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Iris: a tool for designing contextually relevant gaze visualizations

Published: 25 June 2019 Publication History

Abstract

Advances in eye tracking technology have enabled new interaction techniques and gaze-based applications. However, the techniques for visualizing gaze information have remained relatively unchanged. We developed Iris, a tool to support the design of contextually relevant gaze visualizations. Iris allows users to explore displaying different features of gaze behavior including the current fixation point, duration, and saccades. Stylistic elements such as color, opacity, and smoothness can also be adjusted to give users creative and detailed control over the design of their gaze visualization. We present the Iris system and perform a user study to examine how participants can make use of the tool to devise contextually relevant gaze visualizations for a variety of collaborative tasks. We show that changes in color and opacity as well as variation in gaze trails can be adjusted to create meaningful gaze visualizations that fit the context of use.

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Cited By

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  • (2025)The fundamentals of eye tracking part 4: Tools for conducting an eye tracking studyBehavior Research Methods10.3758/s13428-024-02529-757:1Online publication date: 6-Jan-2025
  • (2024)GazeMolVR: Sharing Eye-Gaze Cues in a Collaborative VR Environment for Molecular VisualizationProceedings of the International Conference on Mobile and Ubiquitous Multimedia10.1145/3701571.3701599(7-23)Online publication date: 1-Dec-2024
  • (2024)The Widening Gap: The Benefits and Harms of Generative AI for Novice ProgrammersProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671116(469-486)Online publication date: 12-Aug-2024
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    cover image ACM Conferences
    ETRA '19: Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications
    June 2019
    623 pages
    ISBN:9781450367097
    DOI:10.1145/3314111
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 25 June 2019

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    Author Tags

    1. design
    2. eye-tracking
    3. gaze visualizations

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    Cited By

    View all
    • (2025)The fundamentals of eye tracking part 4: Tools for conducting an eye tracking studyBehavior Research Methods10.3758/s13428-024-02529-757:1Online publication date: 6-Jan-2025
    • (2024)GazeMolVR: Sharing Eye-Gaze Cues in a Collaborative VR Environment for Molecular VisualizationProceedings of the International Conference on Mobile and Ubiquitous Multimedia10.1145/3701571.3701599(7-23)Online publication date: 1-Dec-2024
    • (2024)The Widening Gap: The Benefits and Harms of Generative AI for Novice ProgrammersProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671116(469-486)Online publication date: 12-Aug-2024
    • (2022)EyeBox: A Toolbox based on Python3 for Eye Movement AnalysisProcedia Computer Science10.1016/j.procs.2022.03.024201(166-173)Online publication date: 2022
    • (2021)Visualizing Prediction Correctness of Eye Tracking ClassifiersACM Symposium on Eye Tracking Research and Applications10.1145/3448018.3457997(1-7)Online publication date: 25-May-2021
    • (2019)Designing Interactions with Intention-Aware Gaze-Enabled Artificial AgentsHuman-Computer Interaction – INTERACT 201910.1007/978-3-030-29384-0_17(255-281)Online publication date: 2-Sep-2019

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