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. KeywordsDepartment of Psychology, Virtual Reality Application Center, User intention, prediction, Fitts' Law, movingtarget selection, perceived difficulty, decision trees, virtual reality 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.

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Science Arts & Métiers (SAM)is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. ABSTRACTSelection 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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