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

dc.contributor.author Oliver, James
dc.contributor.author Casallas, Juan
dc.contributor.author Oliver, James
dc.contributor.author Kelly, Jonathan
dc.contributor.author Merienne, Frederic
dc.contributor.author Kelly, Jonathan
dc.contributor.author Garbaya, Samir
dc.contributor.department Mechanical Engineering
dc.contributor.department Psychology
dc.date 2018-02-15T20:04:23.000
dc.date.accessioned 2020-06-30T06:03:17Z
dc.date.available 2020-06-30T06:03:17Z
dc.date.copyright Tue Jan 01 00:00:00 UTC 2013
dc.date.embargo 2014-01-01
dc.date.issued 2013-01-01
dc.description.abstract <p>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.</p>
dc.description.comments <p>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 <a href="http://dx.doi.org/10.1007/978-3-642-39360-0_2" target="_blank">http://dx.doi.org/10.1007/978-3-642-39360-0_2</a>.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/me_pubs/131/
dc.identifier.articleid 1135
dc.identifier.contextkey 6628831
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath me_pubs/131
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/54980
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/me_pubs/131/2013_Oliver_TowardsModel.pdf|||Fri Jan 14 19:44:17 UTC 2022
dc.source.uri 10.1007/978-3-642-39360-0_2
dc.subject.disciplines Computer-Aided Engineering and Design
dc.subject.disciplines Graphics and Human Computer Interfaces
dc.subject.keywords Department of Psychology
dc.subject.keywords Virtual Reality Application Center
dc.subject.keywords User intention
dc.subject.keywords prediction
dc.subject.keywords Fitts’ Law
dc.subject.keywords moving-target selection
dc.subject.keywords perceived difficulty
dc.subject.keywords decision trees
dc.subject.keywords virtual reality
dc.title Towards a Model for Predicting Intention in 3D Moving-Target Selection Tasks
dc.type article
dc.type.genre article
dspace.entity.type Publication
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