Stress recognition in human-computer interaction using physiological and self-reported data

Abstract

This paper investigates gender differences in stress recognition in human computer interaction (HCI) for both objective (i.e., skin conductance) and subjective (i.e., valence-arousal VA ratings) metrics. To this end, 31 healthy participants, 18 females, performed five HCI tasks, while their skin conductance was recorded. These selected HCI tasks were the ones listed as the most stressful, by a group of typical computer users, who were involved in a face to face pre-experiment interview for the identification of stressful cases in computer interaction. After each task, participants rated their interaction experience using the valence-arousal scale. The collected data were split based on participants' gender. Skin conductance signals were analyzed using seven popular machine learning classifiers. In both groups the best stress recognition accuracy for all tasks was achieved by Linear Discriminant Analysis LDA; Males: Mean=94.8% and SD=1.5%, Females: Mean=98.9% and SD=0.3%. Self-reported data analysis revealed a significant difference on how both genders communicate their emotions using the arousal scale. Our findings tend to suggest that gender does not affect skin conductance data during subtle HCI tasks. However subjective ratings such as arousal of emotional experience must be utilized carefully.