AI 中文总结
本研究结合生理信号与主观报告，分析学员飞行员飞行训练中的压力与疲劳模式，证实数据驱动方法可用于监测飞行员健康状态。
AI 中文摘要
本研究结合生理测量与自我报告测量，分析飞行训练全过程中学员飞行员的压力与疲惫模式。采用知觉压力量表（PSS-10）测量每次飞行前后的知觉压力与疲惫，飞行期间持续记录心率（HR）、皮电活动（EDA）、皮肤温度及加速度等生理数据。为识别唤醒与工作负荷的重复模式，对生理信号进行预处理并分飞行阶段分析。研究结果显示，随时间推移，与工作负荷相关的疲惫逐渐累积，表现为各飞行阶段皮电活动（EDA）与皮肤温度稳步上升，且飞行后自我报告的疲惫感增加；心率反应更具事件特异性，在飞行高需求阶段出现短暂峰值。总体而言，研究证实将生理信号与主观报告结合，对识别真实飞行训练中飞行员压力与疲劳模式的价值，并凸显数据驱动方法在监测飞行员健康状态方面的潜力。
英文摘要
This study analyzes patterns of stress and exhaustion among student pilots throughout flight training using a combination of physiological and self-reported measurements. The Perceived Stress Scale (PSS-10) was used to measure perceived stress and exhaustion before and after each flight, while physiological data, including heart rate (HR), electrodermal activity (EDA), skin temperature, and acceleration, were continuously recorded during flight sessions. To identify recurring patterns in arousal and workload, physiological signals were preprocessed and analyzed across the flight stages. The findings indicate a buildup of workload-related weariness over time, as evidenced by steady increases in EDA and skin temperature across flights, as well as post-flight increases in self-reported exhaustion. Heart rate responses were more event-specific, with brief spikes during high-demand phases of flight. Overall, the findings demonstrate the value of combining physiological signals with subjective reports to identify patterns of stress and fatigue during real-world flight training and highlight the potential of data-driven approaches for monitoring pilot well-being.
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