Document Type

Article

Publication Date

2013

Journal or Book Title

Lecture Notes in Computer Science

Volume

8019

First Page

13

Last Page

22

DOI

10.1007/978-3-642-39360-0_2

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 movingtarget 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.

Comments

This is a manuscript of an article from Lecture Notes in Computer Science 8019 (2013): 13, doi: 10.1007/978-3-642-39360-0_2. Posted with permission. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-39360-0_2.

Copyright Owner

Oliver, et al.

Language

en

File Format

application/pdf

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