Assessing drivers' psychophysiological states using heart rate, electrodermal activity, and pupillometry in real-world driving.

Objectives: The increasing availability of wearable, low-cost physiological sensors offers new opportunities for monitoring drivers' internal states during real-world driving, complementing traditional performance-based evaluations. This study investigates the potential of three biometric variables-heart rate, electrodermal activity, and pupil diameter-to identify psychophysiological conditions such as stress, workload, and fatigue in real-world road scenarios.

Methods: Data were collected from 10 drivers on a 4.- km rural road segment featuring 14 selected curves. Physiological signals were analyzed using Fuzzy C-Means clustering to detect recurring latent states.

Results: The analysis revealed weak correlations among the three indicators, suggesting they provide complementary information. Although heart rate did not show a consistent trend, dermal conductivity and pupil diameter exhibited cumulative responses, supporting their use as indicators of driver psychophysiological states. The following clustering technique identified two distinct psychophysiological profiles, varying along the road section.

Conclusions: The findings highlight the usefulness of multimodal physiological data for identifying potentially demanding road segments and suggest implications for infrastructure design and real-time driver assistance systems.