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dc.contributor.authorJames, Danielen_US
dc.contributor.authorWixted, Andrewen_US
dc.contributor.editorAleksandar Subic, Franz Konstantin Fuss, Firoz Alam and Patrick Cliftonen_US
dc.date.accessioned2017-05-03T12:29:15Z
dc.date.available2017-05-03T12:29:15Z
dc.date.issued2011en_US
dc.date.modified2013-05-30T04:31:25Z
dc.identifier.issn1877-7058en_US
dc.identifier.doi10.1016/j.proeng.2011.05.113en_US
dc.identifier.urihttp://hdl.handle.net/10072/45843
dc.description.abstractThe treatment and handling of large quantities of time series sensor data is a particular challenge for the sport science community. Significant quantities of data can be generated during routine training sessions that involve multi-sensor monitoring on multiple limb segments. Whilst sensor devices are commonplace, the data formats, available sensors and acquisition rates vary considerably. Additionally sensor fusion is of increasing interest where multiple data sets from multiple sources are required to be combined. In this paper we present a set of tools that have been developed over the last 5 years to help meet this emerging challenge. The tools are based on the popular computational environment Matlab, which allows rapid customisation of routines, together with complex analysis and visualisation tools to be used by technical and non-technical researchers alike. Using these developed tools data gained from a variety of sources (including video) can be combined together, visualised and processed using over 50 processing and visualisation tools. The toolbox is designed to automatically annotate data sets to keep track of signal processing steps and maintain original data source integrity. It can also be easily extended and customised for individual applications. At its core is the 'athdata' data structure, which can accommodate multiple channels and kind of data at a variety of sample rates, annotations and unlimited processing steps. A sample import tool has been developed for users to easily apply the toolbox to their own data sets and real-time streaming of data into the toolbox is also possible. When adopted as a research team tool it facilitates the sharing of developed processing and visualisation steps, it also enables a faster path to application for new researchers joining any team.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.format.extent309939 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherElsevieren_US
dc.publisher.placeNetherlandsen_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofpagefrom451en_US
dc.relation.ispartofpageto456en_US
dc.relation.ispartofjournalProcedia Engineeringen_US
dc.relation.ispartofvolume13en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchHuman Movement and Sports Science not elsewhere classifieden_US
dc.subject.fieldofresearchcode110699en_US
dc.titleADAT: A Matlab toolbox for handling time series athlete performance dataen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.rights.copyrightCopyright 2011 Elsevier. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.en_US
gro.date.issued2011
gro.hasfulltextFull Text


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