RT Journal Article
T1 Fear recognition for women using a reduced set of physiological signals
A1 Miranda Calero, José Ángel
A1 Canabal Benito, Manuel Felipe
A1 Gutiérrez Martín, Laura
A1 Lanza Gutiérrez, José Manuel
A1 Portela García, Marta
A1 López Ongil, Celia
AB 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.
PB MDPI
SN 1424-8220
YR 2021
FD 2021-03-01
LK https://hdl.handle.net/10016/33372
UL https://hdl.handle.net/10016/33372
LA eng
NO This article belongs to the Section Biomedical Sensors.
NO 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.
DS e-Archivo
RD 27 jul. 2024