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A Novel sEMG-Based Gait Phase-Kinematics-Coupled Predictor and Its Interaction With Exoskeletons

The interaction between human and exoskeletons increasingly relies on the precise decoding of human motion. One main issue of the current motion decoding algorithms is that seldom algorithms provide both discrete motion patterns (e.g., gait phases) and continuous motion parameters (e.g., kinematics)...

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Autores principales: Wei, Baichun, Ding, Zhen, Yi, Chunzhi, Guo, Hao, Wang, Zhipeng, Zhu, Jianfei, Jiang, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8384035/
https://www.ncbi.nlm.nih.gov/pubmed/34447302
http://dx.doi.org/10.3389/fnbot.2021.704226
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author Wei, Baichun
Ding, Zhen
Yi, Chunzhi
Guo, Hao
Wang, Zhipeng
Zhu, Jianfei
Jiang, Feng
author_facet Wei, Baichun
Ding, Zhen
Yi, Chunzhi
Guo, Hao
Wang, Zhipeng
Zhu, Jianfei
Jiang, Feng
author_sort Wei, Baichun
collection PubMed
description The interaction between human and exoskeletons increasingly relies on the precise decoding of human motion. One main issue of the current motion decoding algorithms is that seldom algorithms provide both discrete motion patterns (e.g., gait phases) and continuous motion parameters (e.g., kinematics). In this paper, we propose a novel algorithm that uses the surface electromyography (sEMG) signals that are generated prior to their corresponding motions to perform both gait phase recognition and lower-limb kinematics prediction. Particularly, we first propose an end-to-end architecture that uses the gait phase and EMG signals as the priori of the kinematics predictor. In so doing, the prediction of kinematics can be enhanced by the ahead-of-motion property of sEMG and quasi-periodicity of gait phases. Second, we propose to select the optimal muscle set and reduce the number of sensors according to the muscle effects in a gait cycle. Finally, we experimentally investigate how the assistance of exoskeletons can affect the motion intent predictor, and we propose a novel paradigm to make the predictor adapt to the change of data distribution caused by the exoskeleton assistance. The experiments on 10 subjects demonstrate the effectiveness of our algorithm and reveal the interaction between assistance and the kinematics predictor. This study would aid the design of exoskeleton-oriented motion-decoding and human–machine interaction methods.
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spelling pubmed-83840352021-08-25 A Novel sEMG-Based Gait Phase-Kinematics-Coupled Predictor and Its Interaction With Exoskeletons Wei, Baichun Ding, Zhen Yi, Chunzhi Guo, Hao Wang, Zhipeng Zhu, Jianfei Jiang, Feng Front Neurorobot Neuroscience The interaction between human and exoskeletons increasingly relies on the precise decoding of human motion. One main issue of the current motion decoding algorithms is that seldom algorithms provide both discrete motion patterns (e.g., gait phases) and continuous motion parameters (e.g., kinematics). In this paper, we propose a novel algorithm that uses the surface electromyography (sEMG) signals that are generated prior to their corresponding motions to perform both gait phase recognition and lower-limb kinematics prediction. Particularly, we first propose an end-to-end architecture that uses the gait phase and EMG signals as the priori of the kinematics predictor. In so doing, the prediction of kinematics can be enhanced by the ahead-of-motion property of sEMG and quasi-periodicity of gait phases. Second, we propose to select the optimal muscle set and reduce the number of sensors according to the muscle effects in a gait cycle. Finally, we experimentally investigate how the assistance of exoskeletons can affect the motion intent predictor, and we propose a novel paradigm to make the predictor adapt to the change of data distribution caused by the exoskeleton assistance. The experiments on 10 subjects demonstrate the effectiveness of our algorithm and reveal the interaction between assistance and the kinematics predictor. This study would aid the design of exoskeleton-oriented motion-decoding and human–machine interaction methods. Frontiers Media S.A. 2021-08-10 /pmc/articles/PMC8384035/ /pubmed/34447302 http://dx.doi.org/10.3389/fnbot.2021.704226 Text en Copyright © 2021 Wei, Ding, Yi, Guo, Wang, Zhu and Jiang. 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
Wei, Baichun
Ding, Zhen
Yi, Chunzhi
Guo, Hao
Wang, Zhipeng
Zhu, Jianfei
Jiang, Feng
A Novel sEMG-Based Gait Phase-Kinematics-Coupled Predictor and Its Interaction With Exoskeletons
title A Novel sEMG-Based Gait Phase-Kinematics-Coupled Predictor and Its Interaction With Exoskeletons
title_full A Novel sEMG-Based Gait Phase-Kinematics-Coupled Predictor and Its Interaction With Exoskeletons
title_fullStr A Novel sEMG-Based Gait Phase-Kinematics-Coupled Predictor and Its Interaction With Exoskeletons
title_full_unstemmed A Novel sEMG-Based Gait Phase-Kinematics-Coupled Predictor and Its Interaction With Exoskeletons
title_short A Novel sEMG-Based Gait Phase-Kinematics-Coupled Predictor and Its Interaction With Exoskeletons
title_sort novel semg-based gait phase-kinematics-coupled predictor and its interaction with exoskeletons
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8384035/
https://www.ncbi.nlm.nih.gov/pubmed/34447302
http://dx.doi.org/10.3389/fnbot.2021.704226
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