Towards Generalizable Drowsiness Monitoring with Physiological Sensors: A Preliminary Study
This work addresses the challenge of generalizable drowsiness monitoring for driving safety, but it is incremental as it builds on existing physiological signal analysis without introducing a new method.
The study analyzed physiological signals across four datasets to identify robust metrics for drowsiness detection, finding that increased heart rate stability, reduced respiratory amplitude, and decreased tonic EDA are consistently associated with drowsiness, with objective assessments being more sensitive than subjective ones.
Accurately detecting drowsiness is vital to driving safety. Among all 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. 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 different drowsiness inducers can lead to different physiological responses, and objective assessments were more sensitive than subjective ones in detecting drowsiness. Further, the increased heart rate stability, reduced respiratory amplitude, and decreased tonic EDA are robustly associated with increased drowsiness. The results enhance understanding of drowsiness detection and can inform future generalizable monitoring designs.