research-article

Interaction graphs: visual analysis of eye movement data from interactive stimuli

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Michael BurchAuthors Info & Claims

ETRA '19: Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications

Article No.: 89, Pages 1 - 5

https://doi.org/10.1145/3317960.3321617

Published: 25 June 2019 Publication History

Abstract

Eye tracking studies have been conducted to understand the visual attention in different scenarios like, for example, how people read text, which graphical elements in a visualization are frequently attended, how they drive a car, or how they behave during a shopping task. All of these scenarios - either static or dynamic - show a visual stimulus in which the spectators are not able to change the visual content they see. This is different if interaction is allowed like in (graphical) user interfaces (UIs), integrated development environments (IDEs), dynamic web pages (with different user-defined states), or interactive displays in general as in human-computer interaction, which gives a viewer the opportunity to actively change the stimulus content. Typically, for the analysis and visualization of time-varying visual attention paid to a web page, there is a big difference for the analytics and visualization approaches - algorithmically as well as visually - if the presented web page stimulus is static or dynamic, i.e. time-varying, or dynamic in the sense that user interaction is allowed. In this paper we discuss the challenges for visual analysis concepts in order to analyze the recorded data, in particular, with the goal to improve interactive stimuli, i.e., the layout of a web page, but also the interaction concept. We describe a data model which leads to interaction graphs, a possible way to analyze and visualize this kind of eye movement data.

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Cited By

Teng JWan FKong Y(2023)Exploring Enhancement of AR-HUD Visual Interaction Design Through Application of Intelligent AlgorithmsInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.32655816:2(1-24)Online publication date: 21-Jul-2023
https://doi.org/10.4018/IJITSA.326558
Tupikovskaja-Omovie Z(2022)Enhancing User Experience in Fashion m-Retail: Mapping Shopping User Journey Using Google Analytics, Eye Tracking Technology and Retrospective Think Aloud InterviewFashion Practice10.1080/17569370.2022.212946614:3(352-375)Online publication date: 11-Nov-2022
https://doi.org/10.1080/17569370.2022.2129466
Sultan MPopelka SStrobl J(2022)ET2Spatial – software for georeferencing of eye movement dataEarth Science Informatics10.1007/s12145-022-00832-515:3(2031-2049)Online publication date: 24-Jun-2022
https://doi.org/10.1007/s12145-022-00832-5
Show More Cited By

Index Terms

Interaction graphs: visual analysis of eye movement data from interactive stimuli
1. Human-centered computing
  1. Visualization
    1. Visualization techniques

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Published In

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

Conference Chairs:
Krzysztof Krejtz
SWPS University, Poland
,
Bonita Sharif
University of Nebraska-Lincoln

Copyright © 2019 ACM.

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Publication History

Published: 25 June 2019

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ETRA '19: 2019 Symposium on Eye Tracking Research and Applications

June 25 - 28, 2019

Colorado, Denver

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Cited By

Teng JWan FKong Y(2023)Exploring Enhancement of AR-HUD Visual Interaction Design Through Application of Intelligent AlgorithmsInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.32655816:2(1-24)Online publication date: 21-Jul-2023
https://doi.org/10.4018/IJITSA.326558
Tupikovskaja-Omovie Z(2022)Enhancing User Experience in Fashion m-Retail: Mapping Shopping User Journey Using Google Analytics, Eye Tracking Technology and Retrospective Think Aloud InterviewFashion Practice10.1080/17569370.2022.212946614:3(352-375)Online publication date: 11-Nov-2022
https://doi.org/10.1080/17569370.2022.2129466
Sultan MPopelka SStrobl J(2022)ET2Spatial – software for georeferencing of eye movement dataEarth Science Informatics10.1007/s12145-022-00832-515:3(2031-2049)Online publication date: 24-Jun-2022
https://doi.org/10.1007/s12145-022-00832-5
Bakardzhiev Hvan der Burgt MMartins Evan den Dool BJansen Cvan Scheppingen DWallner GBurch M(2021)A Web-Based Eye Tracking Data Visualization ToolPattern Recognition. ICPR International Workshops and Challenges10.1007/978-3-030-68796-0_29(405-419)Online publication date: 10-Jan-2021
https://dl.acm.org/doi/10.1007/978-3-030-68796-0_29

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