Fear Detection in Multimodal affective computing: Physiological Signals versus Catecholamine Concentration

dc.affiliation.dptoUC3M. Departamento de Tecnología Electrónicaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Diseño Microelectrónico y Aplicaciones (DMA)es
dc.contributor.authorGutiérrez Martín, Laura
dc.contributor.authorRomero Perales, Elena
dc.contributor.authorSainz de Baranda Andújar, Clara
dc.contributor.authorCanabal Benito, Manuel Felipe
dc.contributor.authorRodríguez-Ramos, Gema Esther
dc.contributor.authorToro-Flores, Rafael
dc.contributor.authorLópez-Ongil, Susana
dc.contributor.authorLópez Ongil, Celia
dc.contributor.funderComunidad de Madrides
dc.contributor.funderUniversidad Carlos III de Madrides
dc.description.abstractAffective computing through physiological signals monitoring is currently a hot topic in the scientific literature, but also in the industry. Many wearable devices are being developed for health or wellness tracking during daily life or sports activity. Likewise, other applications are being proposed for the early detection of risk situations involving sexual or violent aggressions, with the identification of panic or fear emotions. The use of other sources of information, such as video or audio signals will make multimodal affective computing a more powerful tool for emotion classification, improving the detection capability. There are other biological elements that have not been explored yet and that could provide additional information to better disentangle negative emotions, such as fear or panic. Catecholamines are hormones produced by the adrenal glands, two small glands located above the kidneys. These hormones are released in the body in response to physical or emotional stress. The main catecholamines, namely adrenaline, noradrenaline and dopamine have been analysed, as well as four physiological variables: skin temperature, electrodermal activity, blood volume pulse (to calculate heart rate activity. i.e., beats per minute) and respiration rate. This work presents a comparison of the results provided by the analysis of physiological signals in reference to catecholamine, from an experimental task with 21 female volunteers receiving audiovisual stimuli through an immersive environment in virtual reality. Artificial intelligence algorithms for fear classification with physiological variables and plasma catecholamine concentration levels have been proposed and tested. The best results have been obtained with the features extracted from the physiological variables. Adding catecholamine’s maximum variation during the five minutes after the video clip visualization, as well as adding the five measurements (1-min interval) of these levels, are not providing better performance in the classifiers.en
dc.description.sponsorshipThis research has been supported by the Madrid Governement (Comunidad de Madrid, Spain) under the ARTEMISA-UC3M-CM research project (reference 2020/00048/001), the EMPATIACM research project (reference Y2018/TCS-5046) and the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M26), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation).es
dc.identifier.bibliographicCitationGutiérrez-Martín, L., Romero-Perales, E., de Baranda Andújar, C. S., F. Canabal-Benito, M., Rodríguez-Ramos, G. E., Toro-Flores, R., López-Ongil, S., & López-Ongil, C. (2022). Fear Detection in Multimodal Affective Computing: Physiological Signals versus Catecholamine Concentration. In Sensors, 22(11), 4023en
dc.publisherMDPI AGen
dc.relation.projectIDComunidad de Madrid. Y2018/TCS-5046es
dc.relation.projectIDComunidad de Madrid. ARTEMISA-CM-UC3Mes
dc.relation.projectIDComunidad de Madrid. 2020/00048/001es
dc.rights© 2022 by the authors. Licensee MDPI, Basel, Switzerland.en
dc.rightsAtribución 3.0 España*
dc.rightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) licenseen
dc.rights.accessRightsopen accessen
dc.subject.otherMultimodal affective computingen
dc.subject.otherEmotion classificationen
dc.subject.otherWearable devicesen
dc.titleFear Detection in Multimodal affective computing: Physiological Signals versus Catecholamine Concentrationen
dc.typeresearch article*
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