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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, p. 70-77Conference 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. p. 70-77
Keywords [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, id: diva2:1055232
Conference
6th Bi-Annual Workshop (BELIV)
Available from: 2016-12-12 Created: 2016-12-09 Last updated: 2019-11-25
In thesis
1. Data Abstraction and Pattern Identification in Time-series Data
Open this publication in new window or tab >>Data Abstraction and Pattern Identification in Time-series Data
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Data sources such as simulations, sensor networks across many application domains generate large volumes of time-series data which exhibit characteristics that evolve over time. Visual data analysis methods can help us in exploring and understanding the underlying patterns present in time-series data but, due to their ever-increasing size, the visual data analysis process can become complex. Large data sets can be handled using data abstraction techniques by transforming the raw data into a simpler format while, at the same time, preserving significant features that are important for the user. When dealing with time-series data, abstraction techniques should also take into account the underlying temporal characteristics.  

This thesis focuses on different data abstraction and pattern identification methods particularly in the cases of large 1D time-series and 2D spatio-temporal time-series data which exhibit spatiotemporal discontinuity. Based on the dimensionality and characteristics of the data, this thesis proposes a variety of efficient data-adaptive and user-controlled data abstraction methods that transform the raw data into a symbol sequence. The transformation of raw time-series into a symbol sequence can act as input to different sequence analysis methods from data mining and machine learning communities to identify interesting patterns of user behavior.  

In the case of very long duration 1D time-series, locally adaptive and user-controlled data approximation methods were presented to simplify the data, while at the same time retaining the perceptually important features. The simplified data were converted into a symbol sequence and a sketch-based pattern identification was then used to identify patterns in the symbolic data using regular expression based pattern matching. The method was applied to financial time-series and patterns such as head-and-shoulders, double and triple-top patterns were identified using hand drawn sketches in an interactive manner. Through data smoothing, the data approximation step also enables visualization of inherent patterns in the time-series representation while at the same time retaining perceptually important points.  

Very long duration 2D spatio-temporal eye tracking data sets that exhibit spatio-temporal discontinuity was transformed into symbolic data using scalable clustering and hierarchical cluster merging processes, each of which can be parallelized. The raw data is transformed into a symbol sequence with each symbol representing a region of interest in the eye gaze data. The identified regions of interest can also be displayed in a Space-Time Cube (STC) that captures both the temporal and contextual information. Through interactive filtering, zooming and geometric transformation, the STC representation along with linked views enables interactive data exploration. Using different sequence analysis methods, the symbol sequences are analyzed further to identify temporal patterns in the data set. Data collected from air traffic control officers from the domain of Air traffic control were used as application examples to demonstrate the results.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2019. p. 58
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2030
National Category
Media Engineering
Identifiers
urn:nbn:se:liu:diva-162220 (URN)10.3384/diss.diva-162220 (DOI)9789179299651 (ISBN)
Public defence
2019-12-13, Domteatern, Visualiseringscenter C, Kungsgatan 54, 60233 Norrköping, Norrköping, 09:15 (English)
Opponent
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Available from: 2019-11-25 Created: 2019-11-25 Last updated: 2019-11-25Bibliographically approved

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