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Supporting Exploration of Eye Tracking Data: Identifying Changing Behaviour Over Long Durations
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-4761-8601
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
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2016 (English)In: BEYOND TIME AND ERRORS: NOVEL EVALUATION METHODS FOR VISUALIZATION, BELIV 2016, ASSOC COMPUTING MACHINERY , 2016, 70-77 p.Conference paper, Published paper (Refereed)
Abstract [en]

Visual analytics of eye tracking data is a common tool for evaluation studies across diverse fields. In this position paper we propose a novel user-driven interactive data exploration tool for understanding the characteristics of eye gaze movements and the changes in these behaviours over time. Eye tracking experiments generate multidimensional scan path data with sequential information. Many mathematical methods in the past have analysed one or a few of the attributes of the scan path data and derived attributes such as Area of Interest (AoI), statistical measures, geometry, domain specific features etc. In our work we are interested in visual analytics of one of the derived attributes of sequential data-the: AoI and the sequences of visits to these AoIs over time. In the case of static stimuli, such as images, or dynamic stimuli, like videos, having predefined or fixed AoIs is not an efficient way of analysing scan path patterns. The AoI of a user over a stimulus may evolve over time and hence determining the AoIs dynamically through temporal clustering could be a better method for analysing the eye gaze patterns. In this work we primarily focus on the challenges in analysis and visualization of the temporal evolution of AoIs. This paper discusses the existing methods, their shortcomings and scope for improvement by adopting visual analytics methods for event-based temporal data to the analysis of eye tracking data.

Place, publisher, year, edition, pages
ASSOC COMPUTING MACHINERY , 2016. 70-77 p.
Keyword [en]
Eye tracking; pattern analysis; scan path; time evolving AoIs; Clustering of Fixations; ActiviTree
National Category
Media Engineering
Identifiers
URN: urn:nbn:se:liu:diva-133129DOI: 10.1145/2993901.2993905ISI: 000387865100009ISBN: 978-1-4503-4818-8 (print)OAI: oai:DiVA.org:liu-133129DiVA: diva2:1055232
Conference
6th Bi-Annual Workshop (BELIV)
Available from: 2016-12-12 Created: 2016-12-09 Last updated: 2016-12-12

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Citation style
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