Pilot mental fatigue is a growing concern in the aviation field due to its significant contributions to human errors and aviation accidents. Long work hours, sleep loss, circadian rhythm disruption, and workload are well-known reasons, but there is a need to accurately detect pilot mental fatigue to improve aviation safety. However, due to the highly restricted cockpit environment and the complex nature of mental fatigue, feasible in-flight detection remains under-investigated. The objective of this study is to define a promising approach for mental fatigue detection based on psychophysiological measurements in flying-relevant environments. Eleven participants engaged in a simulated flight experiment, where several conventional heart rate variability (HRV) and ocular indices were examined to study their relevance to mental fatigue. Additionally, a Toeplitz Inverse Covariance-Based Clustering (TICC) method was performed to determine the ground truth, after which supervised machine learning was adopted to enable automated mental fatigue detection using HRV and eye metrics. Results showed that HRV and eye metrics were sensitive to the mental fatigue induced by prolonged flight-relevant tasks. The TICC method helped determine the ground truth for mental fatigue and identify its three distinct levels. Furthermore, a supervised learning-based detection of mental fatigue was achieved using a support vector machine with the greatest detection accuracy of 91.8%. The findings and methodology of this study provide new insights into the fatigue countermeasures in restricted cockpit environment and lay the groundwork for further explorations into the mental fatigue induced by prolonged flight missions.

中文翻译：

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在模拟飞行期间使用心率变异性和眼部指标检测精神疲劳状态

由于飞行员精神疲劳对人为错误和航空事故的重大贡献，飞行员精神疲劳在航空领域日益受到关注。工作时间长、睡眠不足、昼夜节律紊乱、工作量大是众所周知的原因，但需要准确检测飞行员的精神疲劳，以提高航空安全。然而，由于驾驶舱环境的高度限制和精神疲劳的复杂性，可行的飞行检测仍在研究中。本研究的目的是基于飞行相关环境中的心理生理学测量，定义一种有前景的精神疲劳检测方法。11 名参与者参与了模拟飞行实验，其中检查了几种常规心率变异性 (HRV) 和眼部指数，以研究它们与精神疲劳的相关性。此外，执行基于 Toeplitz 逆协方差的聚类 (TICC) 方法来确定基本事实，然后采用监督机器学习以使用 HRV 和眼部指标实现自动心理疲劳检测。结果表明，HRV 和眼睛指标对长时间飞行相关任务引起的精神疲劳很敏感。TICC 方法有助于确定精神疲劳的基本事实并确定其三个不同的水平。此外，使用支持向量机实现了基于监督学习的精神疲劳检测，最高检测准确率为 91.8%。