Regression-based analysis of front crawl swimming using upper-arm mounted accelerometers

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dc.contributor.authorDoheny, Emer P.-
dc.contributor.authorGoulding, Cathy-
dc.contributor.authorLowery, Madeleine M.-
dc.date.accessioned2020-02-13T12:58:29Z-
dc.date.available2020-02-13T12:58:29Z-
dc.date.copyright2019 IEEEen_US
dc.date.issued2019-07-27-
dc.identifier.isbn978-1-5386-1311-5/19-
dc.identifier.issn1094-687X-
dc.identifier.urihttp://hdl.handle.net/10197/11282-
dc.descriptionThe 41st International Engineering in Medicine and Biology Conference, Berlin, Germany, 23-27 July 2019en_US
dc.description.abstractWearable accelerometers can be used to quantify movement during swimming, enabling objective performance analysis. This study examined arm acceleration during front crawl swimming, and investigated how accelerometer-derived features change with lap times. Thirteen participants swam eight 50m laps using front crawl with a tri-axial accelerometer attached to each upper arm. Data were segmented into individual laps; lap times estimated and individual strokes extracted. Stroke times, root mean squared (RMS) acceleration, RMS jerk and spectral edge frequencies (SEF) were calculated for each stroke. Movement symmetry was assessed as the ratio of the minimum to maximum feature value for left and right arms. A regularized multivariate regression model was developed to estimate lap time using a subset of the accelerometer-derived features. Mean lap time was 56.99±11.99s. Fifteen of the 42 derived features were significantly correlated with lap time. The regression model included 5 features (stroke count, mean SEF of the X and Z axes, stroke count symmetry, and the coefficient of variation of stroke time symmetry) and estimated 50m lap time with a correlation coefficient of 0.86, and a cross-validated RMS error of 6.38s. The accelerometer-derived features and developed regression model may provide a useful tool to quantitatively evaluate swimming performance.en_US
dc.description.sponsorshipEuropean Research Councilen_US
dc.description.sponsorshipScience Foundation Irelanden_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.subjectStrokeen_US
dc.subjectSportsen_US
dc.subjectAccelerationen_US
dc.subjectAccelerometersen_US
dc.subjectSensorsen_US
dc.subjectFeature extractionen_US
dc.titleRegression-based analysis of front crawl swimming using upper-arm mounted accelerometersen_US
dc.typeConference Publicationen_US
dc.internal.authorcontactotheraoife.ogorman@ucd.ieen_US
dc.internal.webversionshttps://embc.embs.org/2019/-
dc.statusPeer revieweden_US
dc.identifier.doi10.1109/embc.2019.8857026-
dc.neeo.contributorDoheny|Emer P.|aut|-
dc.neeo.contributorGoulding|Cathy|aut|-
dc.neeo.contributorLowery|Madeleine M.|aut|-
dc.description.othersponsorshipInsight Research Centreen_US
dc.description.admin2020-02-13 JG: docx replaced with INSIGHT submitted PDFen_US
dc.date.updated2019-12-12T10:28:03Z-
dc.identifier.grantid12/RC/2289-
dc.identifier.grantidSFI/RC/2289-
dc.identifier.grantidERC-2014-CoG-646923-
item.grantfulltextopen-
item.fulltextWith Fulltext-
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