列车驾驶员精神疲劳的多传感器测量：从模拟到现实
Multisensor Measurement of Train Driver Mental Fatigue: From Simulation to Reality
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中文总结 AI 辅助
本研究在模拟器与真实铁路环境中，首次部署完整多传感器组测量列车驾驶员精神疲劳，发现HRV和呼吸频率是可靠的运营疲劳监测指标，神经生理指标需进一步验证。
中文摘要 AI 辅助
轨道交通自动化程度的提升，使列车驾驶员的角色从主动控制转向长期监控，这为精神疲劳（MF）和警觉性下降创造了条件。尽管该问题与安全密切相关，但关于铁路运营条件下精神疲劳生理指标的可行性与鲁棒性的证据仍有限，以往多数研究依赖模拟器或实验室研究。本研究在两种互补场景下调查了职业列车驾驶员的多种主观、生理及行为指标：高保真列车模拟器（样本量n=14）和真实铁路环境（样本量n=6）。据我们所知，这是首个在实际列车运营条件下部署完整多传感器组的研究。两种场景均采用标准化方案，包括基线驾驶、1小时听觉n-back任务作为精神疲劳诱导程序，以及第二次驾驶。心率变异性（HRV）和呼吸频率在两种环境中均呈现一致且符合理论预期的变化，表明疲劳诱导任务后生理唤醒降低。相比之下，基于脑电图（EEG）的额叶θ功率、顶叶α和β功率、皮肤电活动、眨眼时长及行为指标未呈现清晰的精神疲劳相关模式。真实环境数据采集暴露出与振动、传感器连接及同步高频数据采集相关的重大技术挑战。这些发现表明，自主神经指标，尤其是HRV和呼吸频率，是列车驾驶员运营疲劳监测最具前景且生态鲁棒性的指标；但神经生理指标在部署于驾驶员监测系统前需在现实条件下进一步验证，且需要更大样本确认这些初步模式。
英文摘要
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.
