Detecting Cognitive Distraction using Random Forest by Considering Eye Movement Type
Hiroaki Koma, Taku Harada, Akira Yoshizawa, Hirotoshi Iwasaki · International Journal of Cognitive Informatics and Natural Intelligence · 2017
Detecting distracted states can be applied to various problems such as danger prevention when driving a car. A cognitive distracted state is one example of a distracted state. It is known that eye movements express cognitive distraction. Eye movements can be classified into several types. In this paper, the authors detect a cognitive distraction using classified eye movement types when applying the Random Forest machine learning algorithm, which uses decision trees. They show the effectiveness of considering eye movement types for detecting cognitive distraction when applying Random Forest. The authors use visual experiments with still images for the detection.