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Visual Analytics Methods for Eye Tracking Data

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Human Centred Intelligent Systems

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 189))

Abstract

Nowadays, eye tracking data have become important and valuable information that help to understand the behavior of users. The gathering of these data is not an issue anymore. However, the problem is the analysis process and especially how can these raw data be converted to understandable and useful information. Visual analytics can solve this issue by combining human analytics skills and the advanced computer analytics. This leads to the novel discoveries and helps humans take control of the analytical process. These visualizations can be used to solve difficult problems by discovering new unknown patterns of available data. In this work, we discussed different methods that are used in the case of eye tracking data, and we addressed the challenges of visual analytics in this context.

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Author information

Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, K1N 6N5, Canada

    Nordine Quadar

  2. Department of Applied Sciences, University of Québec in Chicoutimi, Chicoutimi, QC, G7H 2B1, Canada

    Abdellah Chehri

  3. Department Embedded Systems Engineering, Incheon National University, Incheon, Korea

    Gwanggil Geon

Authors
  1. Nordine Quadar
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  2. Abdellah Chehri
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  3. Gwanggil Geon
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Corresponding author

Correspondence to Abdellah Chehri .

Editor information

Editors and Affiliations

  1. Faculty of Informatics, Reutlingen University, Reutlingen, Baden-Württemberg, Germany

    Alfred Zimmermann

  2. KES International and Bournemouth University, Shoreham-by-sea, UK

    Robert J. Howlett

  3. University of Technology Sydney, Sydney, NSW, Australia

    Lakhmi C. Jain

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Quadar, N., Chehri, A., Geon, G. (2021). Visual Analytics Methods for Eye Tracking Data. In: Zimmermann, A., Howlett, R., Jain, L. (eds) Human Centred Intelligent Systems. Smart Innovation, Systems and Technologies, vol 189. Springer, Singapore. https://doi.org/10.1007/978-981-15-5784-2_1

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