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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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Visual Analytics for Mobile Eye Tracking.

Kuno Kurzhals, Marcel Hlawatsch, Christof Seeger

    IEEE Transactions on Visualization and Computer Graphics
    |November 23, 2016
    PubMed
    Summary
    This summary is machine-generated.

    We developed a visual analytics system to automate the time-consuming annotation of areas of interest (AOIs) in eye tracking data. This approach simplifies the analysis of viewing behavior from wearable eye tracking glasses.

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    Area of Science:

    • Human-Computer Interaction
    • Data Visualization
    • Behavioral Science

    Background:

    • Eye tracking data analysis requires manual annotation of Areas of Interest (AOIs), which is a significant bottleneck.
    • Annotation is particularly laborious for data from wearable eye tracking glasses, demanding individual video annotation and cross-video AOI identification.

    Purpose of the Study:

    • To introduce a novel visual analytics approach for automating and simplifying the annotation of eye tracking data.
    • To integrate automatic, image-based clustering of eye tracking data within an interactive system for labeling and analysis.

    Main Methods:

    • Developed a user-centered visual analytics environment with tightly coupled, linked views for direct data interpretation.
    • Implemented image-based, automatic clustering of eye tracking data to streamline AOI identification.
    • Conducted an expert user study with eye tracking researchers to evaluate the system and gather feedback.

    Main Results:

    • The visual analytics approach significantly eases the annotation process compared to manual methods.
    • The system facilitates direct interpretation of labeled data within the context of recorded video stimuli.
    • User study provided insights into expert analysis strategies and system usability.

    Conclusions:

    • The proposed visual analytics system offers an efficient solution for annotating eye tracking data, reducing analysis time.
    • Integrating automatic clustering and interactive labeling enhances the interpretation of human viewing behavior.
    • The user-centered design and expert validation ensure the practical applicability of the system in eye tracking research.