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Fear recognition for women using a reduced set of physiological signals

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dc.affiliation.dptoUC3M. Departamento de Tecnología Electrónicaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Diseño Microelectrónico y Aplicaciones (DMA)es
dc.affiliation.institutoUC3M. Instituto Universitario de Estudios de Géneroes
dc.contributor.authorMiranda Calero, José Ángel
dc.contributor.authorCanabal Benito, Manuel Felipe
dc.contributor.authorGutiérrez Martín, Laura
dc.contributor.authorLanza Gutiérrez, José Manuel
dc.contributor.authorPortela García, Marta
dc.contributor.authorLópez Ongil, Celia
dc.contributor.funderComunidad de Madrides
dc.date.accessioned2021-10-06T09:10:56Z
dc.date.available2021-10-06T09:10:56Z
dc.date.issued2021-03-01
dc.descriptionThis article belongs to the Section Biomedical Sensors.en
dc.description.abstractEmotion recognition is benefitting from the latest research into physiological monitoring and wireless communications, among other remarkable achievements. These technologies can indeed provide solutions to protect vulnerable people in scenarios such as personal assaults, the abuse of children or the elderly, gender violence or sexual aggression. Cyberphysical systems using smart sensors, artificial intelligence and wearable and inconspicuous devices can serve as bodyguards to detect these risky situations (through fear-related emotion detection) and automatically trigger a protection protocol. As expected, these systems should be trained and customized for each user to ensure the best possible performance, which undoubtedly requires a gender perspective. This paper presents a specialized fear recognition system for women based on a reduced set of physiological signals. The architecture proposed is characterized by the usage of three physiological sensors, lightweight binary classification and the conjunction of linear (temporal and frequency) and non-linear features. Moreover, a binary fear mapping strategy between dimensional and discrete emotional information based on emotional self-report data is implemented to avoid emotional bias. The architecture is evaluated using a public multi-modal physiological dataset with two approaches (subject-dependent and subject-independent models) focusing on the female participants. As a result, the proposal outperforms the state-of-the-art in fear recognition, achieving a recognition rate of up to 96.33% for the subject-dependent model.en
dc.description.sponsorshipThis activity is partially supported by Community of Madrid in the pluri-annual agreement with Universidad Carlos III de Madrid, in the line of action "Excelence with the University Faculty", V Regional Plan of Scientific Research and Technology Innovation 2016-2020, and by the Community of Madrid Region Government under the Synergic Program: EMPATIA-CM, Y2018/TCS-5046.en
dc.format.extent31
dc.identifier.bibliographicCitationMiranda, J. A., F. Canabal, M., Gutiérrez-Martín, L., Lanza-Gutierrez, J. M., Portela-García, M. & López-Ongil, C. (2021). Fear Recognition for Women Using a Reduced Set of Physiological Signals. Sensors, 21(5), 1587.en
dc.identifier.doihttps://doi.org/10.3390/s21051587
dc.identifier.issn1424-8220
dc.identifier.publicationfirstpage1587
dc.identifier.publicationissue5
dc.identifier.publicationtitleSensorsen
dc.identifier.publicationvolume21
dc.identifier.urihttps://hdl.handle.net/10016/33372
dc.identifier.uxxiAR/0000028310
dc.language.isoeng
dc.publisherMDPI
dc.relation.projectIDComunidad de Madrid. Y2018/TCS-5046es
dc.rights© 2021 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.en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaElectrónicaes
dc.subject.otherFear recognitionen
dc.subject.otherPhysiological signalsen
dc.subject.otherSignal processingen
dc.subject.otherWearable sensorsen
dc.titleFear recognition for women using a reduced set of physiological signalsen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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