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%).
KeywordsMental fatigue Cognitive fatigue Feature selection Natural viewing Free viewing Visual attention model
This research was partially supported by the Japan Science and Technology Agency (JST) under the Strategic Promotion of Innovative Research and Development Program.
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