Detecting Cognitive Distraction using Random Forest by Considering Eye Movement Type

Detecting Cognitive Distraction using Random Forest by Considering Eye Movement Type

Hiroaki Koma (Tokyo University of Science, Chiba, Japan), Taku Harada (Tokyo University of Science, Chiba, Japan), Akira Yoshizawa (Denso IT Laboratory, Inc., Tokyo, Japan) and Hirotoshi Iwasaki (Denso IT Laboratory, Inc., Tokyo, Japan)
DOI: 10.4018/IJCINI.2017010102
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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.
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Various research efforts have applied identification-based machine learning algorithms to detect cognitive distraction using eye movement data. In (Miyaji, Kawanaka, & Oguri, 2010), the Adaboost algorithm was applied to detect cognitive distraction in drivers using gaze angles and head rotation angles. In (Liu, Yang, Huang, & Lin, 2015) and (Liu, Yang, Huang, Yeo, & Lin, 2016), the Semi-Supervised Extreme Learning Machine algorithm was applied to detect distraction in drivers using eye and head movements. In (Liu, Yang, Huang, Lin, Klanner, Denk, & Rasshofer, 2015), the Cluster Regularized Extreme Learning Machine algorithm was applied to detect distraction in drivers using eye and head movements. However, classified eye movement types were not considered in these studies.

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