Skip to main content
Springer Nature Link
Log in

Visual Data Cleansing of Low-Level Eye-Tracking Data

  • Conference paper
  • First Online:
Eye Tracking and Visualization (ETVIS 2015)

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

Included in the following conference series:

  • 2231 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://github.com/schulzch/BinocularVis

References

  1. Ahrens, J., Geveci, B., Law, C.: ParaView: an end-user tool for large data visualization. Energy 836, 717–732 (2005)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. 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. Beard, K., Deese, H., Pettigrew, N.R.: A framework for visualization and exploration of events. Inf. Vis. 7 (2), 133–151 (2007)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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. 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. 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. 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. 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. Hopf, M., Luttenberger Michael, M., Thomas, E.: Hierarchical splatting of scattered 4D data. IEEE Comput. Graph. Appl. 24 (4), 64–72 (2004)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Rahm, E., Do, H.H.: Data cleaning: Problems and current approaches. IEEE Data Eng. Bull. 23 (4), 3–13 (2000)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. SensoMotoric Instruments GmbH: BeGaze 2.4 Manual (2010)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. Skeels, M., Lee, B., Smith, G., Robertson, G.G.: Revealing uncertainty for information visualization. Inf. Vis. 9 (1), 70–81 (2010)

    Article  Google Scholar 

  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. Tobii Technology: Tobii Studio 2.2 User Manual (2010)

    Google Scholar 

  33. Tobii Technology: Accuracy and Precision Test Report: Tobii T60 Eye Tracker (2011). 21 July 2011, Version: 2.1.1

    Google Scholar 

Download references

Acknowledgements

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

Author information

Authors and Affiliations

  1. Visualization Research Center, University of Stuttgart, Allmandring 19, 70569, Stuttgart, Germany

    Christoph Schulz, Michael Burch, Fabian Beck & Daniel Weiskopf

Authors
  1. Christoph Schulz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Burch
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fabian Beck
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniel Weiskopf
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christoph Schulz .

Editor information

Editors and Affiliations

  1. VISUS, University of Stuttgart, Stuttgart, Germany

    Michael Burch

  2. Max Planck Institute for Biological Cybernetics, Tübingen, Germany

    Lewis Chuang

  3. SIAT, Simon Fraser University, Surrey, British Columbia, Canada

    Brian Fisher

  4. VIS, University of Stuttgart, Stuttgart, Germany

    Albrecht Schmidt

  5. VISUS, University of Stuttgart, Stuttgart, Germany

    Daniel Weiskopf

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Schulz, C., Burch, M., Beck, F., Weiskopf, D. (2017). Visual Data Cleansing of Low-Level Eye-Tracking Data. In: Burch, M., Chuang, L., Fisher, B., Schmidt, A., Weiskopf, D. (eds) Eye Tracking and Visualization. ETVIS 2015. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-47024-5_12

Download citation

Publish with us

Policies and ethics