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Online Flight Crew Alertness and Fatigue Level Monitoring System Using Machine Learning
, Ph.D. Dissertation Sharif University of Technology ; Saghafi, Fariborz (Supervisor)
Abstract
Pilot fatigue and increased cognitive load are primary contributors to fatal aviation accidents, especially in general aviation. However, pilots lack real-time, objective systems for monitoring their cognitive states. This dissertation introduces a cost-effective multimodal framework combining gaze tracking, control stick inputs, and Continuous Performance Test (CPT) scores to compute a cognitive performance stability index based on exceedance autocorrelation. Experiments were conducted in a single-engine Cessna simulator under IFR conditions with pilots of varying experience. Simulator data, gaze coordinates, control inputs, and CPT outputs were captured through automated software...