Towards a Model for Predicting Intention in 3D Moving-Target Selection Tasks

Date
2013-01-01
Authors
Oliver, James
Casallas, Juan
Oliver, James
Kelly, Jonathan
Merienne, Frederic
Kelly, Jonathan
Garbaya, Samir
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Altmetrics
Authors
Research Projects
Organizational Units
Mechanical Engineering
Organizational Unit
Psychology
Organizational Unit
Journal Issue
Series
Department
Mechanical EngineeringPsychology
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.

Comments

This is a manuscript of an article from Lecture Notes in Computer Science 8019 (2013): 13, doi: 10.1007/978-3-642-39360-0_2. Posted with permission. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-39360-0_2.

Description
Keywords
Citation
DOI
Collections