MCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram

The human–robot interface (HRI) based on biological signals can realize the natural interaction between human and robot. It has been widely used in exoskeleton robots recently to help predict the wearer's movement. Surface electromyography (sEMG)-based HRI has mature applications on the exoskel...

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Autores principales:	Kecheng Shi, Rui Huang, Zhinan Peng, Fengjun Mu, Xiao Yang
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	human-robot interface lower limb movement prediction channel synergy-based network exoskeleton paraplegic patients surface electromyography Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/44487bcd904141148649f2bebd0c64ea
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id	oai:doaj.org-article:44487bcd904141148649f2bebd0c64ea
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spelling	oai:doaj.org-article:44487bcd904141148649f2bebd0c64ea2021-11-17T14:33:00ZMCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram1662-453X10.3389/fnins.2021.704603https://doaj.org/article/44487bcd904141148649f2bebd0c64ea2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnins.2021.704603/fullhttps://doaj.org/toc/1662-453XThe human–robot interface (HRI) based on biological signals can realize the natural interaction between human and robot. It has been widely used in exoskeleton robots recently to help predict the wearer's movement. Surface electromyography (sEMG)-based HRI has mature applications on the exoskeleton. However, the sEMG signals of paraplegic patients' lower limbs are weak, which means that most HRI based on lower limb sEMG signals cannot be applied to the exoskeleton. Few studies have explored the possibility of using upper limb sEMG signals to predict lower limb movement. In addition, most HRIs do not consider the contribution and synergy of sEMG signal channels. This paper proposes a human–exoskeleton interface based on upper limb sEMG signals to predict lower limb movements of paraplegic patients. The interface constructs an channel synergy-based network (MCSNet) to extract the contribution and synergy of different feature channels. An sEMG data acquisition experiment is designed to verify the effectiveness of MCSNet. The experimental results show that our method has a good movement prediction performance in both within-subject and cross-subject situations, reaching an accuracy of 94.51 and 80.75%, respectively. Furthermore, feature visualization and model ablation analysis show that the features extracted by MCSNet are physiologically interpretable.Kecheng ShiKecheng ShiKecheng ShiRui HuangRui HuangRui HuangZhinan PengZhinan PengZhinan PengFengjun MuFengjun MuXiao YangFrontiers Media S.A.articlehuman-robot interfacelower limb movement predictionchannel synergy-based networkexoskeletonparaplegic patientssurface electromyographyNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	human-robot interface lower limb movement prediction channel synergy-based network exoskeleton paraplegic patients surface electromyography Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	human-robot interface lower limb movement prediction channel synergy-based network exoskeleton paraplegic patients surface electromyography Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Kecheng Shi Kecheng Shi Kecheng Shi Rui Huang Rui Huang Rui Huang Zhinan Peng Zhinan Peng Zhinan Peng Fengjun Mu Fengjun Mu Xiao Yang MCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram
description	The human–robot interface (HRI) based on biological signals can realize the natural interaction between human and robot. It has been widely used in exoskeleton robots recently to help predict the wearer's movement. Surface electromyography (sEMG)-based HRI has mature applications on the exoskeleton. However, the sEMG signals of paraplegic patients' lower limbs are weak, which means that most HRI based on lower limb sEMG signals cannot be applied to the exoskeleton. Few studies have explored the possibility of using upper limb sEMG signals to predict lower limb movement. In addition, most HRIs do not consider the contribution and synergy of sEMG signal channels. This paper proposes a human–exoskeleton interface based on upper limb sEMG signals to predict lower limb movements of paraplegic patients. The interface constructs an channel synergy-based network (MCSNet) to extract the contribution and synergy of different feature channels. An sEMG data acquisition experiment is designed to verify the effectiveness of MCSNet. The experimental results show that our method has a good movement prediction performance in both within-subject and cross-subject situations, reaching an accuracy of 94.51 and 80.75%, respectively. Furthermore, feature visualization and model ablation analysis show that the features extracted by MCSNet are physiologically interpretable.
format	article
author	Kecheng Shi Kecheng Shi Kecheng Shi Rui Huang Rui Huang Rui Huang Zhinan Peng Zhinan Peng Zhinan Peng Fengjun Mu Fengjun Mu Xiao Yang
author_facet	Kecheng Shi Kecheng Shi Kecheng Shi Rui Huang Rui Huang Rui Huang Zhinan Peng Zhinan Peng Zhinan Peng Fengjun Mu Fengjun Mu Xiao Yang
author_sort	Kecheng Shi
title	MCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram
title_short	MCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram
title_full	MCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram
title_fullStr	MCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram
title_full_unstemmed	MCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram
title_sort	mcsnet: channel synergy-based human-exoskeleton interface with surface electromyogram
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/44487bcd904141148649f2bebd0c64ea
work_keys_str_mv	AT kechengshi mcsnetchannelsynergybasedhumanexoskeletoninterfacewithsurfaceelectromyogram AT kechengshi mcsnetchannelsynergybasedhumanexoskeletoninterfacewithsurfaceelectromyogram AT kechengshi mcsnetchannelsynergybasedhumanexoskeletoninterfacewithsurfaceelectromyogram AT ruihuang mcsnetchannelsynergybasedhumanexoskeletoninterfacewithsurfaceelectromyogram AT ruihuang mcsnetchannelsynergybasedhumanexoskeletoninterfacewithsurfaceelectromyogram AT ruihuang mcsnetchannelsynergybasedhumanexoskeletoninterfacewithsurfaceelectromyogram AT zhinanpeng mcsnetchannelsynergybasedhumanexoskeletoninterfacewithsurfaceelectromyogram AT zhinanpeng mcsnetchannelsynergybasedhumanexoskeletoninterfacewithsurfaceelectromyogram AT zhinanpeng mcsnetchannelsynergybasedhumanexoskeletoninterfacewithsurfaceelectromyogram AT fengjunmu mcsnetchannelsynergybasedhumanexoskeletoninterfacewithsurfaceelectromyogram AT fengjunmu mcsnetchannelsynergybasedhumanexoskeletoninterfacewithsurfaceelectromyogram AT xiaoyang mcsnetchannelsynergybasedhumanexoskeletoninterfacewithsurfaceelectromyogram
_version_	1718425410830598144

MCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram

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