Visual Data Cleansing of Low-Level Eye-Tracking Data

  • Christoph Schulz
  • Michael Burch
  • Fabian Beck
  • Daniel Weiskopf
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

Analysis and visualization of eye movement data from eye-tracking studies typically take into account gazes, fixations, and saccades of both eyes filtered and fused into a combined eye. Although this is a valid strategy, we argue that it is also worth investigating low-level eye-tracking data prior to high-level analysis, because today’s eye-tracking systems measure and infer data from both eyes separately. In this work, we present an approach that supports visual analysis and cleansing of low-level time-varying data for eye-tracking experiments. The visualization helps researchers get insights into the quality of the data in terms of its uncertainty, or reliability. We discuss uncertainty originating from eye tracking, and how to reveal it for visualization, using a comparative approach for disagreement between plots, and a density-based approach for accuracy in volume rendering. Finally, we illustrate the usefulness of our approach by applying it to eye movement data recorded with two state-of-the-art eye trackers.

Notes

Acknowledgements

We would like to thank the German Research Foundation (DFG) for financial support within project A01 of SFB/Transregio 161.

References

  1. 1.
    Ahrens, J., Geveci, B., Law, C.: ParaView: an end-user tool for large data visualization. Energy 836, 717–732 (2005)Google Scholar
  2. 2.
    Aigner, W., Miksch, S., Müller, W., Schumann, H., Tominski, C.: Visualizing time-oriented data—a systematic view. Comput. Graph. 31 (3), 401–409 (2007)CrossRefGoogle Scholar
  3. 3.
    Al-Rahayfeh, A., Faezipour, M.: Eye tracking and head movement detection: a state-of-art survey. Transl. Eng. Health Med. 1 (2013). http://ieeexplore.ieee.org/document/6656866/
  4. 4.
    Andersson, R., Nyström, M., Holmqvist, K.: Sampling frequency and eye-tracking measures: how speed affects durations, latencies, and more. J. Eye Mov. Res. 3 (3), 1–12 (2010)Google Scholar
  5. 5.
    Barz, M., Bulling, A., Daiber, F.: Computational modelling and prediction of gaze estimation error for head-mounted eye trackers (2015). https://www.d2.mpi-inf.mpg.de/content/computational-modelling-and-prediction-gaze-estimation-error-head-mounted-eye-trackers Google Scholar
  6. 6.
    Bavoil, L., Callahan, S.P., Crossno, P.J., Freire, J., Scheidegger, C.E., Silva, T., Vo, H.T.: VisTrails: enabling interactive multiple-view visualizations. In: Proceedings of IEEE Visualization, pp. 135–142 (2005)Google Scholar
  7. 7.
    Beard, K., Deese, H., Pettigrew, N.R.: A framework for visualization and exploration of events. Inf. Vis. 7 (2), 133–151 (2007)CrossRefGoogle Scholar
  8. 8.
    Blascheck, T., Kurzhals, K., Raschke, M., Burch, M., Weiskopf, D., Ertl, T.: State-of-the-art of visualization for eye tracking data. In: EuroVis STAR, pp. 63–82 (2014)Google Scholar
  9. 9.
    Blascheck, T., John, M., Koch, S., Kurzhals, K., Ertl, T.: VA 2: a visual analytics approach for evaluating visual analytics applications. IEEE Trans. Vis. Comput. Graph. 22 (1), 61–70 (2016)CrossRefGoogle Scholar
  10. 10.
    Bojko, A.: Informative or misleading? Heatmaps deconstructed. In: Jacko, J. (ed.) Human-Computer Interaction. New Trends. Lecture Notes in Computer Science, vol. 5610, pp. 30–39. Springer, Berlin/Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Brodlie, K., Allendes Osorio, R., Lopes, A.: A review of uncertainty in data visualization. In: Dill, J., Earnshaw, R., Kasik, D., Vince, J., Wong, P.C. (eds.) Expanding the Frontiers of Visual Analytics and Visualization, pp. 81–109. Springer, London (2012)Google Scholar
  12. 12.
    Cerrolaza, J.J., Villanueva, A., Villanueva, M., Cabeza, R.: Error characterization and compensation in eye tracking systems. In: Proceedings of the Symposium on Eye Tracking Research and Applications, ETRA’12, pp. 205–208 (2012)Google Scholar
  13. 13.
    Çöltekin, A., Fabrikant, S., Lacayo, M.: Exploring the efficiency of users’ visual analytics strategies based on sequence analysis of eye movement recordings. Int. J. Geogr. Inf. Sci. 24 (10), 1559–1575 (2010)CrossRefGoogle Scholar
  14. 14.
    Djurcilov, S., Kim, K., Lermusiaux, P.F.J., Pang, A.: Volume rendering data with uncertainty information. In: Proceedings of the Joint EUROGRAPHICS and IEEE TCVG Symposium on Visualization, pp. 243–252 (2001)Google Scholar
  15. 15.
    Gschwandtner, T., Aigner, W., Kaiser, K., Miksch, S., Seyfang, A.: CareCruiser: exploring and visualizing plans, events, and effects interactively. In: Proceedings of IEEE Pacific Visualization Symposium, PacificVis, pp. 43–50 (2011)Google Scholar
  16. 16.
    Gschwandtner, T., Aigner, W., Miksch, S., Gärtner, J., Kriglstein, S., Pohl, M., Suchy, N.: TimeCleanser: a visual analytics approach for data cleansing of time-oriented data. In: Proceedings of the 14th International Conference on Knowledge Technologies and Data-driven Business, i-KNOW’14, pp. 18:1–18:8 (2014)Google Scholar
  17. 17.
    Harrower, M., Brewer, C.A.: ColorBrewer.org: an online tool for selecting colour schemes for maps. Cartogr. J. 40 (1), 27–37 (2003)Google Scholar
  18. 18.
    Holmqvist, K., Nyström, M., Mulvey, F.: Eye tracker data quality: what it is and how to measure it. In: Proceedings of the Symposium on Eye Tracking Research and Applications, ETRA’12, pp. 45–52 (2012)Google Scholar
  19. 19.
    Hopf, M., Luttenberger Michael, M., Thomas, E.: Hierarchical splatting of scattered 4D data. IEEE Comput. Graph. Appl. 24 (4), 64–72 (2004)CrossRefGoogle Scholar
  20. 20.
    Kandel, S., Paepcke, A., Hellerstein, J., Heer, J.: Wrangler: interactive visual specification of data transformation scripts. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3363–3372 (2011)Google Scholar
  21. 21.
    Kurzhals, K., Weiskopf, D.: Space-time visual analytics of eye-tracking data for dynamic stimuli. IEEE Trans. Vis. Comput. Graph. 19 (12), 2129–2138 (2013)CrossRefGoogle Scholar
  22. 22.
    Mackworth, J.F., Mackworth, N.H.: Eye fixations recorded on changing visual scenes by the television eye-marker. J. Opt. Soc. Am. 48 (7), 439–445 (1958)CrossRefGoogle Scholar
  23. 23.
    Netzel, R., Burch, M., Weiskopf, D.: Interactive scanpath-oriented annotation of fixations. In: Proceedings of the Symposium on Eye Tracking Research and Applications, ETRA’16, pp. 183–187 (2016)Google Scholar
  24. 24.
    Rahm, E., Do, H.H.: Data cleaning: Problems and current approaches. IEEE Data Eng. Bull. 23 (4), 3–13 (2000)Google Scholar
  25. 25.
    Richardson, D.C., Dale, R.: Looking to understand: the coupling between speakers’ and listeners’ eye movements and its relationship to discourse comprehension. Cognit. Sci. 29 (6), 1045–1060 (2005)CrossRefGoogle Scholar
  26. 26.
    Rind, A., Aigner, W., Miksch, S., Wiltner, S., Pohl, M., Turic, T., Drexler, F.: Visual exploration of time-oriented patient data for chronic diseases: design study and evaluation. In: Information Quality in e-Health. Lecture Notes in Computer Science, vol. 7058, pp. 301–320. Springer, Berlin/New York (2011)Google Scholar
  27. 27.
    SensoMotoric Instruments GmbH: BeGaze 2.4 Manual (2010)Google Scholar
  28. 28.
    Shahar, Y., Goren-Bar, D., Boaz, D., Tahan, G.: Distributed, intelligent, interactive visualization and exploration of time-oriented clinical data and their abstractions. Artif. Intell. Med. 38 (2), 115–135 (2006)CrossRefGoogle Scholar
  29. 29.
    Singh, H., Singh, J.: Human eye tracking and related issues: a review. Int. J. Sci. Res. Publ. 2, 1–9 (2012)Google Scholar
  30. 30.
    Skeels, M., Lee, B., Smith, G., Robertson, G.G.: Revealing uncertainty for information visualization. Inf. Vis. 9 (1), 70–81 (2010)CrossRefGoogle Scholar
  31. 31.
    Stegmaier, S., Strengert, M., Klein, T., Ertl, T.: A simple and flexible volume rendering framework for graphics-hardware-based raycasting. In: Proceedings of the Fourth Eurographics/IEEE VGTC Conference on Volume Graphics, VG’05, pp. 187–195 (2005)Google Scholar
  32. 32.
    Tobii Technology: Tobii Studio 2.2 User Manual (2010)Google Scholar
  33. 33.
    Tobii Technology: Accuracy and Precision Test Report: Tobii T60 Eye Tracker (2011). 21 July 2011, Version: 2.1.1Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Christoph Schulz
    • 1
  • Michael Burch
    • 1
  • Fabian Beck
    • 1
  • Daniel Weiskopf
    • 1
  1. 1.Visualization Research CenterUniversity of StuttgartStuttgartGermany

Personalised recommendations