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 attainm...

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Autores principales:	Vrinda Prajapati, Rajlakshmi Guha, Aurobinda Routray
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c32a29b2315b498ebed254d068448250
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spelling	oai:doaj.org-article:c32a29b2315b498ebed254d0684482502021-12-02T20:15:31ZMultimodal prediction of trait emotional intelligence-Through affective changes measured using non-contact based physiological measures.1932-620310.1371/journal.pone.0254335https://doaj.org/article/c32a29b2315b498ebed254d0684482502021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254335https://doaj.org/toc/1932-6203Inability 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.Vrinda PrajapatiRajlakshmi GuhaAurobinda RoutrayPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254335 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Vrinda Prajapati Rajlakshmi Guha Aurobinda Routray Multimodal prediction of trait emotional intelligence-Through affective changes measured using non-contact based physiological measures.
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	article
author	Vrinda Prajapati Rajlakshmi Guha Aurobinda Routray
author_facet	Vrinda Prajapati Rajlakshmi Guha Aurobinda Routray
author_sort	Vrinda Prajapati
title	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_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_sort	multimodal prediction of trait emotional intelligence-through affective changes measured using non-contact based physiological measures.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/c32a29b2315b498ebed254d068448250
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_version_	1718374608532406272

Multimodal prediction of trait emotional intelligence-Through affective changes measured using non-contact based physiological measures.

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