Multimodal emotion evaluation: a physiological model for cost-effective emotion classification

Detalhes bibliográficos
Autor(a) principal: Pinto, Gisela
Data de Publicação: 2020
Outros Autores: Carvalho, João M., Barros, Filipa, Soares, Sandra C., Pinho, Armando J., Brás, Susana
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10773/28812
Resumo: Emotional responses are associated with distinct body alterations and are crucial to foster adaptive responses, well-being, and survival. Emotion identification may improve peoples' emotion regulation strategies and interaction with multiple life contexts. Several studies have investigated emotion classification systems, but most of them are based on the analysis of only one, a few, or isolated physiological signals. Understanding how informative the individual signals are and how their combination works would allow to develop more cost-effective, informative, and objective systems for emotion detection, processing, and interpretation. In the present work, electrocardiogram, electromyogram, and electrodermal activity were processed in order to find a physiological model of emotions. Both a unimodal and a multimodal approach were used to analyze what signal, or combination of signals, may better describe an emotional response, using a sample of 55 healthy subjects. The method was divided in: (1) signal preprocessing; (2) feature extraction; (3) classification using random forest and neural networks. Results suggest that the electrocardiogram (ECG) signal is the most effective for emotion classification. Yet, the combination of all signals provides the best emotion identification performance, with all signals providing crucial information for the system. This physiological model of emotions has important research and clinical implications, by providing valuable information about the value and weight of physiological signals for emotional classification, which can critically drive effective evaluation, monitoring and intervention, regarding emotional processing and regulation, considering multiple contexts.
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spelling Multimodal emotion evaluation: a physiological model for cost-effective emotion classificationAffective computingMultimodalFeature extractionRandom forestNeural networkEmotional responses are associated with distinct body alterations and are crucial to foster adaptive responses, well-being, and survival. Emotion identification may improve peoples' emotion regulation strategies and interaction with multiple life contexts. Several studies have investigated emotion classification systems, but most of them are based on the analysis of only one, a few, or isolated physiological signals. Understanding how informative the individual signals are and how their combination works would allow to develop more cost-effective, informative, and objective systems for emotion detection, processing, and interpretation. In the present work, electrocardiogram, electromyogram, and electrodermal activity were processed in order to find a physiological model of emotions. Both a unimodal and a multimodal approach were used to analyze what signal, or combination of signals, may better describe an emotional response, using a sample of 55 healthy subjects. The method was divided in: (1) signal preprocessing; (2) feature extraction; (3) classification using random forest and neural networks. Results suggest that the electrocardiogram (ECG) signal is the most effective for emotion classification. Yet, the combination of all signals provides the best emotion identification performance, with all signals providing crucial information for the system. This physiological model of emotions has important research and clinical implications, by providing valuable information about the value and weight of physiological signals for emotional classification, which can critically drive effective evaluation, monitoring and intervention, regarding emotional processing and regulation, considering multiple contexts.MDPI2020-07-10T11:05:33Z2020-06-21T00:00:00Z2020-06-21info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/28812eng1424-822010.3390/s20123510Pinto, GiselaCarvalho, João M.Barros, FilipaSoares, Sandra C.Pinho, Armando J.Brás, Susanainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T11:55:44Zoai:ria.ua.pt:10773/28812Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:01:16.159794Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Multimodal emotion evaluation: a physiological model for cost-effective emotion classification
title Multimodal emotion evaluation: a physiological model for cost-effective emotion classification
spellingShingle Multimodal emotion evaluation: a physiological model for cost-effective emotion classification
Pinto, Gisela
Affective computing
Multimodal
Feature extraction
Random forest
Neural network
title_short Multimodal emotion evaluation: a physiological model for cost-effective emotion classification
title_full Multimodal emotion evaluation: a physiological model for cost-effective emotion classification
title_fullStr Multimodal emotion evaluation: a physiological model for cost-effective emotion classification
title_full_unstemmed Multimodal emotion evaluation: a physiological model for cost-effective emotion classification
title_sort Multimodal emotion evaluation: a physiological model for cost-effective emotion classification
author Pinto, Gisela
author_facet Pinto, Gisela
Carvalho, João M.
Barros, Filipa
Soares, Sandra C.
Pinho, Armando J.
Brás, Susana
author_role author
author2 Carvalho, João M.
Barros, Filipa
Soares, Sandra C.
Pinho, Armando J.
Brás, Susana
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Pinto, Gisela
Carvalho, João M.
Barros, Filipa
Soares, Sandra C.
Pinho, Armando J.
Brás, Susana
dc.subject.por.fl_str_mv Affective computing
Multimodal
Feature extraction
Random forest
Neural network
topic Affective computing
Multimodal
Feature extraction
Random forest
Neural network
description Emotional responses are associated with distinct body alterations and are crucial to foster adaptive responses, well-being, and survival. Emotion identification may improve peoples' emotion regulation strategies and interaction with multiple life contexts. Several studies have investigated emotion classification systems, but most of them are based on the analysis of only one, a few, or isolated physiological signals. Understanding how informative the individual signals are and how their combination works would allow to develop more cost-effective, informative, and objective systems for emotion detection, processing, and interpretation. In the present work, electrocardiogram, electromyogram, and electrodermal activity were processed in order to find a physiological model of emotions. Both a unimodal and a multimodal approach were used to analyze what signal, or combination of signals, may better describe an emotional response, using a sample of 55 healthy subjects. The method was divided in: (1) signal preprocessing; (2) feature extraction; (3) classification using random forest and neural networks. Results suggest that the electrocardiogram (ECG) signal is the most effective for emotion classification. Yet, the combination of all signals provides the best emotion identification performance, with all signals providing crucial information for the system. This physiological model of emotions has important research and clinical implications, by providing valuable information about the value and weight of physiological signals for emotional classification, which can critically drive effective evaluation, monitoring and intervention, regarding emotional processing and regulation, considering multiple contexts.
publishDate 2020
dc.date.none.fl_str_mv 2020-07-10T11:05:33Z
2020-06-21T00:00:00Z
2020-06-21
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10.3390/s20123510
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