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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8076547/ https://www.ncbi.nlm.nih.gov/pubmed/33927589 http://dx.doi.org/10.3389/fnins.2021.645374 |
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author | Wang, Can Guo, Ziming Duan, Shengcai He, Bailin Yuan, Ye Wu, Xinyu |
author_facet | Wang, Can Guo, Ziming Duan, Shengcai He, Bailin Yuan, Ye Wu, Xinyu |
author_sort | Wang, Can |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-8076547 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80765472021-04-28 A Real-Time Stability Control Method Through sEMG Interface for Lower Extremity Rehabilitation Exoskeletons Wang, Can Guo, Ziming Duan, Shengcai He, Bailin Yuan, Ye Wu, Xinyu Front Neurosci Neuroscience 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. Frontiers Media S.A. 2021-04-13 /pmc/articles/PMC8076547/ /pubmed/33927589 http://dx.doi.org/10.3389/fnins.2021.645374 Text en Copyright © 2021 Wang, Guo, Duan, He, Yuan and Wu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Wang, Can Guo, Ziming Duan, Shengcai He, Bailin Yuan, Ye Wu, Xinyu A Real-Time Stability Control Method Through sEMG Interface for Lower Extremity Rehabilitation Exoskeletons |
title | 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_short | 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 |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8076547/ https://www.ncbi.nlm.nih.gov/pubmed/33927589 http://dx.doi.org/10.3389/fnins.2021.645374 |
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