20.500.12556/RUL-182476 Gaze dynamics reveal age-related physiological patterns across driving events Discriminating between multiple driving events using physiological signals remains challenging. This study examined whether gaze dynamics and pupil responses could distinguish multiple driving events and reveal age-related physiological patterns. Twenty-seven participants (12 young, 15 old) completed a simulated driving scenario featuring nine events while physiological signals were recorded. Statistical analyses revealed significant event-specific modulation of gaze dispersion metrics ((▫$\eta^2$▫ = 0.571–0.582, p < 0.001). Gradient boosted trees achieved 82.4% accuracy (95% CI [74.3%, 89.8%]) in classifying four events (Bicycle, DeerAlert, LowGas, StopAtGasStation) using 7 gaze-based features, with SHAP analysis identifying horizontal deviation standard deviation and median as primary discriminators. Time-series motif discovery revealed consistent gaze motif amplitudes (3.0–3.4 z-scores) across events, while pupil motifs showed selective enhancement during monitoring tasks (3.1–3.8 z-scores). Age-stratified analyses uncovered distinct physiological patterns: younger drivers exhibited greater gaze variability with broader scanning patterns, whereas older drivers demonstrated elevated pupil motif amplitudes (particularly during LowGas: 3.88 vs. 3.18 z-scores) alongside more constrained visual exploration. The convergence of statistical, machine learning, and motif discovery approaches establishes gaze dynamics as sufficient for multiclass event discrimination in simulated driving. These findings demonstrate that gaze-based features alone can reliably distinguish between specific driving events, while motif analysis reveals temporal dynamics and age-related patterns that warrant further investigation in larger samples, providing a foundation for interpretable driver monitoring systems. physiological patterns aging driver behavior driver monitoring systems machine learning time-series analysis fiziološki vzorci staranje vodenje voznika sistemi za spremljanje voznika strojno učenje analiza časovnih vrst true false true Angleški jezik Slovenski jezik Članek v reviji 2026-05-13 09:56:25 2026-05-13 09:56:37 2026-05-15 04:08:25 0000-00-00 00:00:00 2026 0 0 22 str. art. 103827 Vol. 213 Jun.-Jul. 2026 0000-00-00 Zaloznikova Objavljeno apc 0000-00-00 0000-00-00 0000-00-00 004.85:629.072-053.9:159.955 1071-5819 10.1016/j.ijhcs.2026.103827 277955075 1-s2.0-S1071581926001023-main.pdf 1-s2.0-S1071581926001023-main.pdf 1 D0ECA7243B4A54C9B9D457E3B740EBCD 7f18adee5ab3fac5a5c0e9760f15c44260031b5159ed80e72dfefff05624f331 b43de92f-4ea3-11f1-b0ab-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=233782 https://www.sciencedirect.com/science/article/pii/S1071581926001023 1 f72c06aa-4ea0-11f1-b0ab-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=233781 Fakulteta za elektrotehniko 0 0 0