Publication: Fear Detection in Multimodal affective computing: Physiological Signals versus Catecholamine Concentration
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2022-05-26
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MDPI AG
Abstract
Affective 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.
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Keywords
Multimodal affective computing, Catecholamines, Emotion classification, Wearable devices
Bibliographic citation
Gutiérrez-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), 4023