ORCID

Alireza Saidi : 0000-0003-4816-6539

Type de document

Études primaires

Année de publication

2026

Langue

Anglais

Titre de la revue

Sensors

Résumé

Drowsy driving contributes to roughly 20% of traffic fatalities, yet most detection systems rely on behavioral cues that appear only after impairment has set in. Here we ask whether first and second derivatives of heart rate variability (HRV) can detect pre-crash states earlier than conventional approaches. Twenty-five participants completed 49 driving simulator sessions while we recorded cardiac activity through capacitive ECG electrodes embedded in the seat backrest—a non-contact method that avoids the privacy concerns of camera-based monitoring. To prevent circular evaluation, ground truth labels were based solely on crash proximity rather than HRV-derived scores. The combined HRV feature set (conventional metrics plus derivatives) achieved AUC = 0.863 for pre-crash prediction; derivatives alone reached only AUC = 0.573, indicating their value as complementary rather than standalone features. Driving performance indicators remained the strongest predictors (AUC = 0.999). Temporally, derivative-based detection preceded behavioral manifestations by 5–8 min and crash events by 6.8 ± 2.3 min. Across 1591 crashes and 6.78 million data points, we found that HRV derivatives capture physiological changes that precede overt impairment, though their utility depends on integration with other feature types. ©MDPI

Mots-clés

Évaluation de la fatigue, Fatigue assessment, Conduite de véhicule, Driving, Rythme cardiaque, Pulse rate, Électrocardiographie, Electrocardiography, Sécurité routière, Road safety

Numéro de projet IRSST

2020-0006

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