEMG Interface for Lower Extremity Rehabilitation Exoskeletons
Frontiers in Neuroscience
rehabilitation exoskeleton robot
real-time motion stability
gait switch
surface electromyography
motion intention recognition
muscle fatigue
author_facet Can Wang
Can Wang
Can Wang
Ziming Guo
Ziming Guo
Ziming Guo
Shengcai Duan
Shengcai Duan
Shengcai Duan
Bailin He
Bailin He
Bailin He
Ye Yuan
Ye Yuan
Ye Yuan
Xinyu Wu
Xinyu Wu
Xinyu Wu
Xinyu Wu
Xinyu Wu
author_sort Can Wang
title A Real-Time Stability Control Method Through sEMG Interface for Lower Extremity Rehabilitation Exoskeletons
title_short A Real-Time Stability Control Method Through sEMG Interface for Lower Extremity Rehabilitation Exoskeletons
title_full A Real-Time Stability Control Method Through sEMG Interface for Lower Extremity Rehabilitation Exoskeletons
title_fullStr A Real-Time Stability Control Method Through sEMG Interface for Lower Extremity Rehabilitation Exoskeletons
title_full_unstemmed A Real-Time Stability Control Method Through sEMG Interface for Lower Extremity Rehabilitation Exoskeletons
title_sort real-time stability control method through semg interface for lower extremity rehabilitation exoskeletons
publisher Frontiers Media S.A.
series Frontiers in Neuroscience
issn 1662-453X
publishDate 2021-04-01
description Herein, we propose a real-time stable control gait switching method for the exoskeleton rehabilitation robot. Exoskeleton rehabilitation robots have been extensively developed during the past decade and are able to offer valuable motor ability to paraplegics. However, achieving stable states of the human-exoskeleton system while conserving wearer strength remains challenging. The constant switching of gaits during walking may affect the center of gravity, resulting in imbalance of human–exoskeleton system. In this study, it was determined that forming an equilateral triangle with two crutch-supporting points and a supporting leg has a positive impact on walking stability and ergonomic interaction. First, the gaits planning and stability analysis based on human kinematics model and zero moment point method for the lower limb exoskeleton are demonstrated. Second, a neural interface based on surface electromyography (sEMG), which realizes the intention recognition and muscle fatigue estimation, is constructed. Third, the stability of human–exoskeleton system and ergonomic effects are tested through different gaits with planned and unplanned gait switching strategy on the SIAT lower limb rehabilitation exoskeleton. The intention recognition based on long short-term memory (LSTM) model can achieve an accuracy of nearly 99%. The experimental results verified the feasibility and efficiency of the proposed gait switching method for enhancing stability and ergonomic effects of lower limb rehabilitation exoskeleton.
topic rehabilitation exoskeleton robot
real-time motion stability
gait switch
surface electromyography
motion intention recognition
muscle fatigue
url https://www.frontiersin.org/articles/10.3389/fnins.2021.645374/full
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spelling doaj-13b3761af19a460282810b20721e1d152021-04-13T05:06:31ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2021-04-011510.3389/fnins.2021.645374645374A Real-Time Stability Control Method Through sEMG Interface for Lower Extremity Rehabilitation ExoskeletonsCan Wang0Can Wang1Can Wang2Ziming Guo3Ziming Guo4Ziming Guo5Shengcai Duan6Shengcai Duan7Shengcai Duan8Bailin He9Bailin He10Bailin He11Ye Yuan12Ye Yuan13Ye Yuan14Xinyu Wu15Xinyu Wu16Xinyu Wu17Xinyu Wu18Xinyu Wu19Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaShenzhen Institute of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, ChinaCAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Shenzhen, ChinaGuangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaShenzhen Institute of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, ChinaCAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Shenzhen, ChinaGuangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaShenzhen Institute of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, ChinaCAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Shenzhen, ChinaGuangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaShenzhen Institute of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, ChinaCAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Shenzhen, ChinaGuangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaShenzhen Institute of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, ChinaCAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Shenzhen, ChinaGuangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaShenzhen Institute of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, ChinaCAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Shenzhen, ChinaGuangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaDepartment of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, Hong KongHerein, we propose a real-time stable control gait switching method for the exoskeleton rehabilitation robot. Exoskeleton rehabilitation robots have been extensively developed during the past decade and are able to offer valuable motor ability to paraplegics. However, achieving stable states of the human-exoskeleton system while conserving wearer strength remains challenging. The constant switching of gaits during walking may affect the center of gravity, resulting in imbalance of human–exoskeleton system. In this study, it was determined that forming an equilateral triangle with two crutch-supporting points and a supporting leg has a positive impact on walking stability and ergonomic interaction. First, the gaits planning and stability analysis based on human kinematics model and zero moment point method for the lower limb exoskeleton are demonstrated. Second, a neural interface based on surface electromyography (sEMG), which realizes the intention recognition and muscle fatigue estimation, is constructed. Third, the stability of human–exoskeleton system and ergonomic effects are tested through different gaits with planned and unplanned gait switching strategy on the SIAT lower limb rehabilitation exoskeleton. The intention recognition based on long short-term memory (LSTM) model can achieve an accuracy of nearly 99%. The experimental results verified the feasibility and efficiency of the proposed gait switching method for enhancing stability and ergonomic effects of lower limb rehabilitation exoskeleton.https://www.frontiersin.org/articles/10.3389/fnins.2021.645374/fullrehabilitation exoskeleton robotreal-time motion stabilitygait switchsurface electromyographymotion intention recognitionmuscle fatigue