Towards a Model for Predicting Intention in 3D Moving-Target Selection Tasks
dc.contributor.author | Casallas, Juan | |
dc.contributor.author | Oliver, James | |
dc.contributor.author | Merienne, Frederic | |
dc.contributor.author | Garbaya, Samir | |
dc.contributor.author | Kelly, Jonathan | |
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 | |
relation.isAuthorOfPublication | 5ccf5963-e33d-4d89-a5ce-f3d4fc78e115 | |
relation.isAuthorOfPublication | 24f11159-4819-4445-b0a4-dadee45766dc | |
relation.isOrgUnitOfPublication | 6d38ab0f-8cc2-4ad3-90b1-67a60c5a6f59 | |
relation.isOrgUnitOfPublication | 796236b3-85a0-4cde-b154-31da9e94ed42 |
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