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 [UMR6306]

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.

Mots clés: Three-dimensional displays; Solid modeling; Human computer interaction; Predictive models; Virtual reality; Accuracy; Decision trees; Interaction styles; User interfaces

Domaines: Interface homme-machine [cs.HC]; Synthèse d'image et réalité virtuelle [cs.GR]

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. pp.51-56, ⟨10.1109/VR.2014.6802050⟩. ⟨hal-01133927⟩