Subjective Assessment of Stress in HCI

Abstract

Thirty-one healthy participants performed five stressful HCI tasks (stimuli) while their skin conductance signals were monitored. The selected interaction tasks were most frequently listed as stressful by 15 interviewees, who were typical computer users. At the end of each task, participants expressed their perceived emotional experience using the dimensional Valence-Arousal (VA) rating space. The obtained VA ratings were used to define nine rectangular regions in the VA space, labeled as "stress". Next, five popular machine learning classifiers were employed to identify stress based on the associated skin conductance signals per tested region. Results showed sufficient cross-region stress recognition accuracy; L-SVM: Mean=62.3%, SD=9.3%. Our findings support that the VA scale may be used for stress self-assessments in the context of subtle interaction events, which are typically expected in most HCI tasks.