Show simple item record

dc.creatorNugent, Chris D.
dc.creatorSynnott, Jonathan
dc.creatorGabrielli, Celeste
dc.creatorZhang, Shuai
dc.creatorEspinilla, Macarena
dc.creatorCalzada, Alberto
dc.creatorLundström, Jens
dc.creatorCleland, Ian
dc.creatorSynnes, Kåre
dc.creatorHallberg, Josef
dc.creatorSpinsante, Susanna
dc.creatorOrtiz Barrios, Miguel Angel
dc.date.accessioned2018-11-20T12:37:15Z
dc.date.available2018-11-20T12:37:15Z
dc.date.issued2016
dc.identifier.isbn978-331948798-4
dc.identifier.issn03029743
dc.identifier.urihttp://hdl.handle.net/11323/1387
dc.description.abstractIt is fully appreciated that progress in the development of data driven approaches to activity recognition are being hampered due to the lack of large scale, high quality, annotated data sets. In an effort to address this the Open Data Initiative (ODI) was conceived as a potential solution for the creation of shared resources for the collection and sharing of open data sets. As part of this process, an analysis was undertaken of datasets collected using a smart environment simulation tool. A noticeable difference was found in the first 1–2 cycles of users generating data. Further analysis demonstrated the effects that this had on the development of activity recognition models with a decrease of performance for both support vector machine and decision tree based classifiers. The outcome of the study has led to the production of a strategy to ensure an initial training phase is considered prior to full scale collection of the data.spa
dc.language.isoengeng
dc.rightsAtribución – No comercial – Compartir igualeng
dc.subjectActivity recognitioneng
dc.subjectData driven classificationeng
dc.subjectData validationeng
dc.subjectOpen data setseng
dc.titleImproving the quality of user generated data sets for activity recognitioneng
dc.typeArticleeng
dc.identifier.doi10.1007/978-3-319-48799-1_13


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record