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Multimodal Emotion Evaluation: A Physiological Model for Cost-Effective Emotion Classification
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 c...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7349550/ https://www.ncbi.nlm.nih.gov/pubmed/32575894 http://dx.doi.org/10.3390/s20123510 |
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author | Pinto, Gisela Carvalho, João M. Barros, Filipa Soares, Sandra C. Pinho, Armando J. Brás, Susana |
author_facet | Pinto, Gisela Carvalho, João M. Barros, Filipa Soares, Sandra C. Pinho, Armando J. Brás, Susana |
author_sort | Pinto, Gisela |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-7349550 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-73495502020-07-14 Multimodal Emotion Evaluation: A Physiological Model for Cost-Effective Emotion Classification Pinto, Gisela Carvalho, João M. Barros, Filipa Soares, Sandra C. Pinho, Armando J. Brás, Susana Sensors (Basel) Article 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. MDPI 2020-06-21 /pmc/articles/PMC7349550/ /pubmed/32575894 http://dx.doi.org/10.3390/s20123510 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pinto, Gisela Carvalho, João M. Barros, Filipa Soares, Sandra C. Pinho, Armando J. Brás, Susana Multimodal Emotion Evaluation: A Physiological Model for Cost-Effective Emotion Classification |
title | 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_short | Multimodal Emotion Evaluation: A Physiological Model for Cost-Effective Emotion Classification |
title_sort | multimodal emotion evaluation: a physiological model for cost-effective emotion classification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7349550/ https://www.ncbi.nlm.nih.gov/pubmed/32575894 http://dx.doi.org/10.3390/s20123510 |
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