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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)

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.

Keywords

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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