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Multimodal prediction of trait emotional intelligence–Through affective changes measured using non-contact based physiological measures
Inability to efficiently deal with emotionally laden situations, often leads to poor interpersonal interactions. This adversely affects the individual’s psychological functioning. A higher trait emotional intelligence (EI) is not only associated with psychological wellbeing, educational attainment,...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8270480/ https://www.ncbi.nlm.nih.gov/pubmed/34242354 http://dx.doi.org/10.1371/journal.pone.0254335 |
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author | Prajapati, Vrinda Guha, Rajlakshmi Routray, Aurobinda |
author_facet | Prajapati, Vrinda Guha, Rajlakshmi Routray, Aurobinda |
author_sort | Prajapati, Vrinda |
collection | PubMed |
description | Inability to efficiently deal with emotionally laden situations, often leads to poor interpersonal interactions. This adversely affects the individual’s psychological functioning. A higher trait emotional intelligence (EI) is not only associated with psychological wellbeing, educational attainment, and job-related success, but also with willingness to seek professional and non-professional help for personal-emotional problems, depression and suicidal ideation. Thus, it is important to identify low (EI) individuals who are more prone to mental health problems than their high EI counterparts, and give them the appropriate EI training, which will aid in preventing the onset of various mood related disorders. Since people may be unaware of their level of EI/emotional skills or may tend to fake responses in self-report questionnaires in high stake situations, a system that assesses EI using physiological measures can prove affective. We present a multimodal method for detecting the level of trait Emotional intelligence using non-contact based autonomic sensors. To our knowledge, this is the first work to predict emotional intelligence level from physiological/autonomic (cardiac and respiratory) response patterns to emotions. Trait EI of 50 users was measured using Schutte Self Report Emotional Intelligence Test (SSEIT) along with their cardiovascular and respiratory data, which was recorded using FMCW radar sensor both at baseline and while viewing affective movie clips. We first examine relationships between users’ Trait EI scores and autonomic response and reactivity to the clips. Our analysis suggests a significant relationship between EI and autonomic response and reactivity. We finally attempt binary EI level detection using linear SVM. We also attempt to classify each sub factor of EI, namely–perception of emotion, managing own emotions, managing other’s emotions, and utilization of emotions. The proposed method achieves an EI classification accuracy of 84%, while accuracies ranging from 58 to 76% is achieved for recognition of the sub factors. This is the first step towards identifying EI of an individual purely through physiological responses. Limitation and future directions are discussed. |
format | Online Article Text |
id | pubmed-8270480 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-82704802021-07-21 Multimodal prediction of trait emotional intelligence–Through affective changes measured using non-contact based physiological measures Prajapati, Vrinda Guha, Rajlakshmi Routray, Aurobinda PLoS One Research Article Inability to efficiently deal with emotionally laden situations, often leads to poor interpersonal interactions. This adversely affects the individual’s psychological functioning. A higher trait emotional intelligence (EI) is not only associated with psychological wellbeing, educational attainment, and job-related success, but also with willingness to seek professional and non-professional help for personal-emotional problems, depression and suicidal ideation. Thus, it is important to identify low (EI) individuals who are more prone to mental health problems than their high EI counterparts, and give them the appropriate EI training, which will aid in preventing the onset of various mood related disorders. Since people may be unaware of their level of EI/emotional skills or may tend to fake responses in self-report questionnaires in high stake situations, a system that assesses EI using physiological measures can prove affective. We present a multimodal method for detecting the level of trait Emotional intelligence using non-contact based autonomic sensors. To our knowledge, this is the first work to predict emotional intelligence level from physiological/autonomic (cardiac and respiratory) response patterns to emotions. Trait EI of 50 users was measured using Schutte Self Report Emotional Intelligence Test (SSEIT) along with their cardiovascular and respiratory data, which was recorded using FMCW radar sensor both at baseline and while viewing affective movie clips. We first examine relationships between users’ Trait EI scores and autonomic response and reactivity to the clips. Our analysis suggests a significant relationship between EI and autonomic response and reactivity. We finally attempt binary EI level detection using linear SVM. We also attempt to classify each sub factor of EI, namely–perception of emotion, managing own emotions, managing other’s emotions, and utilization of emotions. The proposed method achieves an EI classification accuracy of 84%, while accuracies ranging from 58 to 76% is achieved for recognition of the sub factors. This is the first step towards identifying EI of an individual purely through physiological responses. Limitation and future directions are discussed. Public Library of Science 2021-07-09 /pmc/articles/PMC8270480/ /pubmed/34242354 http://dx.doi.org/10.1371/journal.pone.0254335 Text en © 2021 Prajapati et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Prajapati, Vrinda Guha, Rajlakshmi Routray, Aurobinda Multimodal prediction of trait emotional intelligence–Through affective changes measured using non-contact based physiological measures |
title | Multimodal prediction of trait emotional intelligence–Through affective changes measured using non-contact based physiological measures |
title_full | Multimodal prediction of trait emotional intelligence–Through affective changes measured using non-contact based physiological measures |
title_fullStr | Multimodal prediction of trait emotional intelligence–Through affective changes measured using non-contact based physiological measures |
title_full_unstemmed | Multimodal prediction of trait emotional intelligence–Through affective changes measured using non-contact based physiological measures |
title_short | Multimodal prediction of trait emotional intelligence–Through affective changes measured using non-contact based physiological measures |
title_sort | multimodal prediction of trait emotional intelligence–through affective changes measured using non-contact based physiological measures |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8270480/ https://www.ncbi.nlm.nih.gov/pubmed/34242354 http://dx.doi.org/10.1371/journal.pone.0254335 |
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