Journal or Book Title
Lecture Notes in Computer Science
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.
Oliver, et al.
Casallas, Juan Sebastian; Oliver, James H.; Kelly, Jonathan W.; Merienne, Frederic; and Garbaya, Samir, "Towards a Model for Predicting Intention in 3D Moving-Target Selection Tasks" (2013). Mechanical Engineering Publications. 131.