Underwater sEMG-based Recognition of Hand Gestures using Tensor Decomposition

Autores
Xue, Jianing; Sun, Zhe; Duan, Feng; Caiafa, César Federico; Solé Casals, Jordi
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Amputees have limited ability to complete specific movements because of the loss of hands. Prosthetic hands can help amputees as an effective human-computer interaction system in their daily lives, and some amputees need to use the prosthetic hands for underwater operations. Therefore, it is necessary to solve the problem of using prosthetic hands underwater. There are two main problems in underwater surface Electromyogram (sEMG) signal recognition. The underwater sEMG signals are disturbed by noise, and the traditional sEMG features are easily affected by noise, decreasing the recognition accuracy of underwater sEMG signals. It is difficult for subjects to acquire quantity training data underwater, and satisfactory sEMG recognition accuracy needs to be obtained based on small datasets. Tensor decomposition has the advantage of finding potential features of signals, and it is widely used in many fields. Tucker tensor decomposition was used for feature extraction and recognition of underwater sEMG signals. Seven subjects were selected to complete four hand gestures underwater and two-channel sEMG signals were collected. Wavelet transform was applied to generate a three-dimensional tensor and extracted signal features by tensor decomposition. The recognition accuracy based on K-Nearest Neighbor reaches 96.43%. The results show that the proposed sEMG feature extraction method based on tensor decomposition helps improve the recognition accuracy of underwater sEMG signals, which provides a basis for applying prosthetic hands in a water environment.
Fil: Xue, Jianing. Nankai University; China
Fil: Sun, Zhe. Riken. Labsp; Japón
Fil: Duan, Feng. Nankai University; China
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina
Fil: Solé Casals, Jordi. University of Vic-Central University of Catalonia; España
Materia
Hand gesture recognition
sEMG signals
tensor decomposition
underwater signal acquisition
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/218141

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spelling Underwater sEMG-based Recognition of Hand Gestures using Tensor DecompositionXue, JianingSun, ZheDuan, FengCaiafa, César FedericoSolé Casals, JordiHand gesture recognitionsEMG signalstensor decompositionunderwater signal acquisitionhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Amputees have limited ability to complete specific movements because of the loss of hands. Prosthetic hands can help amputees as an effective human-computer interaction system in their daily lives, and some amputees need to use the prosthetic hands for underwater operations. Therefore, it is necessary to solve the problem of using prosthetic hands underwater. There are two main problems in underwater surface Electromyogram (sEMG) signal recognition. The underwater sEMG signals are disturbed by noise, and the traditional sEMG features are easily affected by noise, decreasing the recognition accuracy of underwater sEMG signals. It is difficult for subjects to acquire quantity training data underwater, and satisfactory sEMG recognition accuracy needs to be obtained based on small datasets. Tensor decomposition has the advantage of finding potential features of signals, and it is widely used in many fields. Tucker tensor decomposition was used for feature extraction and recognition of underwater sEMG signals. Seven subjects were selected to complete four hand gestures underwater and two-channel sEMG signals were collected. Wavelet transform was applied to generate a three-dimensional tensor and extracted signal features by tensor decomposition. The recognition accuracy based on K-Nearest Neighbor reaches 96.43%. The results show that the proposed sEMG feature extraction method based on tensor decomposition helps improve the recognition accuracy of underwater sEMG signals, which provides a basis for applying prosthetic hands in a water environment.Fil: Xue, Jianing. Nankai University; ChinaFil: Sun, Zhe. Riken. Labsp; JapónFil: Duan, Feng. Nankai University; ChinaFil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Solé Casals, Jordi. University of Vic-Central University of Catalonia; EspañaElsevier Science2023-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/218141Xue, Jianing; Sun, Zhe; Duan, Feng; Caiafa, César Federico; Solé Casals, Jordi; Underwater sEMG-based Recognition of Hand Gestures using Tensor Decomposition; Elsevier Science; Pattern Recognition Letters; 165; 1-2023; 39-460167-8655CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0167865522003518info:eu-repo/semantics/altIdentifier/doi/10.1016/j.patrec.2022.11.021info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:50:52Zoai:ri.conicet.gov.ar:11336/218141instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:50:52.872CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Underwater sEMG-based Recognition of Hand Gestures using Tensor Decomposition
title Underwater sEMG-based Recognition of Hand Gestures using Tensor Decomposition
spellingShingle Underwater sEMG-based Recognition of Hand Gestures using Tensor Decomposition
Xue, Jianing
Hand gesture recognition
sEMG signals
tensor decomposition
underwater signal acquisition
title_short Underwater sEMG-based Recognition of Hand Gestures using Tensor Decomposition
title_full Underwater sEMG-based Recognition of Hand Gestures using Tensor Decomposition
title_fullStr Underwater sEMG-based Recognition of Hand Gestures using Tensor Decomposition
title_full_unstemmed Underwater sEMG-based Recognition of Hand Gestures using Tensor Decomposition
title_sort Underwater sEMG-based Recognition of Hand Gestures using Tensor Decomposition
dc.creator.none.fl_str_mv Xue, Jianing
Sun, Zhe
Duan, Feng
Caiafa, César Federico
Solé Casals, Jordi
author Xue, Jianing
author_facet Xue, Jianing
Sun, Zhe
Duan, Feng
Caiafa, César Federico
Solé Casals, Jordi
author_role author
author2 Sun, Zhe
Duan, Feng
Caiafa, César Federico
Solé Casals, Jordi
author2_role author
author
author
author
dc.subject.none.fl_str_mv Hand gesture recognition
sEMG signals
tensor decomposition
underwater signal acquisition
topic Hand gesture recognition
sEMG signals
tensor decomposition
underwater signal acquisition
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Amputees have limited ability to complete specific movements because of the loss of hands. Prosthetic hands can help amputees as an effective human-computer interaction system in their daily lives, and some amputees need to use the prosthetic hands for underwater operations. Therefore, it is necessary to solve the problem of using prosthetic hands underwater. There are two main problems in underwater surface Electromyogram (sEMG) signal recognition. The underwater sEMG signals are disturbed by noise, and the traditional sEMG features are easily affected by noise, decreasing the recognition accuracy of underwater sEMG signals. It is difficult for subjects to acquire quantity training data underwater, and satisfactory sEMG recognition accuracy needs to be obtained based on small datasets. Tensor decomposition has the advantage of finding potential features of signals, and it is widely used in many fields. Tucker tensor decomposition was used for feature extraction and recognition of underwater sEMG signals. Seven subjects were selected to complete four hand gestures underwater and two-channel sEMG signals were collected. Wavelet transform was applied to generate a three-dimensional tensor and extracted signal features by tensor decomposition. The recognition accuracy based on K-Nearest Neighbor reaches 96.43%. The results show that the proposed sEMG feature extraction method based on tensor decomposition helps improve the recognition accuracy of underwater sEMG signals, which provides a basis for applying prosthetic hands in a water environment.
Fil: Xue, Jianing. Nankai University; China
Fil: Sun, Zhe. Riken. Labsp; Japón
Fil: Duan, Feng. Nankai University; China
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina
Fil: Solé Casals, Jordi. University of Vic-Central University of Catalonia; España
description Amputees have limited ability to complete specific movements because of the loss of hands. Prosthetic hands can help amputees as an effective human-computer interaction system in their daily lives, and some amputees need to use the prosthetic hands for underwater operations. Therefore, it is necessary to solve the problem of using prosthetic hands underwater. There are two main problems in underwater surface Electromyogram (sEMG) signal recognition. The underwater sEMG signals are disturbed by noise, and the traditional sEMG features are easily affected by noise, decreasing the recognition accuracy of underwater sEMG signals. It is difficult for subjects to acquire quantity training data underwater, and satisfactory sEMG recognition accuracy needs to be obtained based on small datasets. Tensor decomposition has the advantage of finding potential features of signals, and it is widely used in many fields. Tucker tensor decomposition was used for feature extraction and recognition of underwater sEMG signals. Seven subjects were selected to complete four hand gestures underwater and two-channel sEMG signals were collected. Wavelet transform was applied to generate a three-dimensional tensor and extracted signal features by tensor decomposition. The recognition accuracy based on K-Nearest Neighbor reaches 96.43%. The results show that the proposed sEMG feature extraction method based on tensor decomposition helps improve the recognition accuracy of underwater sEMG signals, which provides a basis for applying prosthetic hands in a water environment.
publishDate 2023
dc.date.none.fl_str_mv 2023-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/218141
Xue, Jianing; Sun, Zhe; Duan, Feng; Caiafa, César Federico; Solé Casals, Jordi; Underwater sEMG-based Recognition of Hand Gestures using Tensor Decomposition; Elsevier Science; Pattern Recognition Letters; 165; 1-2023; 39-46
0167-8655
CONICET Digital
CONICET
url http://hdl.handle.net/11336/218141
identifier_str_mv Xue, Jianing; Sun, Zhe; Duan, Feng; Caiafa, César Federico; Solé Casals, Jordi; Underwater sEMG-based Recognition of Hand Gestures using Tensor Decomposition; Elsevier Science; Pattern Recognition Letters; 165; 1-2023; 39-46
0167-8655
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0167865522003518
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.patrec.2022.11.021
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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