Multimodal Analysis of Eye Movements and Fatigue in a Simulated Glass Cockpit Environment
Multimodal Analysis of Eye Movements and Fatigue in a Simulated Glass Cockpit Environment
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DOI：
10.3390/aerospace8100283
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发表时间：
2021-10-01
期刊：
影响因子：
2.6
通讯作者：
Kim, Kwangtaek
中科院分区：
文献类型：
--
作者：
Naeeri, Salem;Kang, Ziho;Kim, Kwangtaek
飞行员疲劳是与人为失误相关的航空事故的一个关键原因。如果可以利用飞行员的眼动测量来预测疲劳，则可能会减少与人为相关的事故。眼动追踪可以是一种非侵入式可行的方法，不需要飞行员暂停当前的任务，并且设备不需要与飞行员直接接触。在这项研究中，研究了精神运动警惕性测试（PVT）测量（即反应时间、误报次数和失误次数）和眼动测量（即瞳孔大小、眼睛注视次数、眼睛注视持续时间、视觉熵）之间的正相关或负相关。然后，开发了疲劳预测模型，使用通过向前和向后逐步回归确定的眼动测量来预测疲劳。所提出的方法在涉及新手和专家飞行员的模拟短途多阶段飞行任务中实施。结果表明，根据专业知识（即新手与专家），测量之间的相关性有所不同；因此，相应地开发了两个预测模型。此外，回归结果表明，眼球运动测量的单个或子集可能足以预测疲劳。结果显示了使用非侵入性眼球运动作为疲劳预测指标的前景，并为我们进一步开发近乎实时的预警系统以防止严重事故奠定了基础。
Pilot fatigue is a critical reason for aviation accidents related to human errors. Human-related accidents might be reduced if the pilots' eye movement measures can be leveraged to predict fatigue. Eye tracking can be a non-intrusive viable approach that does not require the pilots to pause their current task, and the device does not need to be in direct contact with the pilots. In this study, the positive or negative correlations among the psychomotor vigilance test (PVT) measures (i.e., reaction times, number of false alarms, and number of lapses) and eye movement measures (i.e., pupil size, eye fixation number, eye fixation duration, visual entropy) were investigated. Then, fatigue predictive models were developed to predict fatigue using eye movement measures identified through forward and backward stepwise regressions. The proposed approach was implemented in a simulated short-haul multiphase flight mission involving novice and expert pilots. The results showed that the correlations among the measures were different based on expertise (i.e., novices vs. experts); thus, two predictive models were developed accordingly. In addition, the results from the regressions showed that either a single or a subset of the eye movement measures might be sufficient to predict fatigue. The results show the promise of using non-intrusive eye movements as an indicator for fatigue prediction and provides a foundation that can lead us closer to developing a near real-time warning system to prevent critical accidents.