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Abstract

Five concurrent eye activity measures were used to model fatigue-related changes in performance during a visual compensatory tracking task. Five subjects demonstrated considerable variations in performance level within two 53-min testing sessions during which continuous video-based eye activity measures were obtained. For each subject, moving estimates of blink duration and frequency, fixation duration and frequency, and mean pupil diameter from one session were used to train an artificial neural network to produce moving estimates of changes in mean tracking performance during the same session. Applied to eye tracking data from a second session, the same networks produced moving estimates of tracking performance that were highly correlated with actual performance changes (R ²=0.65, range 0.30–0.89 across ten sessions). The results suggest that information from multiple eye measures may be combined to produce individualized and accurate estimates of sub-minute scale changes in alertness during continuous task performance.

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Eye Activity Correlates of Fatigue during a Visual Tracking Task

Abstract

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References