Fear detection in multimodal affective computing: physiological signals versus catecholamine concentration Articles uri icon

publication date

  • May 2022

start page

  • 1

end page

  • 27


  • 11


  • 22, 4023

International Standard Serial Number (ISSN)

  • 1424-3210

Electronic International Standard Serial Number (EISSN)

  • 1424-8220


  • 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.


  • Electronics
  • Telecommunications


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