Scan path visualization and comparison using visual aggregation techniques

Peysakhovich, Vsevolod; Hurter, Christophe

doi:10.16910/jemr.10.5.9

Cited by 16 publications

(4 citation statements)

References 30 publications

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“…For example, J. H. Goldberg et al utilize the so-called "dotplot" method to find sequential matches of scanpaths [14]. V. Peysakhovich et al propose bundling the saccades and fixations based on geometrical features "to reduce visual clutter" by scanpath rendering "and provide a mathematical base for scanpath comparison" [15]. To the best of our knowledge, no published works address the usage of fuzzy sets for scanpath aggregation.…”

Section: Related Workmentioning

confidence: 99%

Eye Tracking Data Mining Based on Fuzzy Sets of Fixations

Ryabinin,

Erofeeva,

Guseva

2023

Fuzzy Systems and Data Mining IX

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This paper is devoted to the novel method of automated detection of common eye gaze movement patterns by mining the data recorded in eye-tracking-based experiments. For this, a model of aggregated scanpath is proposed that represents a fuzzy set of all possible eye gaze trajectories found in the experimental data. In contrast to the traditional methods of aggregation, no averaging is used to avoid information loss. Instead, the belonging function determines the probability of each particular trajectory. The constructed fuzzy scanpath is then filtered and automatically analyzed by applying methods of network science. For this, the fixations (eye gaze stops) are represented as network nodes and saccades (eye gaze jumps) are mapped to network links. For the network composed, modularity is calculated utilizing the Louvain method of community detection. In the case of eye gaze data, modularity represents saccadic cycles, which can be mapped to the cycles of cognitive processing. Thereby, the common perception structure is retrieved. To support all the analysis steps, we proposed corresponding scalable visualization tools based on our visual analytics platform SciVi. We demonstrate the viability of our approach by analyzing the data obtained from the real-world eye-tracking-based experiment from the Digital Humanities application domain. Preliminary experiment results are discussed along with the efficacy of the proposed methods.

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Section: Related Workmentioning

confidence: 99%

Eye Tracking Data Mining Based on Fuzzy Sets of Fixations

Ryabinin,

Erofeeva,

Guseva

2023

Fuzzy Systems and Data Mining IX

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Section: Related Workmentioning

confidence: 99%

“…There are a few studies for abstracted gaze motion visualizations [ 6 , 41 ]. Peysakhovich and Hunter [ 41 ] extract the fixations and saccades and perform saccade bundling in the saccade direction. They propose a color-encoded visualization of the saccade length, time stamp, and saccade direction into a bundled saccade link.…”

Section: Related Workmentioning

confidence: 99%

“…The difference between these two abstraction gaze movement visualizations is the use of temporal information. The visualization proposed by Peysakhovich and Hunter [ 41 ] focuses on the representation of the saccade directions rather than the distribution of the data over time, while the visualization proposed by Yoo et al [ 6 ] focuses on showing eye movements with the gaze distribution over time.…”

Section: Related Workmentioning

confidence: 99%

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Gaze Behavior Effect on Gaze Data Visualization at Different Abstraction Levels

Yoo

Jeong

Jang

2021

Sensors

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Many gaze data visualization techniques intuitively show eye movement together with visual stimuli. The eye tracker records a large number of eye movements within a short period. Therefore, visualizing raw gaze data with the visual stimulus appears complicated and obscured, making it difficult to gain insight through visualization. To avoid the complication, we often employ fixation identification algorithms for more abstract visualizations. In the past, many scientists have focused on gaze data abstraction with the attention map and analyzed detail gaze movement patterns with the scanpath visualization. Abstract eye movement patterns change dramatically depending on fixation identification algorithms in the preprocessing. However, it is difficult to find out how fixation identification algorithms affect gaze movement pattern visualizations. Additionally, scientists often spend much time on adjusting parameters manually in the fixation identification algorithms. In this paper, we propose a gaze behavior-based data processing method for abstract gaze data visualization. The proposed method classifies raw gaze data using machine learning models for image classification, such as CNN, AlexNet, and LeNet. Additionally, we compare the velocity-based identification (I-VT), dispersion-based identification (I-DT), density-based fixation identification, velocity and dispersion-based (I-VDT), and machine learning based and behavior-based modelson various visualizations at each abstraction level, such as attention map, scanpath, and abstract gaze movement visualization.

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Group Diagrams for simplified representation of scanpaths

Schäfer

Rodrigues

Weiskopf

et al. 2023

J Vis

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We instrument Group Diagrams (GDs) to reduce clutter in sets of eye-tracking scanpaths. Group Diagrams consist of trajectory subsets that cover, or represent, the whole set of trajectories with respect to some distance measure and an adjustable distance threshold. The original GDs allow for an application of various distance measures. We implement the GD framework and evaluate it on scanpaths that were collected by a former user study on public transit maps. We find that the Fréchet distance is the most appropriate measure to get meaningful results, yet it is flexible enough to cover outliers. We discuss several implementation-specific challenges and improve the scalability of the algorithm. To evaluate our results, we conducted a qualitative study with a group of eye-tracking experts. Finally, we note that our enhancements are also beneficial within the original problem setting, suggesting that our approach might be applicable to various types of input data. Graphical abstract Eye tracking on a public transit map of Warsaw. Input scanpaths (left), and simplified Group Diagram (right).

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Scan path visualization and comparison using visual aggregation techniques

Cited by 16 publications

References 30 publications

Eye Tracking Data Mining Based on Fuzzy Sets of Fixations

Eye Tracking Data Mining Based on Fuzzy Sets of Fixations

Gaze Behavior Effect on Gaze Data Visualization at Different Abstraction Levels

Group Diagrams for simplified representation of scanpaths

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