Considering eye movement type when applying random forest to detect cognitive distraction
Hiroaki Koma, Taku Harada, Akira Yoshizawa, Hirotoshi Iwasaki · 2016
Eye movements are well known to express cognitive distraction. Detecting cognitive distraction can help to prevent work-related accidents; thus, it is very useful to detect cognitive distraction using eye movements. Eye movements can be classified into various types. In this paper, we apply an identification-based machine learning algorithm considering eye movement types. We apply Random Forest as the machine learning algorithm. We show the effectiveness of considering eye movement types when applying Random Forest to detect cognitive distraction.