Detecting Mental Fatigue from Eye-Tracking Data Gathered While Watching Video

Monitoring mental fatigue is of increasing importance for improving cognitive performance and health outcomes. Previous models using eye-tracking data allow inference of fatigue in cognitive tasks, such as driving, but they require us to engage in a specific cognitive task. A model capable of estimating fatigue from eye-tracking data in natural-viewing situations when an individual is not performing cognitive tasks has many potential applications. Here, we collected eye-tracking data from 18 adults as they watched video clips (simulating the situation of watching TV programs) before and after performing cognitive tasks. Using this data, we built a fatigue-detection model including novel feature sets and an automated feature selection method. With eye-tracking data of individuals watching only 30-seconds worth of video, our model could determine whether that person was fatigued with 91.0% accuracy in 10-fold cross-validation (chance 50%). Through a comparison with a model incorporating the feature sets used in previous studies, we showed that our model improved the detection accuracy by up to 13.9% (from 77.1 to 91.0%).