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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/218141
Ver los metadatos del registro completo
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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 |
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CONICET Digital (CONICET) |
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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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13.070432 |