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A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals

The use of upper limb rehabilitation robots to assist the affected limbs for active rehabilitation training is an inevitable trend in the field of rehabilitation medicine. In particular, the active motion intention-based control of the upper limb rehabilitation robots to assist subjects in rehabilit...

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Autores principales: Zhang, Bangcheng, Lan, Xuteng, Wang, Gang, Pang, Zaixiang, Zhang, Xiyu, Sun, Zhongbo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9669369/
https://www.ncbi.nlm.nih.gov/pubmed/36406950
http://dx.doi.org/10.3389/fnbot.2022.1047325
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author Zhang, Bangcheng
Lan, Xuteng
Wang, Gang
Pang, Zaixiang
Zhang, Xiyu
Sun, Zhongbo
author_facet Zhang, Bangcheng
Lan, Xuteng
Wang, Gang
Pang, Zaixiang
Zhang, Xiyu
Sun, Zhongbo
author_sort Zhang, Bangcheng
collection PubMed
description The use of upper limb rehabilitation robots to assist the affected limbs for active rehabilitation training is an inevitable trend in the field of rehabilitation medicine. In particular, the active motion intention-based control of the upper limb rehabilitation robots to assist subjects in rehabilitation training is a hot research topic in human-computer interaction control. Therefore, improving the accuracy of active motion intention recognition is the premise of the human-machine interaction controller design. Furthermore, there are external disturbances (bounded/unbounded disturbances) during rehabilitation training, which seriously threaten the safety of subjects. Thereby, eliminating external disturbances (especially unbounded disturbances) is the difficulty and key to the human-machine interaction control of the upper limb rehabilitation robots. In response to these problems, based on the surface electromyogram signal of the human upper limb, this paper proposes a fuzzy neural network active motion intention recognition method to explore the internal connection between the surface electromyogram signal of the human upper limb and active motion intention, and improve the real-time and accuracy of recognition. Based on this, two types of human-machine interaction controllers, which can be called as zeroing neural network controller and noise-suppressing zeroing neural network controller are designed to establish a safe and comfortable training environment to avoid secondary damage to the affected limb. Numerical experiments verify the feasibility and effectiveness of the proposed theories and methods.
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spelling pubmed-96693692022-11-18 A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals Zhang, Bangcheng Lan, Xuteng Wang, Gang Pang, Zaixiang Zhang, Xiyu Sun, Zhongbo Front Neurorobot Neuroscience The use of upper limb rehabilitation robots to assist the affected limbs for active rehabilitation training is an inevitable trend in the field of rehabilitation medicine. In particular, the active motion intention-based control of the upper limb rehabilitation robots to assist subjects in rehabilitation training is a hot research topic in human-computer interaction control. Therefore, improving the accuracy of active motion intention recognition is the premise of the human-machine interaction controller design. Furthermore, there are external disturbances (bounded/unbounded disturbances) during rehabilitation training, which seriously threaten the safety of subjects. Thereby, eliminating external disturbances (especially unbounded disturbances) is the difficulty and key to the human-machine interaction control of the upper limb rehabilitation robots. In response to these problems, based on the surface electromyogram signal of the human upper limb, this paper proposes a fuzzy neural network active motion intention recognition method to explore the internal connection between the surface electromyogram signal of the human upper limb and active motion intention, and improve the real-time and accuracy of recognition. Based on this, two types of human-machine interaction controllers, which can be called as zeroing neural network controller and noise-suppressing zeroing neural network controller are designed to establish a safe and comfortable training environment to avoid secondary damage to the affected limb. Numerical experiments verify the feasibility and effectiveness of the proposed theories and methods. Frontiers Media S.A. 2022-11-03 /pmc/articles/PMC9669369/ /pubmed/36406950 http://dx.doi.org/10.3389/fnbot.2022.1047325 Text en Copyright © 2022 Zhang, Lan, Wang, Pang, Zhang and Sun. 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
Zhang, Bangcheng
Lan, Xuteng
Wang, Gang
Pang, Zaixiang
Zhang, Xiyu
Sun, Zhongbo
A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals
title A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals
title_full A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals
title_fullStr A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals
title_full_unstemmed A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals
title_short A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals
title_sort noise-suppressing neural network approach for upper limb human-machine interactive control based on semg signals
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9669369/
https://www.ncbi.nlm.nih.gov/pubmed/36406950
http://dx.doi.org/10.3389/fnbot.2022.1047325
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