Gaze Behavior Effect on Gaze Data Visualization at Different Abstraction Levels

Many gaze data visualization techniques intuitively show eye movement together with visual stimuli. The eye tracker records a large number of eye movements within a short period. Therefore, visualizing raw gaze data with the visual stimulus appears complicated and obscured, making it difficult to ga...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-07, Vol.21 (14), p.4686
Main Authors: Yoo, Sangbong, Jeong, Seongmin, Jang, Yun
Format: Article
Language:English
Subjects:
Algorithms
Behavior
Data analysis
Data processing
Dispersion
Eye movements
Fixation
gaze behavior
gaze data visualization
Identification
Image classification
Machine learning
Scientific visualization
Scientists
Statistical analysis
Velocity
Visualization
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Summary:Many gaze data visualization techniques intuitively show eye movement together with visual stimuli. The eye tracker records a large number of eye movements within a short period. Therefore, visualizing raw gaze data with the visual stimulus appears complicated and obscured, making it difficult to gain insight through visualization. To avoid the complication, we often employ fixation identification algorithms for more abstract visualizations. In the past, many scientists have focused on gaze data abstraction with the attention map and analyzed detail gaze movement patterns with the scanpath visualization. Abstract eye movement patterns change dramatically depending on fixation identification algorithms in the preprocessing. However, it is difficult to find out how fixation identification algorithms affect gaze movement pattern visualizations. Additionally, scientists often spend much time on adjusting parameters manually in the fixation identification algorithms. In this paper, we propose a gaze behavior-based data processing method for abstract gaze data visualization. The proposed method classifies raw gaze data using machine learning models for image classification, such as CNN, AlexNet, and LeNet. Additionally, we compare the velocity-based identification (I-VT), dispersion-based identification (I-DT), density-based fixation identification, velocity and dispersion-based (I-VDT), and machine learning based and behavior-based modelson various visualizations at each abstraction level, such as attention map, scanpath, and abstract gaze movement visualization.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21144686