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dc.contributor.authorLee, James
dc.contributor.authorRowlands, David
dc.contributor.authorJackson, Nicholas
dc.contributor.authorLeadbetter, Raymond
dc.contributor.authorWada, Tomohito
dc.contributor.authorJames, Daniel A
dc.date.accessioned2017-07-03T00:59:16Z
dc.date.available2017-07-03T00:59:16Z
dc.date.issued2017
dc.identifier.issn1999-4893
dc.identifier.doi10.3390/a10010023
dc.identifier.urihttp://hdl.handle.net/10072/341142
dc.description.abstractThe increasing professionalism of sports persons and desire of consumers to imitate this has led to an increased metrification of sport. This has been driven in no small part by the widespread availability of comparatively cheap assessment technologies and, more recently, wearable technologies. Historically, whilst these have produced large data sets, often only the most rudimentary analysis has taken place (Wisbey et al in: “Quantifying movement demands of AFL football using GPS tracking”). This paucity of analysis is due in no small part to the challenges of analysing large sets of data that are often from disparate data sources to glean useful key performance indicators, which has been a largely a labour intensive process. This paper presents a framework that can be cloud based for the gathering, storing and algorithmic interpretation of large and inhomogeneous time series data sets. The framework is architecture based and technology agnostic in the data sources it can gather, and presents a model for multi set analysis for inter- and intra- devices and individual subject matter. A sample implementation demonstrates the utility of the framework for sports performance data collected from distributed inertial sensors in the sport of swimming.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherMDPI AG
dc.relation.ispartofpagefrom23-1
dc.relation.ispartofpageto23-14
dc.relation.ispartofissue1
dc.relation.ispartofjournalAlgorithms
dc.relation.ispartofvolume10
dc.subject.fieldofresearchMathematical sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchcode49
dc.subject.fieldofresearchcode40
dc.titleAn architectural based framework for the distributed collection, analysis and query from inhomogeneous time series data sets and wearables for biofeedback applications
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2017 The Author(s). Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY 4.0) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorRowlands, David D.


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