Abstract
Pointing tasks, for example to select an object in an interface, constitute a significant part of human-computer interactions. This motivated several studies into techniques that facilitate the pointing task and improve its accuracy. In this paper, we introduce a number of intentionality prediction algorithms to determine the intended target a priori from partial cursor tracks. They yield notable reductions in the pointing time, aid effective selection assistance routines and enhance the overall pointing accuracy. A number of benchmark prediction models are also restated within a statistical framework and their probabilistic interpretation is utilised to calculate their corresponding outcomes. The relative performance of all considered predictors is assessed for point-click task data sets pertaining to both able-bodied and impaired users. Bayesian adaptive filtering is deployed to smooth highly perturbed mouse cursor tracks that are typically produced by motor impaired users undertaking a pointing task.
Chapter PDF
Similar content being viewed by others
References
Oirschot, H.K.-V., Houtsma, A.J.M.: Cursor Trajectory Analysis. In: Brewster, S., Murray-Smith, R. (eds.) Haptic HCI 2000. LNCS, vol. 2058, pp. 127–134. Springer, Heidelberg (2001)
Bateman, S., Mandryk, R.L., Xiao, R., Gutwin, C.: Analysis and comparison of target assistance techniques for relative ray-cast pointing. International Journal of Human-Computer Studies, 511–532 (2013)
Fitts, P.M., Peterson, J.R.: Information capacity of discrete motor responses. J. Exp. Psych. 67, 103–112 (1964)
Mackenzie, I.S.: Fitts’ law as a research and design tool in human-computer interaction. Journal of Human Computer Interaction 7, 91–139 (1992)
Kopper, R., Bowman, D.A., Silva, M.G., McMahan, R.P.: A human motor behavior model for distal pointing tasks. Int. J. of Human Computer Studies 68, 603–615 (2010)
Meyer, D.E., Smith, J.E., Kornblum, S., Abrams, R.A., Wright, C.E.: Optimality in human motor performance: ideal control of rapid aimed movements. Psychological Review 8, 340–370 (1988)
McGuffin, M.J., Balakrishnan, R.: Fitts’s law and expanding targets: Experimental studies and designs for user interfaces. ACM Transactions Computer-Human Interaction 4, 388–422 (2005)
Lane, D.M., Peres, S.C., Sándor, A., Napier, A.H.A.: A Process for Anticipating and Executing Icon Selection in Graphical User Interfaces. International Journal of Human Computer Interaction 2, 243–254 (2005)
Wobbrock, J.O., Fogarty, J., Liu, S., Kimuro, S., Harada, S.: The Angle Mouse: Target-Agnostic Dynamic Gain Adjustment Based on Angular Deviation. In: Proc. of the 27th International Conference on Human Factors in Computing Systems, New York, pp. 1401–1410 (2009)
Murata, A.: Improvement of pointing time by predicting targets in pointing with a PC mouse. International Journal of Human Computer Studies 10, 23–32 (2005)
Asano, T., Sharlin, E., Kitamura, Y., Takashima, K., Kishino, F.: Predictive interaction using the Delphian desktop. In: Proc. of the 186th Annual ACM Smp. on User Interface Software and Technology (UIST 2005), New York, pp. 133–141 (2005)
Lank, E., Cheng, Y.N., Ruiz, J.: Endpoint prediction using motion kinematicst. In: Proc. of the SIGCHI Conference on Human Factors in Computing System, NY, pp. 637–646 (2007)
Keates, S., Hwang, F., Langdon, P., Clarkson, P.J., Robinson, P.: Cursor measures for motion-impaired computer users. In: Proc. of the Fifth International ACM Conference on Assistive Technologies – ASSETS, New York, pp. 135–142 (2002)
Ziebart, B., Dey, A., Bagnell, J.A.: Probabilistic pointing target prediction via inverse optimal control. In: Proc. of the ACM Int. Conf. on Intelligent User Interfaces, pp. 1–10 (2012)
Haug, A.: Bayesian Estimation and Tracking: A Practical Guide. John Wiley & Sons (2012)
Meucci, A.: Review of Statistical Arbitrage, Cointegration, and Multivariate Ornstein-Uhlenbeck. SSRN Preprint 1404905 (2010)
Godsill, S.J., Vermaak, J., Ng, W., Li, J.: Models and algorithms for tracking of maneuvering objects using variable rate particle filters. Proc. of IEEE 95, 925–952 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Ahmad, B.I., Langdon, P.M., Bunch, P., Godsill, S.J. (2014). Probabilistic Intentionality Prediction for Target Selection Based on Partial Cursor Tracks. In: Stephanidis, C., Antona, M. (eds) Universal Access in Human-Computer Interaction. Aging and Assistive Environments. UAHCI 2014. Lecture Notes in Computer Science, vol 8515. Springer, Cham. https://doi.org/10.1007/978-3-319-07446-7_42
Download citation
DOI: https://doi.org/10.1007/978-3-319-07446-7_42
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07445-0
Online ISBN: 978-3-319-07446-7
eBook Packages: Computer ScienceComputer Science (R0)