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dc.contributor.authorGrijincu, Daniela
dc.contributor.authorNacenta, Miguel
dc.contributor.authorKristensson, Per Ola
dc.date.accessioned2014-11-28T10:02:26Z
dc.date.available2014-11-28T10:02:26Z
dc.date.issued2014-11-16
dc.identifier152716174
dc.identifier5f9ce4c1-ccc4-4018-9e40-18ec37489402
dc.identifier84919340632
dc.identifier.citationGrijincu , D , Nacenta , M & Kristensson , P O 2014 , User-defined interface gestures : dataset and analysis . in Proceedings of the 9th ACM International Conference on Interactive Tabletops and Surfaces (ITS 2014) . ACM , New York, NY , pp. 25-34 . https://doi.org/10.1145/2669485.2669511en
dc.identifier.isbn9781450325875
dc.identifier.otherORCID: /0000-0002-9864-9654/work/34034535
dc.identifier.urihttps://hdl.handle.net/10023/5841
dc.description.abstractWe present a video-based gesture dataset and a methodology for annotating video-based gesture datasets. Our dataset consists of user-defined gestures generated by 18 participants from a previous investigation of gesture memorability. We design and use a crowd-sourced classification task to annotate the videos. The results are made available through a web-based visualization that allows researchers and designers to explore the dataset. Finally, we perform an additional descriptive analysis and quantitative modeling exercise that provide additional insights into the results of the original study. To facilitate the use of the presented methodology by other researchers we share the data, the source of the human intelligence tasks for crowdsourcing, a new taxonomy that integrates previous work, and the source code of the visualization tool.
dc.format.extent10
dc.format.extent1998814
dc.format.extent102381470
dc.language.isoeng
dc.publisherACM
dc.relation.ispartofProceedings of the 9th ACM International Conference on Interactive Tabletops and Surfaces (ITS 2014)en
dc.subjectGesture designen
dc.subjectUser-defined gesturesen
dc.subjectGesture elicitationen
dc.subjectGesture analysis methodologyen
dc.subjectGesture annotationen
dc.subjectGesture memorabilityen
dc.subjectGesturesen
dc.subjectGesture datasetsen
dc.subjectCrowdsourcingen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subject.lccQA75en
dc.titleUser-defined interface gestures : dataset and analysisen
dc.typeConference itemen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.identifier.doi10.1145/2669485.2669511
dc.date.embargoedUntil2014-11-10
dc.identifier.urlhttp://udigesturesdataset.cs.st-andrews.ac.uk/en


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