Fear recognition for women using a reduced set of physiological signals

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dc.contributor.author Miranda Calero, José Ángel
dc.contributor.author Canabal Benito, Manuel Felipe
dc.contributor.author Gutiérrez Martín, Laura
dc.contributor.author Lanza Gutiérrez, José Manuel
dc.contributor.author Portela García, Marta
dc.contributor.author López Ongil, Celia
dc.date.accessioned 2021-10-06T09:10:56Z
dc.date.available 2021-10-06T09:10:56Z
dc.date.issued 2021-03-01
dc.identifier.bibliographicCitation Miranda, 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.
dc.identifier.issn 1424-8220
dc.identifier.uri http://hdl.handle.net/10016/33372
dc.description This article belongs to the Section Biomedical Sensors.
dc.description.abstract Emotion 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.
dc.description.sponsorship This 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.
dc.format.extent 31
dc.language.iso eng
dc.publisher MDPI
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.
dc.rights Atribución 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by/3.0/es/
dc.subject.other Fear recognition
dc.subject.other Physiological signals
dc.subject.other Signal processing
dc.subject.other Wearable sensors
dc.title Fear recognition for women using a reduced set of physiological signals
dc.type research article
dc.subject.eciencia Electrónica
dc.identifier.doi https://doi.org/10.3390/s21051587
dc.rights.accessRights open access
dc.relation.projectID Comunidad de Madrid. Y2018/TCS-5046
dc.identifier.publicationfirstpage 1587
dc.identifier.publicationissue 5
dc.identifier.publicationtitle Sensors
dc.identifier.publicationvolume 21
dc.identifier.uxxi AR/0000028310
dc.contributor.funder Comunidad de Madrid
dc.affiliation.dpto UC3M. Departamento de Tecnología Electrónica
dc.affiliation.instituto UC3M. Instituto Universitario de Estudios de Género
dc.affiliation.grupoinv UC3M. Grupo de Investigación: Diseño Microelectrónico y Aplicaciones (DMA)
dc.type.hasVersion VoR
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