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Detecting Cognitive Distraction using Random Forest by Considering Eye Movement Type

Detecting Cognitive Distraction using Random Forest by Considering Eye Movement Type

Hiroaki Koma, Taku Harada, Akira Yoshizawa, Hirotoshi Iwasaki
Copyright: © 2017 |Volume: 11 |Issue: 1 |Pages: 13
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781522511694|DOI: 10.4018/IJCINI.2017010102
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MLA

Koma, Hiroaki, et al. "Detecting Cognitive Distraction using Random Forest by Considering Eye Movement Type." IJCINI vol.11, no.1 2017: pp.16-28. http://doi.org/10.4018/IJCINI.2017010102

APA

Koma, H., Harada, T., Yoshizawa, A., & Iwasaki, H. (2017). Detecting Cognitive Distraction using Random Forest by Considering Eye Movement Type. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 11(1), 16-28. http://doi.org/10.4018/IJCINI.2017010102

Chicago

Koma, Hiroaki, et al. "Detecting Cognitive Distraction using Random Forest by Considering Eye Movement Type," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 11, no.1: 16-28. http://doi.org/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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