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Improving the Quality of User Generated Data Sets for Activity Recognition
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. University of Ulster, Jordanstown, North Ireland.
University of Ulster, Jordanstown, North Ireland.
Marche Polytechnic University, Ancona, Italy.
University of Ulster, Jordanstown, North Ireland.
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2016 (English)In: Ubiquitous Computing and Ambient Intelligence, UCAMI 2016, PT II / [ed] Garcia, CR CaballeroGil, P Burmester, M QuesadaArencibia, A, Amsterdam: Springer Publishing Company, 2016, p. 104-110Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Amsterdam: Springer Publishing Company, 2016. p. 104-110
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10070
Keyword [en]
Activity recognition, Open data sets, Data validation, Data driven classification
National Category
Other Computer and Information Science Computer Sciences Media Engineering Computer Systems
Identifiers
URN: urn:nbn:se:hh:diva-35659DOI: 10.1007/978-3-319-48799-1_13ISI: 000389507400013Scopus ID: 2-s2.0-85009788304ISBN: 978-3-319-48799-1 (electronic)ISBN: 978-3-319-48798-4 (print)OAI: oai:DiVA.org:hh-35659DiVA: diva2:1165293
Conference
10th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI), NOV 29-DEC 02, 2016, San Bartolome de Tirajana, SPAIN
Available from: 2017-12-13 Created: 2017-12-13 Last updated: 2018-01-13Bibliographically approved

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Nugent, ChristopherLundström, Jens

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