Fear recognition for women using a reduced set of physiological signals
Authors
Miranda Calero, José A.; Canabal, Manuel F.; Gutierrez-Martin, Laura; Lanza Gutiérrez, José Manuel; Portela-Garcia, Marta; [et al.]Identifiers
Permanent link (URI): http://hdl.handle.net/10017/60516DOI: 10.3390/s21051587
ISSN: 1424-8220
Publisher
MDPI
Date
2021-02-25Funders
Comunidad de Madrid
Bibliographic citation
Miranda, J.A.; F. Canabal, M.; Gutiérrez-Martín, L.; Lanza-Gutierrez, J.M.; Portela-García, M.; López-Ongil, C. Fear Recognition for Women Using a Reduced Set of Physiological Signals. Sensors 2021, 21, 1587.
Keywords
Fear recognition
Physiological signals
Signal processing
Wearable sensors
Project
info:eu-repo/grantAgreement/CAM//Y2018%2FTCS-5046/ES//EMPATIA-CM
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Publisher's version
https://doi.org/10.3390/s21051587Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
© 2021 The authors
Access rights
info:eu-repo/semantics/openAccess
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.
Files in this item
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Fear_recognition_Sensors_2021.pdf | 639.2Kb |
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