Fear Detection in Multimodal affective computing: Physiological Signals versus Catecholamine Concentration Articles uri icon

publication date

  • June 2021

start page

  • 4023

end page

  • 4050

issue

  • 11

volume

  • 22

International Standard Serial Number (ISSN)

  • 1424-3210

Electronic International Standard Serial Number (EISSN)

  • 1424-8220

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.

subjects

  • Electronics
  • Telecommunications

keywords

  • multimodal affective computing; catecholamines; emotion classification; wearable devices