Abstract
Visualizing eye tracking data can provide insights in many research fields. However, visualizing such data efficiently and cost-effectively is challenging without well-designed tools. Easily accessible web-based approaches equipped with intuitive and interactive visualizations offer to be a promising solution. Many of such tools already exist, however, they mostly use one specific visualization technique. In this paper, we describe a web application which uses a combination of different visualization methods for eye tracking data. The visualization techniques are interactively linked to provide several perspectives on the eye tracking data. We conclude the paper by discussing challenges, limitations, and future work.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bailly-Salins, I., Luga, H.: Artistic 3D object creation using artificial life paradigms. In: Butz, A., Fisher, B., Krüger, A., Olivier, P., Owada, S. (eds.) SG 2007. LNCS, vol. 4569, pp. 135–145. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73214-3_12
Blascheck, T., Kurzhals, K., Raschke, M., Burch, M., Weiskopf, D., Ertl, T.: Visualization of eye tracking data: a taxonomy and survey: visualization of eye tracking data. Comput. Graph. Forum (2017). https://doi.org/10.1111/cgf.13079
Blignaut, P.J.: Visual span and other parameters for the generation of heatmaps. In: Morimoto, C.H., Istance, H.O., Hyrskykari, A., Ji, Q. (eds.) Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications, ETRA 2010, Austin, Texas, USA, 22-24 March 2010, pp. 125–128. ACM (2010). https://doi.org/10.1145/1743666.1743697
Bojko, A.A.: Informative or misleading? Heatmaps deconstructed. In: Jacko, J.A. (ed.) HCI 2009. LNCS, vol. 5610, pp. 30–39. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02574-7_4
Burch, M.: Time-preserving visual attention maps. In: Proceedings of Intelligent Decision Technologies, pp. 273–283 (2016)
Burch, M.: Interaction graphs: visual analysis of eye movement data from interactive stimuli. In: Krejtz, K., Sharif, B. (eds.) Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications, ETRA 2019, pp. 89:1–89:5. ACM (2019). https://doi.org/10.1145/3317960.3321617
Burch, M., Kumar, A., Mueller, K.: The hierarchical flow of eye movements. In: Proceedings of the 3rd Workshop on Eye Tracking and Visualization, ETVIS, pp. 3:1–3:5. ACM (2018)
Burch, M., Kumar, A., Timmermans, N.: An interactive web-based visual analytics tool for detecting strategic eye movement patterns. In: Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications (2019)
Burch, M., Netzel, R., Ohlhausen, B., Woods, R., Weiskopf, D.: User performance and reading strategies for metro maps: an eye tracking study. Spl Issue Eye Track. Spat. Res. Spat. Cogn. Comput. Interdisc. J. 17 (2016). https://doi.org/10.1080/13875868.2016.1226839
Burch, M., Timmermans, N.: Sankeye: a visualization technique for AOI transitions. In: Proceedings of the Symposium on Eye Tracking Research and Applications, ETRA, pp. 48:1–48:5. ACM (2020)
Burch, M., Veneri, A., Sun, B.: Eyeclouds: a visualization and analysis tool for exploring eye movement data. In: Proceedings of the 12th International Symposium on Visual Information Communication and Interaction. Association for Computing Machinery, New York (2019)
Duchowski, A.T., Price, M.M., Meyer, M.D., Orero, P.: Aggregate gaze visualization with real-time heatmaps. In: Morimoto, C.H., Istance, H.O., Spencer, S.N., Mulligan, J.B., Qvarfordt, P. (eds.) Proceedings of the 2012 Symposium on Eye-Tracking Research and Applications, ETRA 2012, Santa Barbara, CA, USA, 28–30 March 2012, pp. 13–20. ACM (2012)
Fuchs, A., Kaneko, C., Scudder, C.: Brainstem control of saccadic eye movements. Ann. Rev. Neurosci. 8, 307–337 (1985). https://doi.org/10.1146/annurev.ne.08.030185.001515
Fukunaga, K., Hostetler, L.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. Inf. Theory 21(1), 32–40 (1975)
Hessels, R.S., Kemner, C., van den Boomen, C., Hooge, I.T.C.: The area-of-interest problem in eyetracking research: a noise-robust solution for face and sparse stimuli. Behav. Res. Methods 48(4), 1694–1712 (2015). https://doi.org/10.3758/s13428-015-0676-y
Holmqvist, K.: Eye Tracking: A Comprehensive Guide to Methods and Measures. Oxford University Press, Oxford (2011)
Lohse, G.L.: Consumer eye movement patterns on yellow pages advertising. J. Advertising 26(1), 61–73 (1997)
Malzer, C., Baum, M.: A hybrid approach to hierarchical density-based cluster selection (2019)
Mather, G.: Foundations of Sensation and Perception. Psychology Press, London (2009)
Matlin, M., Farmer, T.: Cognition. Wiley, New York (2017)
Muñoz-Leiva, F., Hernández-Méndez, J., Gómez-Carmona, D.: Measuring advertising effectiveness in travel 2.0 websites through eye-tracking technology. Physiol. Behav. 200, 83–95 (2019)
Nyström, M., Holmqvist, K.: An adaptive algorithm for fixation, saccade, and glissade detection in eyetracking data. Behav. Res. Methods 42, 188–204 (2010)
Peysakhovich, V., Hurter, C., Telea, A.: Attribute-driven edge bundling for general graphs with applications in trail analysis. In: Liu, S., Scheuermann, G., Takahashi, S. (eds.) Proceedings of IEEE Pacific Visualization Symposium, PacificVis, pp. 39–46. IEEE Computer Society (2015)
Rosenbaum, D.: Human Motor Control. Elsevier Science, New York (2009)
Spakov, O., Miniotas, D.: Visualization of eye gaze data using heat maps. In: Electronics and Electrical Engineering 2, vol. 74, pp. 55–58 (2007)
Yi, J.S., Kang, Y., Stasko, J., Jacko, J.A.: Toward a deeper understanding of the role of interaction in information visualization. IEEE Trans. Visual. Comput. Graph. 13(6), 1224–1231 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Bakardzhiev, H. et al. (2021). A Web-Based Eye Tracking Data Visualization Tool. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12663. Springer, Cham. https://doi.org/10.1007/978-3-030-68796-0_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-68796-0_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68795-3
Online ISBN: 978-3-030-68796-0
eBook Packages: Computer ScienceComputer Science (R0)
