Toward Intelligent Car Comfort Sensing: New Dataset and Analysis of Annotated Physiological Metrics

Comfort is a subjective experience that people attend to in everyday life including in cars where they are constrained in movement. Could intelligent cars sense their comfort levels for the purpose of maximizing it? To address this, first, we present a new dataset (available on request) of physical measures (skin temperature, blood volume pulse, electrodermal activity, and motion capture) and subjective thermal, sitting, and mental relaxation experience variables captured in semi-ecological settings in a car. Second, we provide an in-depth analysis of the relationship between passengers’ thermal experiences and physiological responses in the collected data. Our findings highlight complex duality in the relationship of thermal experience with heart rate variability and skin temperature variability. We discuss the practical implications that this may have for designing machine learning architectures for automatic detection of thermal discomfort.