Evaluation Model of Cognitive Distraction State Based on Eye Tracking Data Using Neural Networks
Taku Harada, Hirotoshi Iwasaki, Kazuaki Mori, Akira Yoshizawa, Fumio Mizoguchi · International Journal of Software Science and Computational Intelligence · 2014
Eye tracking reveals a person's state of mind. Thus, representing personal cognitive states using eye tracking leads to objective evaluations of these states, and this representation can be applied to various application fields. In this paper, the authors focus on the cognitive distraction state as a cognitive state, and the authors propose a model that evaluates personal cognitive distraction. The model takes as input eye tracking data and outputs the degree of personal cognitive distraction. The authors use a simple recurrent neural network, which is a type of neural network, to build the proposed model. In addition, the authors apply the proposed model to eye tracking for a person driving a car.