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Related Experiment Video

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Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
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Gaze Behavior Effect on Gaze Data Visualization at Different Abstraction Levels.

Sangbong Yoo1, Seongmin Jeong1, Yun Jang1

  • 1Computer Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Korea.

Sensors (Basel, Switzerland)
|July 24, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel gaze behavior-based method for abstract gaze data visualization. It uses machine learning to classify eye movements, improving visualization clarity and reducing manual parameter tuning.

Keywords:
gaze behaviorgaze data visualizationmachine learning

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Area of Science:

  • Human-Computer Interaction
  • Data Visualization
  • Cognitive Science

Background:

  • Gaze data visualization is crucial for understanding user interaction.
  • Raw gaze data is complex, obscuring insights.
  • Existing fixation identification algorithms significantly alter visualization outcomes.

Purpose of the Study:

  • To develop a gaze behavior-based data processing method for abstract gaze data visualization.
  • To address the challenges of raw gaze data complexity and algorithm-dependent visualizations.
  • To compare the effectiveness of various fixation identification algorithms.

Main Methods:

  • Proposed a gaze behavior-based data processing method.
  • Utilized machine learning models (CNN, AlexNet, LeNet) for raw gaze data classification.
  • Compared velocity-based (I-VT), dispersion-based (I-DT), density-based, velocity and dispersion-based (I-VDT), and machine learning-based models.

Main Results:

  • Machine learning models effectively classify raw gaze data for visualization.
  • The proposed method offers a more robust approach to abstract gaze data visualization.
  • Comparisons revealed significant differences in visualization patterns across algorithms.

Conclusions:

  • Gaze behavior-based processing enhances abstract gaze data visualization.
  • Machine learning offers a promising alternative for fixation identification.
  • Standardized methods are needed to understand algorithm impact on gaze visualizations.