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Using relative head and hand-target features to predict intention in 3D moving-target selection

Communication avec acte
Author
CASALLAS, Juan Sebastian
OLIVER, James H.
KELLY, Jonathan W.
ccMERIENNE, Frédéric
22594 Laboratoire Electronique, Informatique et Image [UMR6306] [Le2i]
ccGARBAYA, Samir

URI
http://hdl.handle.net/10985/9417
DOI
10.1109/VR.2014.6802050
Date
2014

Abstract

Selection of moving targets is a common, yet complex task in human-computer interaction (HCI) and virtual reality (VR). Predicting user intention may be beneficial to address the challenges inherent in interaction techniques for moving-target selection. This article extends previous models by integrating relative head-target and hand-target features to predict intended moving targets. The features are calculated in a time window ending at roughly two-thirds of the total target selection time and evaluated using decision trees. With two targets, this model is able to predict user choice with up to ~ 72% accuracy on general moving-target selection tasks and up to ~ 78% by also including task-related target properties.

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