Multisensor Measurement of Train Driver Mental Fatigue: From Simulation to Reality
2026-08-24 • Human-Computer Interaction
Human-Computer Interaction
AI summaryⓘ
The authors studied how mental fatigue affects train drivers when automation changes their role to mostly supervising rather than actively driving. They tested drivers both in a realistic simulator and on actual trains, using different sensors to measure fatigue. They found that heart rate variability and breathing rate were the most reliable signs of fatigue in both settings. Other brain and behavioral measures did not clearly show fatigue signs and were harder to collect in real conditions. The authors suggest focusing on simple body signals like heart and breathing rates for monitoring fatigue in real trains, but more research is needed with larger groups.
mental fatiguetrain automationheart rate variabilitybreathing rateEEGelectrodermal activityvigilancesupervisory monitoringn-back taskoperational conditions
Authors
Esther Bosch, Rebecca Kruschka, David Schackmann, Stephanie Hoyer, Wolfgang Kilian, Stefan Schwanitz, Anneke Hamann
Abstract
Increasing automation in rail transport shifts the train driver's role from active control to prolonged supervisory monitoring. This creates conditions for mental fatigue (MF) and reduced vigilance. Despite the safety relevance of this issue, evidence on the feasibility and robustness of physiological indicators of MF under operational rail conditions remains limited. Most prior work relies on simulators or lab studies. The present study investigated multiple subjective, physiological, and behavioral indicators of MF in professional train drivers across two complementary settings: a high-fidelity train simulator (n=14) and a real-world rail environment (n=6). To our knowledge, this is the first study to deploy a full multisensor battery under actual train operating conditions. In both settings, a standardized protocol was used comprising a baseline drive, a one-hour auditory n-back task as an MF induction procedure, and a second drive. Heart rate variability and breathing rate showed consistent and theoretically expected changes across both environments, suggesting reduced physiological arousal following the fatigue induction task. In contrast, EEG-based frontal theta power and parietal alpha and beta power, electrodermal activity, blink duration, and behavioral indicators did not show clear mental fatigue-related patterns. Real-world data collection revealed substantial technical challenges related to vibration, sensor connectivity, and concurrent high-frequency data acquisition. These findings suggest that autonomic indicators, particularly HRV and breathing rate, represent the most promising and ecologically robust measures for operational fatigue monitoring in train drivers. However, neurophysiological measures require further validation under realistic conditions before deployment in driver monitoring systems, and larger samples are needed to confirm these preliminary patterns.