Deep Learning Methods for the Prediction of Information Display Type Using Eye Tracking Sequences

被引：0

作者：

Yin, Yuehan ^{[1
]}

Alqahtani, Yahya ^{[2
]}

Feng, Jinjuan Heidi ^{[1
]}

Chakraborty, Joyram ^{[1
]}

McGuire, Michael P. ^{[1
]}

机构：

[1] Towson Univ, Dept Comp & Informat Sci, Towson, MD 21252 USA

[2] Jazan Univ, Coll Comp Sci & Informat Technol, Jazan, Saudi Arabia

来源：

20TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2021) | 2021年

关键词：

Convolutional neural network (CNN); deep learning; eye tracking; recurrent neural network (RNN);

D O I：

10.1109/ICMLA52953.2021.00100

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Eye tracking data can help design effective user interfaces by showing how users visually process information. In this study, three neural network models were developed and employed to classify three types of information display methods by using eye gaze data that was collected in visual information processing behavior studies. Eye gaze data was first converted into a sequence and was fed into neural networks to predict the information display type. The results of the study show a comparison between three methods for the creation of eye tracking sequences and how they perform using three neural network models including CNN-LSTM, CNN-GRU, and 3D CNN. The results were positive with all models having an accuracy of higher than 88 percent.

引用

页码：601 / 605

页数：5

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