Finding the outliers in scanpath data

Michael Burch; Ayush Kumar; Klaus Mueller; Titus Kervezee; Wouter Nuijten; Rens Oostenbach; Lucas Peeters; Gijs Smit

doi:10.1145/3317958.3318225

Finding the outliers in scanpath data

Michael Burch, Ayush Kumar, Klaus Mueller, Titus Kervezee, Wouter Nuijten, Rens Oostenbach, Lucas Peeters, Gijs Smit

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

4 Citations (Scopus)

Abstract

In this paper, we describe the design of an interactive visualization tool for the comparison of eye movement data with a special focus on the outliers. In order to make the tool usable and accessible to anyone with a data science background, we provide a web-based solution by using the Dash library based on the Python programming language and the Python library Plotly. Interactive visualization is very well supported by Dash, which makes the visualization tool easy to use. We support multiple ways of comparing user scanpaths like bounding boxes and Jaccard indices to identify similarities. Moreover, we support matrix reordering to clearly separate the outliers in the scanpaths. We further support the data analyst by complementary views such as gaze plots and visual attention maps.

Original language	English
Title of host publication	Proceedings - ETRA 2019
Subtitle of host publication	2019 ACM Symposium On Eye Tracking Research and Applications
Editors	Stephen N. Spencer
Place of Publication	New York
Publisher	Association for Computing Machinery, Inc
Number of pages	5
ISBN (Electronic)	9781450367097
DOIs	https://doi.org/10.1145/3317958.3318225
Publication status	Published - 25 Jun 2019
Event	11th ACM Symposium on Eye Tracking Research and Applications, ETRA 2019 - Denver, United States Duration: 25 Jun 2019 → 28 Jun 2019

Conference

Conference	11th ACM Symposium on Eye Tracking Research and Applications, ETRA 2019
Country	United States
City	Denver
Period	25/06/19 → 28/06/19

Keywords

Eye tracking
Information visualization
Visual analytics

Access to Document

10.1145/3317958.3318225

Link to publication in Scopus

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Cite this

@inproceedings{635188020b134788b9f9d57dea900a57,

title = "Finding the outliers in scanpath data",

abstract = "In this paper, we describe the design of an interactive visualization tool for the comparison of eye movement data with a special focus on the outliers. In order to make the tool usable and accessible to anyone with a data science background, we provide a web-based solution by using the Dash library based on the Python programming language and the Python library Plotly. Interactive visualization is very well supported by Dash, which makes the visualization tool easy to use. We support multiple ways of comparing user scanpaths like bounding boxes and Jaccard indices to identify similarities. Moreover, we support matrix reordering to clearly separate the outliers in the scanpaths. We further support the data analyst by complementary views such as gaze plots and visual attention maps.",

keywords = "Eye tracking, Information visualization, Visual analytics",

author = "Michael Burch and Ayush Kumar and Klaus Mueller and Titus Kervezee and Wouter Nuijten and Rens Oostenbach and Lucas Peeters and Gijs Smit",

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booktitle = "Proceedings - ETRA 2019",

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Burch, M, Kumar, A, Mueller, K, Kervezee, T, Nuijten, W, Oostenbach, R, Peeters, L & Smit, G 2019, Finding the outliers in scanpath data. in SN Spencer (ed.), Proceedings - ETRA 2019: 2019 ACM Symposium On Eye Tracking Research and Applications., 83, Association for Computing Machinery, Inc, New York, 11th ACM Symposium on Eye Tracking Research and Applications, ETRA 2019, Denver, United States, 25/06/19. https://doi.org/10.1145/3317958.3318225

Finding the outliers in scanpath data. / Burch, Michael; Kumar, Ayush; Mueller, Klaus; Kervezee, Titus; Nuijten, Wouter; Oostenbach, Rens; Peeters, Lucas; Smit, Gijs.

Proceedings - ETRA 2019: 2019 ACM Symposium On Eye Tracking Research and Applications. ed. / Stephen N. Spencer. New York : Association for Computing Machinery, Inc, 2019. 83.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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