Performance decrement associated with pilot fatigue is considered a leading contributor to aviation accidents and fatalities. The output of prevalent pilot fatigue methodologies (both subjective & objective) either suffer from human judgement bias or require complex data processing. Moreover, studies catering to long duration flight missions have not been performed. Presently, we investigate the impact of fatigue on pilot performance for long duration of a flight mission composed of multiple take-offs and landings. We propose a new multimodal approach that integrates traditional fatigue metrics with eye tracking methodology. The effect of fatigue on the pilots’ eye movements was evaluated using information theory-based entropy measures. Results showed an increase in the fatigue level (measured by mean reaction times and the number of lapses) with increase in flight duration. The entropy measures showed that visual attention distribution and scanning strategy both became random in nature as fatigue level increased in pilots. Obtained results suggest fatigue decreases both information searching and processing capability in pilots. The proposed method can show which aspect of the pilot performance becomes impaired by fatigue and thus can be applied to evaluate fatigue onset in real time, which enables timely recovery interventions.

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.