Towards a Model for Predicting Intention in 3D Moving-Target Selection Tasks

  • Juan Sebastián Casallas
  • James H. Oliver
  • Jonathan W. Kelly
  • Frédéric Merienne
  • Samir Garbaya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8019)


Novel interaction techniques have been developed to address the difficulties of selecting moving targets. However, similar to their static-target counterparts, these techniques may suffer from clutter and overlap, which can be addressed by predicting intended targets. Unfortunately, current predictive techniques are tailored towards static-target selection. Thus, a novel approach for predicting user intention in moving-target selection tasks using decision-trees constructed with the initial physical states of both the user and the targets is proposed. This approach is verified in a virtual reality application in which users must choose, and select between different moving targets. With two targets, this model is able to predict user choice with approximately 71% accuracy, which is significantly better than both chance and a frequentist approach.


User intention prediction Fitts’ Law moving-target selection perceived difficulty decision trees virtual reality 


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  1. 1.
    Al Hajri, A., Fels, S., Miller, G., Ilich, M.: Moving target selection in 2D graphical user interfaces. In: Campos, P., Graham, N., Jorge, J., Nunes, N., Palanque, P., Winckler, M. (eds.) INTERACT 2011, Part II. LNCS, vol. 6947, pp. 141–161. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Bailenson, J.N., Shum, M.S., Uttal, D.H.: The initial segment strategy: a heuristic for route selection. Memory & Cognition 28(2), 306–318 (2000)CrossRefGoogle Scholar
  3. 3.
    Christenfeld, N.: Choices from identical options. Psychological Science 6(1), 50–55 (1995)CrossRefGoogle Scholar
  4. 4.
    Fitts, P.M.: The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology: General 121(3), 262–269 (1954)CrossRefGoogle Scholar
  5. 5.
    Grilli, S.M.: Perceived Difficulty in a Fitts Task. PhD thesis, Cleveland State University (2011)Google Scholar
  6. 6.
    Guiard, Y., Beaudouin-Lafon, M.: Fitts’ law 50 years later: applications and contributions from human-computer interaction. International Journal of Human-Computer Studies 61(6), 747–750 (2004)CrossRefGoogle Scholar
  7. 7.
    Hall, M., National, H., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations Newsletter 11(1), 10–18 (2009)CrossRefGoogle Scholar
  8. 8.
    Hasan, K., Grossman, T., Irani, P.: Comet and Target Ghost: Techniques for Selecting Moving Targets. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2011, Vancouver, BC, Canada, pp. 839–848. ACM (2011)Google Scholar
  9. 9.
    Hoffmann, E.R.: Capture of moving targets: a modification of Fitts’ Law. Ergonomics 34(2), 211–220 (1991)CrossRefGoogle Scholar
  10. 10.
    Jagacinski, R.J., Repperger, D.W., Ward, S.L., Moran, M.S.: A Test of Fitts’ Law with Moving Targets. Human Factors: The Journal of the Human Factors and Ergonomics Society 22(2), 225–233 (1980)Google Scholar
  11. 11.
    Jorke, H., Simon, A., Fritz, M.: Advanced Stereo Projection Using Interference Filters. In: 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video, Istanbul, Turkey, pp. 177–180. IEEE (2008)Google Scholar
  12. 12.
    Kourtis, D., Sebanz, N., Knoblich, G.: EEG correlates of Fitts’s law during preparation for action. Psychological Research 76(4), 514–524 (2012)CrossRefGoogle Scholar
  13. 13.
    Lank, E., Cheng, Y.-C.N., Ruiz, J.: Endpoint prediction using motion kinematics. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2007, San Jose, CA, USA, pp. 637–646. ACM (2007)Google Scholar
  14. 14.
    MacKenzie, I.S.: A Note on the Information-Theoretic Basis for Fitts’ Law. Journal of Motor Behavior 21(3), 323–330 (1989)CrossRefMathSciNetGoogle Scholar
  15. 15.
    McGuffin, M.J., Balakrishnan, R.: Fitts’ law and expanding targets: Experimental studies and designs for user interfaces. ACM Transactions on Computer-Human Interaction (TOCHI) 12(4), 388–422 (2005)CrossRefGoogle Scholar
  16. 16.
    Mitchell, T.M.: Machine learning. McGraw-Hill, Boston (1997)zbMATHGoogle Scholar
  17. 17.
    Noy, D.: Predicting user intentions in graphical user interfaces using implicit disambiguation. In: CHI 2001 Extended Abstracts on Human Factors in Computing Systems, Seattle, Washington, USA, pp. 455–456. ACM (2001)Google Scholar
  18. 18.
    Pavlik, R.A., Vance, J.M.: VR JuggLua: A framework for VR applications combining Lua, OpenSceneGraph, and VR Juggler. In: 2012 5th Workshop on Software Engineering and Architectures for Realtime Interactive Systems (SEARIS), Singapore, pp. 29–35. IEEE (2012)Google Scholar
  19. 19.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)Google Scholar
  20. 20.
    Slifkin, A.B., Grilli, S.M.: Aiming for the future: prospective action difficulty, prescribed difficulty, and Fitts’ law. Experimental Brain Research 174(4), 746–753 (2006)CrossRefGoogle Scholar
  21. 21.
    Wonner, J., Grosjean, J., Capobianco, A., Bechmann, D.: SPEED: Prédiction de cibles. In: 23rd French Speaking Conference on Human-Computer Interaction, IHM 2011, Sophia Antipolis, France, pp. 19:1–19:4. ACM (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Juan Sebastián Casallas
    • 1
    • 3
  • James H. Oliver
    • 1
  • Jonathan W. Kelly
    • 1
    • 2
  • Frédéric Merienne
    • 3
  • Samir Garbaya
    • 3
  1. 1.Virtual Reality Applications CenterIowa State UniversityAmesUSA
  2. 2.Department of PsychologyIowa State UniversityAmesUSA
  3. 3.Institut Image, Arts et Métiers ParisTechChalon-sur-SaôneFrance

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