Determining the Optimal Window Duration to Enhance Emotion Recognition Based on Galvanic Skin Response and Photoplethysmography Signals

Autores
Bamonte, Marcos F.; Risk, Marcelo; Herrero, Victor
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Automatic emotion recognition using portable sensors is gaining attention due to its potential use in real-life scenarios. Existing studies have not explored Galvanic Skin Response and Photoplethysmography sensors exclusively for emotion recognition using nonlinear features with machine learning (ML) classifiers such as Random Forest, Support Vector Machine, Gradient Boosting Machine, K-Nearest Neighbor, and Decision Tree. In this study, we proposed a genuine window sensitivity analysis on a continuous annotation dataset to determine the window duration and percentage of overlap that optimize the classification performance using ML algorithms and nonlinear features, namely, Lyapunov Exponent, Approximate Entropy, and Poincaré indices. We found an optimum window duration of 3 s with 50% overlap and achieved accuracies of 0.75 and 0.74 for both arousal and valence, respectively. In addition, we proposed a Strong Labeling Scheme that kept only the extreme values of the labels, which raised the accuracy score to 0.94 for arousal. Under certain conditions mentioned, traditional ML models offer a good compromise between performance and low computational cost. Our results suggest that well-known ML algorithms can still contribute to the field of emotion recognition, provided that window duration, overlap percentage, and nonlinear features are carefully selected.
Fil: Bamonte, Marcos F.. Universidad Austral. Facultad de Ingenieria. Laboratorio de Investigacion Desarrollo y Transferencia. - Comision de Investigaciones Cientificas de la Provincia de Buenos Aires. Laboratorio de Investigacion Desarrollo y Transferencia.; Argentina
Fil: Risk, Marcelo. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Houssay. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Hospital Italiano. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Instituto Universitario Hospital Italiano de Buenos Aires. Instituto de Medicina Traslacional E Ingenieria Biomedica.; Argentina
Fil: Herrero, Victor. Universidad Austral. Facultad de Ingenieria. Laboratorio de Investigacion Desarrollo y Transferencia. - Comision de Investigaciones Cientificas de la Provincia de Buenos Aires. Laboratorio de Investigacion Desarrollo y Transferencia.; Argentina
Materia
emotion recognition
machine learning
 photoplethysmography
galvanic skin response
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:11336/264285
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Determining the Optimal Window Duration to Enhance Emotion Recognition Based on Galvanic Skin Response and Photoplethysmography SignalsBamonte, Marcos F.Risk, MarceloHerrero, Victoremotion recognitionmachine learning photoplethysmographygalvanic skin responsehttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Automatic emotion recognition using portable sensors is gaining attention due to its potential use in real-life scenarios. Existing studies have not explored Galvanic Skin Response and Photoplethysmography sensors exclusively for emotion recognition using nonlinear features with machine learning (ML) classifiers such as Random Forest, Support Vector Machine, Gradient Boosting Machine, K-Nearest Neighbor, and Decision Tree. In this study, we proposed a genuine window sensitivity analysis on a continuous annotation dataset to determine the window duration and percentage of overlap that optimize the classification performance using ML algorithms and nonlinear features, namely, Lyapunov Exponent, Approximate Entropy, and Poincaré indices. We found an optimum window duration of 3 s with 50% overlap and achieved accuracies of 0.75 and 0.74 for both arousal and valence, respectively. In addition, we proposed a Strong Labeling Scheme that kept only the extreme values of the labels, which raised the accuracy score to 0.94 for arousal. Under certain conditions mentioned, traditional ML models offer a good compromise between performance and low computational cost. Our results suggest that well-known ML algorithms can still contribute to the field of emotion recognition, provided that window duration, overlap percentage, and nonlinear features are carefully selected.Fil: Bamonte, Marcos F.. Universidad Austral. Facultad de Ingenieria. Laboratorio de Investigacion Desarrollo y Transferencia. - Comision de Investigaciones Cientificas de la Provincia de Buenos Aires. Laboratorio de Investigacion Desarrollo y Transferencia.; ArgentinaFil: Risk, Marcelo. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Houssay. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Hospital Italiano. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Instituto Universitario Hospital Italiano de Buenos Aires. Instituto de Medicina Traslacional E Ingenieria Biomedica.; ArgentinaFil: Herrero, Victor. Universidad Austral. Facultad de Ingenieria. Laboratorio de Investigacion Desarrollo y Transferencia. - Comision de Investigaciones Cientificas de la Provincia de Buenos Aires. Laboratorio de Investigacion Desarrollo y Transferencia.; ArgentinaMDPI2024-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/264285Bamonte, Marcos F.; Risk, Marcelo; Herrero, Victor; Determining the Optimal Window Duration to Enhance Emotion Recognition Based on Galvanic Skin Response and Photoplethysmography Signals; MDPI; Electronics; 13; 16; 8-2024; 1-182079-9292CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2079-9292/13/16/3333info:eu-repo/semantics/altIdentifier/doi/10.3390/electronics13163333info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:21:28Zoai:ri.conicet.gov.ar:11336/264285instacron: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:34982025-09-10 13:21:28.913CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Determining the Optimal Window Duration to Enhance Emotion Recognition Based on Galvanic Skin Response and Photoplethysmography Signals
title Determining the Optimal Window Duration to Enhance Emotion Recognition Based on Galvanic Skin Response and Photoplethysmography Signals
spellingShingle Determining the Optimal Window Duration to Enhance Emotion Recognition Based on Galvanic Skin Response and Photoplethysmography Signals
Bamonte, Marcos F.
emotion recognition
machine learning
 photoplethysmography
galvanic skin response
title_short Determining the Optimal Window Duration to Enhance Emotion Recognition Based on Galvanic Skin Response and Photoplethysmography Signals
title_full Determining the Optimal Window Duration to Enhance Emotion Recognition Based on Galvanic Skin Response and Photoplethysmography Signals
title_fullStr Determining the Optimal Window Duration to Enhance Emotion Recognition Based on Galvanic Skin Response and Photoplethysmography Signals
title_full_unstemmed Determining the Optimal Window Duration to Enhance Emotion Recognition Based on Galvanic Skin Response and Photoplethysmography Signals
title_sort Determining the Optimal Window Duration to Enhance Emotion Recognition Based on Galvanic Skin Response and Photoplethysmography Signals
dc.creator.none.fl_str_mv Bamonte, Marcos F.
Risk, Marcelo
Herrero, Victor
author Bamonte, Marcos F.
author_facet Bamonte, Marcos F.
Risk, Marcelo
Herrero, Victor
author_role author
author2 Risk, Marcelo
Herrero, Victor
author2_role author
author
dc.subject.none.fl_str_mv emotion recognition
machine learning
 photoplethysmography
galvanic skin response
topic emotion recognition
machine learning
 photoplethysmography
galvanic skin response
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Automatic emotion recognition using portable sensors is gaining attention due to its potential use in real-life scenarios. Existing studies have not explored Galvanic Skin Response and Photoplethysmography sensors exclusively for emotion recognition using nonlinear features with machine learning (ML) classifiers such as Random Forest, Support Vector Machine, Gradient Boosting Machine, K-Nearest Neighbor, and Decision Tree. In this study, we proposed a genuine window sensitivity analysis on a continuous annotation dataset to determine the window duration and percentage of overlap that optimize the classification performance using ML algorithms and nonlinear features, namely, Lyapunov Exponent, Approximate Entropy, and Poincaré indices. We found an optimum window duration of 3 s with 50% overlap and achieved accuracies of 0.75 and 0.74 for both arousal and valence, respectively. In addition, we proposed a Strong Labeling Scheme that kept only the extreme values of the labels, which raised the accuracy score to 0.94 for arousal. Under certain conditions mentioned, traditional ML models offer a good compromise between performance and low computational cost. Our results suggest that well-known ML algorithms can still contribute to the field of emotion recognition, provided that window duration, overlap percentage, and nonlinear features are carefully selected.
Fil: Bamonte, Marcos F.. Universidad Austral. Facultad de Ingenieria. Laboratorio de Investigacion Desarrollo y Transferencia. - Comision de Investigaciones Cientificas de la Provincia de Buenos Aires. Laboratorio de Investigacion Desarrollo y Transferencia.; Argentina
Fil: Risk, Marcelo. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Houssay. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Hospital Italiano. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Instituto Universitario Hospital Italiano de Buenos Aires. Instituto de Medicina Traslacional E Ingenieria Biomedica.; Argentina
Fil: Herrero, Victor. Universidad Austral. Facultad de Ingenieria. Laboratorio de Investigacion Desarrollo y Transferencia. - Comision de Investigaciones Cientificas de la Provincia de Buenos Aires. Laboratorio de Investigacion Desarrollo y Transferencia.; Argentina
description Automatic emotion recognition using portable sensors is gaining attention due to its potential use in real-life scenarios. Existing studies have not explored Galvanic Skin Response and Photoplethysmography sensors exclusively for emotion recognition using nonlinear features with machine learning (ML) classifiers such as Random Forest, Support Vector Machine, Gradient Boosting Machine, K-Nearest Neighbor, and Decision Tree. In this study, we proposed a genuine window sensitivity analysis on a continuous annotation dataset to determine the window duration and percentage of overlap that optimize the classification performance using ML algorithms and nonlinear features, namely, Lyapunov Exponent, Approximate Entropy, and Poincaré indices. We found an optimum window duration of 3 s with 50% overlap and achieved accuracies of 0.75 and 0.74 for both arousal and valence, respectively. In addition, we proposed a Strong Labeling Scheme that kept only the extreme values of the labels, which raised the accuracy score to 0.94 for arousal. Under certain conditions mentioned, traditional ML models offer a good compromise between performance and low computational cost. Our results suggest that well-known ML algorithms can still contribute to the field of emotion recognition, provided that window duration, overlap percentage, and nonlinear features are carefully selected.
publishDate 2024
dc.date.none.fl_str_mv 2024-08
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/11336/264285
Bamonte, Marcos F.; Risk, Marcelo; Herrero, Victor; Determining the Optimal Window Duration to Enhance Emotion Recognition Based on Galvanic Skin Response and Photoplethysmography Signals; MDPI; Electronics; 13; 16; 8-2024; 1-18
2079-9292
CONICET Digital
CONICET
url http://hdl.handle.net/11336/264285
identifier_str_mv Bamonte, Marcos F.; Risk, Marcelo; Herrero, Victor; Determining the Optimal Window Duration to Enhance Emotion Recognition Based on Galvanic Skin Response and Photoplethysmography Signals; MDPI; Electronics; 13; 16; 8-2024; 1-18
2079-9292
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://www.mdpi.com/2079-9292/13/16/3333
info:eu-repo/semantics/altIdentifier/doi/10.3390/electronics13163333
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 MDPI
publisher.none.fl_str_mv MDPI
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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score 12.48226

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