Publications:Improving the Quality of User Generated Data Sets for Activity Recognition

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Title Improving the Quality of User Generated Data Sets for Activity Recognition
Author Christopher Nugent and Jonathan Synnott and Celeste Gabrielli and Shuai Zhang and Macarena Espinilla and Alberto Calzada and Jens Lundström and Ian Cleland and Kare Synnes and Josef Hallberg and Susanna Spinsante and Miguel Angel Ortiz Barrios
Year 2016
PublicationType Conference Paper
Journal
HostPublication Ubiquitous Computing and Ambient Intelligence, UCAMI 2016, PT II
Conference 10th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI), NOV 29-DEC 02, 2016, San Bartolome de Tirajana, SPAIN
DOI http://dx.doi.org/10.1007/978-3-319-48799-1_13
Diva url http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1165293
Abstract It 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.