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Affective computing using physiological signals is a key focus in research and industry. Wearable devices track health and emotions, with emerging applications for detecting risks like sexual or violent assaults by recognizing fear or panic. Multimodal systems incorporating video or audio signals can improve emotion classification. Biological factors like catecholamines, stress hormones released by the adrenal glands, may also help distinguish negative emotions. In a study of 21 women in a virtual reality setting, AI classified fear using physiological signals and catecholamine levels. While physiological data gave the best results, adding catecholamine variations did not improve accuracy.
Qu et al. (Wed,) studied this question.
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