Human Acute Stress Detection via Integration of Physiological Signals and Thermal Imaging

Abstract

Daily pressure, work load, and family responsibilities among other factors impose increasing levels of stress on different individuals. Hence, detecting stress as early as possible can potentially reduce the severe consequences and risks that someone may experience. In this paper, we develop a novel dataset to detect acute stress using 50 subjects. We additionally analyze different features extracted automatically from the thermal and physiological modalities. Furthermore, we develop a system that integrates both thermal and physiological features for improved stress detection rates. Our system achieves promising results exceeding 75% accuracy and has the potential to be further improved by adding additional modalities, which can provide a useful and reliable approach in early detection of stress.