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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)

Abstract

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.

Keywords

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

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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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