分析四种数据集的生理信号，发现疲劳与嗜睡的生理反应不同
Towards Generalizable Drowsiness Monitoring with Physiological Sensors: A Preliminary Study
- 对比四组数据的心电、皮肤电、呼吸信号，找出与困倦相关的稳定特征
- 心率稳定性上升、呼吸幅度下降、皮肤电基线降低是困倦的共同指标
- 客观评估比主观报告更敏感，适合开发通用困倦监测系统
准确检测困倦对驾驶安全至关重要。相比基于摄像头的方法，基于生理信号的监测更具隐私保护性。然而，不同数据集中生理指标与困倦标签之间的关联存在冲突。为此，我们分析了四个数据集中的心电图（ECG）、皮肤电活动（EDA）和呼吸（RESP）信号，这些数据使用了不同的困倦诱导方式（如疲劳和低唤醒）和评估方法（主观与客观）。通过构建二元逻辑回归模型，识别出与困倦相关的生理指标。研究发现，不同的困倦诱因会导致不同的生理反应，且客观评估在检测困倦方面比主观评估更敏感。此外，心率稳定性提升、呼吸幅度减小以及皮肤电基线降低与困倦程度增加具有强相关性。这些结果深化了对困倦检测机制的理解，可为未来通用化监测系统的设计提供依据。
原文摘要 · Abstract (English)
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.
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