Circular heat map transition diagram

Computer Graphics Forum

Kurzhals

Raschke

et al. 2017

199

115

This survey provides an introduction into eye tracking visualization with an overview of existing techniques. Eye tracking is important for evaluating user behaviour. Analysing eye tracking data is typically done quantitatively, applying statistical methods. However, in recent years, researchers have been increasingly using qualitative and exploratory analysis methods based on visualization techniques. For this state‐of‐the‐art report, we investigated about 110 research papers presenting visualization techniques for eye tracking data. We classified these visualization techniques and identified two main categories: point‐based methods and methods based on areas of interest. Additionally, we conducted an expert review asking leading eye tracking experts how they apply visualization techniques in their analysis of eye tracking data. Based on the experts' feedback, we identified challenges that have to be tackled in the future so that visualizations will become even more widely applied in eye tracking research.

mentioning

confidence: 99%

Visualization of Eye Tracking Data: A Taxonomy and Survey

Computer Graphics Forum

Kurzhals

Raschke

et al. 2017

199

115

“…Table shows the current state‐of‐the‐art of AOI‐based eye movement visualizations in comparison to our approach according to the personal opinion and experience of the authors. A node‐link AOI graph [HHBL03, BRE13] is best applied to a single participant when analyzing dwell times and directed transitions. Using juxtaposition, this approach can to some degree also be used when comparing two or n participants.…”

Section: Design Considerationsmentioning

confidence: 99%

“…† Radial Transition Graph Comparison Tool: http://www.rtgct.fbeck.com/ Typically, a transition graph is visualized as a node-link diagram, either overlaid on top of a static stimulus [HHBL03] or plotted independently of the stimulus [TAK * 05]. Our approach extends circular heat map transition diagrams [BRE13], a node-link diagram in a radial layout encoding dwell times-the total time a participant focused on an AOI-in ring sector size and color. However, this approach has only been used to show single transition graphs.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Visual Comparison of Eye Movement Patterns

Computer Graphics Forum

Schweizer

Beck

et al. 2017

Self Cite

In eye tracking research, finding eye movement patterns and similar strategies between participants’ eye movements is important to understand task solving strategies and obstacles. In this application paper, we present a graph comparison method using radial graphs that show Areas of Interest (AOIs) and their transitions. An analyst investigates a single graph based on dwell times, directed transitions, and temporal AOI sequences. Two graphs can be compared directly and temporal changes may be analyzed. A list and matrix approach facilitate the analyst to contrast more than two graphs guided by visually encoded graph similarities. We evaluated our approach in case studies with three eye tracking and visualization experts. They identified temporal transition patterns of eye movements across participants, groups of participants, and outliers.

“…Transition matrices show transitions between AOIs in tabular form [Ponsoda et al 1995;Goldberg and Kotval 1999;Li et al 2012] and thus can be seen as a combination of visual and computational methods. Circular heat maps visualize information from transition matrices in a circular layout [Blascheck et al 2013]. If the analysis aims at a comparison of scan paths, these visualization techniques can be used in combination in order to study the temporal and spatial characteristics of scan paths.…”

Section: Related Workmentioning

confidence: 99%

A visual approach for scan path comparison

Raschke

Herr

Proceedings of the Symposium on Eye Tracking Research and Applications

et al. 2014

Self Cite

Several algorithms, approaches, and implementations have been developed to support comparison of scan paths and finding of interesting scan path structures. In this work we contribute a visual approach to support scan path comparison. A key feature of this approach is the combination of a clustering algorithm using Levenshtein distance with the parallel scan path visualization technique. The combination of computational methods with an interactive visualization allows us to use both the power of pattern finding algorithms and the human ability to visually recognize patterns. To use the concept in practice we implemented the approach in a prototype and show its application in two scan path analysis scenarios from automobile usability testing and visualization research.