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

Juan Sebastian Casallas 1, 2 James H. Oliver 1 Jonathan W. Kelly 1 Frédéric Merienne 2 Samir Garbaya 2
1 ISU - Iowa State University
2 Le2i - Laboratoire Electronique, Informatique et Image [UMR6303]
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.
Keywords : Decision trees Interaction styles User interfaces Human computer interaction Predictive models Virtual reality Accuracy Three-dimensional displays Solid modeling
Type de document :
Communication dans un congrès
IEEE Virtual Reality, Mar 2014, Minneapolis, Minnesota, United States. IEEE, pp.51-56, 2014, 〈10.1109/VR.2014.6802050〉
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Juan Sebastian Casallas, James H. Oliver, Jonathan W. Kelly, Frédéric Merienne, Samir Garbaya. Using relative head and hand-target features to predict intention in 3D moving-target selection. IEEE Virtual Reality, Mar 2014, Minneapolis, Minnesota, United States. IEEE, pp.51-56, 2014, 〈10.1109/VR.2014.6802050〉. 〈hal-01133927〉

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