Detecting Cognitive Distraction using Random Forest by Considering Eye Movement Type

Hiroaki Koma, Taku Harada, Akira Yoshizawa and Hirotoshi Iwasaki
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Hiroaki Koma: Tokyo University of Science, Chiba, Japan
Taku Harada: Tokyo University of Science, Chiba, Japan
Akira Yoshizawa: Denso IT Laboratory, Inc., Tokyo, Japan
Hirotoshi Iwasaki: Denso IT Laboratory, Inc., Tokyo, Japan

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2017, vol. 11, issue 1, 16-28

Abstract: 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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jcini0:v:11:y:2017:i:1:p:16-28

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