A novel Approach for sEMG Gesture Recognition using Resource-constrained Hardware Platforms

Autores
Micheletto, Matías Javier; Chesñevar, Carlos Iván; Santos, Rodrigo Martin
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Classifying human gestures using surface electromyografic sensors (sEMG) is a challenging task. Wearable sensors have proven to be extremely useful in this context, but their performance is limited by several factors (signal noise, computing resources, battery consumption, etc.). In particular, computing resources impose a limitation in many application scenarios, in which lightweight classification approaches are desirable. Recent research has shown that machine learning techniques are useful for human gesture classification once their salient features have been determined. This paper presents a novel approach for human gesture classification in which two different strategies are combined: a) a technique based on autoencoders is used to perform feature extraction; b) two alternative machine learning algorithms (namely J48 and K*) are then used for the classification stage. Empirical results are provided, showing that for limited computing power platforms our approach outperforms other alternative methodologies.
Fil: Micheletto, Matías Javier. Universidad Nacional de la Patagonia Austral. Centro de Investigaciones y Transferencia Golfo San Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia Golfo San Jorge. Universidad Nacional de la Patagonia "San Juan Bosco". Centro de Investigaciones y Transferencia Golfo San Jorge; Argentina
Fil: Chesñevar, Carlos Iván. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Fil: Santos, Rodrigo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Materia
AUTOENCODER
DECISION TREES
GESTURE RECOGNITION
NEAREST NEIGHBOORS
SEMG
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:11336/200891
Acceder
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network_name_str CONICET Digital (CONICET)
spelling A novel Approach for sEMG Gesture Recognition using Resource-constrained Hardware PlatformsMicheletto, Matías JavierChesñevar, Carlos IvánSantos, Rodrigo MartinAUTOENCODERDECISION TREESGESTURE RECOGNITIONNEAREST NEIGHBOORSSEMGhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Classifying human gestures using surface electromyografic sensors (sEMG) is a challenging task. Wearable sensors have proven to be extremely useful in this context, but their performance is limited by several factors (signal noise, computing resources, battery consumption, etc.). In particular, computing resources impose a limitation in many application scenarios, in which lightweight classification approaches are desirable. Recent research has shown that machine learning techniques are useful for human gesture classification once their salient features have been determined. This paper presents a novel approach for human gesture classification in which two different strategies are combined: a) a technique based on autoencoders is used to perform feature extraction; b) two alternative machine learning algorithms (namely J48 and K*) are then used for the classification stage. Empirical results are provided, showing that for limited computing power platforms our approach outperforms other alternative methodologies.Fil: Micheletto, Matías Javier. Universidad Nacional de la Patagonia Austral. Centro de Investigaciones y Transferencia Golfo San Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia Golfo San Jorge. Universidad Nacional de la Patagonia "San Juan Bosco". Centro de Investigaciones y Transferencia Golfo San Jorge; ArgentinaFil: Chesñevar, Carlos Iván. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Santos, Rodrigo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaComsis Consortium2022-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/200891Micheletto, Matías Javier; Chesñevar, Carlos Iván; Santos, Rodrigo Martin; A novel Approach for sEMG Gesture Recognition using Resource-constrained Hardware Platforms; Comsis Consortium; Computer Science And Information Systems; 19; 3; 9-2022; 1199-12121820-0214CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://doiserbia.nb.rs/Article.aspx?ID=1820-02142200025Minfo:eu-repo/semantics/altIdentifier/doi/10.2298/CSIS220228025Minfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T12:05:48Zoai:ri.conicet.gov.ar:11336/200891instacron: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:34982025-10-22 12:05:48.746CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A novel Approach for sEMG Gesture Recognition using Resource-constrained Hardware Platforms
title A novel Approach for sEMG Gesture Recognition using Resource-constrained Hardware Platforms
spellingShingle A novel Approach for sEMG Gesture Recognition using Resource-constrained Hardware Platforms
Micheletto, Matías Javier
AUTOENCODER
DECISION TREES
GESTURE RECOGNITION
NEAREST NEIGHBOORS
SEMG
title_short A novel Approach for sEMG Gesture Recognition using Resource-constrained Hardware Platforms
title_full A novel Approach for sEMG Gesture Recognition using Resource-constrained Hardware Platforms
title_fullStr A novel Approach for sEMG Gesture Recognition using Resource-constrained Hardware Platforms
title_full_unstemmed A novel Approach for sEMG Gesture Recognition using Resource-constrained Hardware Platforms
title_sort A novel Approach for sEMG Gesture Recognition using Resource-constrained Hardware Platforms
dc.creator.none.fl_str_mv Micheletto, Matías Javier
Chesñevar, Carlos Iván
Santos, Rodrigo Martin
author Micheletto, Matías Javier
author_facet Micheletto, Matías Javier
Chesñevar, Carlos Iván
Santos, Rodrigo Martin
author_role author
author2 Chesñevar, Carlos Iván
Santos, Rodrigo Martin
author2_role author
author
dc.subject.none.fl_str_mv AUTOENCODER
DECISION TREES
GESTURE RECOGNITION
NEAREST NEIGHBOORS
SEMG
topic AUTOENCODER
DECISION TREES
GESTURE RECOGNITION
NEAREST NEIGHBOORS
SEMG
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Classifying human gestures using surface electromyografic sensors (sEMG) is a challenging task. Wearable sensors have proven to be extremely useful in this context, but their performance is limited by several factors (signal noise, computing resources, battery consumption, etc.). In particular, computing resources impose a limitation in many application scenarios, in which lightweight classification approaches are desirable. Recent research has shown that machine learning techniques are useful for human gesture classification once their salient features have been determined. This paper presents a novel approach for human gesture classification in which two different strategies are combined: a) a technique based on autoencoders is used to perform feature extraction; b) two alternative machine learning algorithms (namely J48 and K*) are then used for the classification stage. Empirical results are provided, showing that for limited computing power platforms our approach outperforms other alternative methodologies.
Fil: Micheletto, Matías Javier. Universidad Nacional de la Patagonia Austral. Centro de Investigaciones y Transferencia Golfo San Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia Golfo San Jorge. Universidad Nacional de la Patagonia "San Juan Bosco". Centro de Investigaciones y Transferencia Golfo San Jorge; Argentina
Fil: Chesñevar, Carlos Iván. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Fil: Santos, Rodrigo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
description Classifying human gestures using surface electromyografic sensors (sEMG) is a challenging task. Wearable sensors have proven to be extremely useful in this context, but their performance is limited by several factors (signal noise, computing resources, battery consumption, etc.). In particular, computing resources impose a limitation in many application scenarios, in which lightweight classification approaches are desirable. Recent research has shown that machine learning techniques are useful for human gesture classification once their salient features have been determined. This paper presents a novel approach for human gesture classification in which two different strategies are combined: a) a technique based on autoencoders is used to perform feature extraction; b) two alternative machine learning algorithms (namely J48 and K*) are then used for the classification stage. Empirical results are provided, showing that for limited computing power platforms our approach outperforms other alternative methodologies.
publishDate 2022
dc.date.none.fl_str_mv 2022-09
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/11336/200891
Micheletto, Matías Javier; Chesñevar, Carlos Iván; Santos, Rodrigo Martin; A novel Approach for sEMG Gesture Recognition using Resource-constrained Hardware Platforms; Comsis Consortium; Computer Science And Information Systems; 19; 3; 9-2022; 1199-1212
1820-0214
CONICET Digital
CONICET
url http://hdl.handle.net/11336/200891
identifier_str_mv Micheletto, Matías Javier; Chesñevar, Carlos Iván; Santos, Rodrigo Martin; A novel Approach for sEMG Gesture Recognition using Resource-constrained Hardware Platforms; Comsis Consortium; Computer Science And Information Systems; 19; 3; 9-2022; 1199-1212
1820-0214
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://doiserbia.nb.rs/Article.aspx?ID=1820-02142200025M
info:eu-repo/semantics/altIdentifier/doi/10.2298/CSIS220228025M
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
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Comsis Consortium
publisher.none.fl_str_mv Comsis Consortium
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
_version_ 1846782411495440384
score 12.982451

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