Traffic accidents determine a large number of injuries, sometimes fatal, every year. Among other factors affecting a driver’s performance, an important role is played by stress which can decrease decision-making capabilities and situational awareness. In this perspective, it would be beneficial to develop a non-invasive driver stress monitoring system able to recognize the driver’s altered state. In this study, a contactless procedure for drivers’ stress state assessment by means of thermal infrared imaging was investigated. Thermal imaging was acquired during an experiment on a driving simulator, and thermal features of stress were investigated with comparison to a gold-standard metric (i.e., the stress index, SI) extracted from contact electrocardiography (ECG). A data-driven multivariate machine learning approach based on a non-linear support vector regression (SVR) was employed to estimate the SI through thermal features extracted from facial regions of interest (i.e., nose tip, nostrils, glabella). The predicted SI showed a good correlation with the real SI (r = 0.61, p = ~0). A two-level classification of the stress state (STRESS, SI ≥ 150, versus NO STRESS, SI < 150) was then performed based on the predicted SI. The ROC analysis showed a good classification performance with an AUC of 0.80, a sensitivity of 77%, and a specificity of 78%.
Over the recent years, Social Robots (SRs) have become more and more prominent in everyday human lives. The main goal of a SR is to interact and communicate with human by following social behaviors and affective interaction. However, they still encounter significant limitations in pursuing a natural interaction, mainly due to their hard task of recognizing and understanding human emotions thus ensuring an appropriate response. The aim of this study was to enrich the SR with affective computing capability and real time assessment of the interlocutor’s psychophysiological state, by means of computational psychophysiology based on thermal infrared imaging.
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