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    Area of Science:

    • Human-Computer Interaction
    • Computer Vision
    • Data Visualization

    Background:

    • Manual annotation of Areas of Interest (AOIs) for gaze data analysis is labor-intensive and prone to labeling ambiguities.
    • Efficient and accurate AOI annotation is crucial for reliable gaze data interpretation.

    Purpose of the Study:

    • To develop an interactive, user-centered system for efficient and explainable AOI annotation.
    • To enhance trust in machine learning-based AOI classification through uncertainty-aware visualization.

    Main Methods:

    • An integrated workflow combining visualization (EyeFlower glyphs, dimensionality reduction) with machine learning and user feedback.
    • Uncertainty-aware visualization to build user trust in the classification process.
    • Hardware-agnostic approach supporting both stationary and mobile eye-tracking data.

    Main Results:

    • Demonstrated an interactive labeling approach that streamlines AOI annotation.
    • Showcased the effectiveness of uncertainty-aware visualization in fostering user trust.
    • Validated the versatility and hardware-agnostic nature of the proposed system.

    Conclusions:

    • The interactive, explainable annotation system significantly improves the efficiency and accuracy of AOI labeling for gaze data.
    • The approach enhances user trust in automated classification, facilitating more reliable gaze analysis.
    • This method offers a versatile solution for diverse eye-tracking applications.