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Identification of Temporally Varying Areas of Interest in Long-Duration Eye-Tracking Data Sets
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. (Information Visualization)
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. (Information Visualization)ORCID iD: 0000-0003-4761-8601
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. (Information Visualization)
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Centre for Climate Science and Policy Research, CSPR. (Information Visualization)
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2018 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506Article in journal (Refereed) Published
Abstract [en]

Eye-tracking has become an invaluable tool for the analysis of working practices in many technological fields of activity. Typically studies focus on short tasks and use static expected areas of interest (AoI) in the display to explore subjects’ behaviour, making the analyst’s task quite straightforward. In long-duration studies, where the observations may last several hours over a complete work session, the AoIs may change over time in response to altering workload, emergencies or other variables making the analysis more difficult. This work puts forward a novel method to automatically identify spatial AoIs changing over time through a combination of clustering and cluster merging in the temporal domain. A visual analysis system based on the proposed methods is also presented. Finally, we illustrate our approach within the domain of air traffic control, a complex task sensitive to prevailing conditions over long durations, though it is applicable to other domains such as monitoring of complex systems. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018.
Keywords [en]
Eye-tracking data, areas of interest, clustering, minimum spanning tree, temporal data, spatio-temporal data
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:liu:diva-152714DOI: 10.1109/TVCG.2018.2865042ISI: 000452640000009PubMedID: 30183636Scopus ID: 2-s2.0-85052788669OAI: oai:DiVA.org:liu-152714DiVA, id: diva2:1263782
Note

Funding agencies: Swedish Research Council [2013-4939]; RESKILL project - Swedish Transport Administration; Swedish Maritime Administration; Swedish Air Navigation Service Provider LFV

Available from: 2018-11-16 Created: 2018-11-16 Last updated: 2019-01-07Bibliographically approved

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Vrotsou, KaterinaVitoria, AidaJohansson, JimmyCooper, Matthew

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IEEE Transactions on Visualization and Computer Graphics
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