Probabilistic Intentionality Prediction for Target Selection Based on Partial Cursor Tracks

  • Bashar I. Ahmad
  • Patrick M. Langdon
  • Pete Bunch
  • Simon J. Godsill
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8515)


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.


cursor movement target assistance intentionality prediction Bayesian inference 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Bashar I. Ahmad
    • 1
  • Patrick M. Langdon
    • 2
  • Pete Bunch
    • 1
  • Simon J. Godsill
    • 1
  1. 1.Signal Processing and Communications LaboratoryUniversity of CambridgeUK
  2. 2.Engineering Design Centre, Department of EngineeringUniversity of CambridgeUK

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