Towards Generalizable Drowsiness Monitoring with Physiological Sensors: A Preliminary Study

Abstract

Accurately detecting drowsiness is vital to driving safety. Among different measures, physiological-signal-based drowsiness monitoring can be more privacy-preserving than a camera-based approach. However, conflicts exist regarding how physiological metrics are associated with different drowsiness labels across datasets, which might reduce the generalizability of data-driven models trained with multiple datasets. Thus, we analyzed key features from electrocardiograms (ECG), electrodermal activity (EDA), and respiratory (RESP) signals across four datasets, where different drowsiness inducers (such as fatigue and low arousal) and assessment methods (subjective vs. objective) were used. Binary logistic regression models were built to identify the physiological metrics that are associated with drowsiness. Findings indicate that distinct drowsiness inducers can lead to different physiological responses, and objective assessments were more sensitive than subjective ones in detecting drowsiness. Further, decreased heart rate stability, respiratory amplitude, and tonic EDA are robustly associated with increased drowsiness. These results enhance the understanding of drowsiness detection and can inform future generalizable monitoring designs.