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

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Title: Regression-based analysis of front crawl swimming using upper-arm mounted accelerometers
Authors: Doheny, Emer P.Goulding, CathyLowery, Madeleine M.
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Date: 27-Jul-2019
Online since: 2020-02-13T12:58:29Z
Abstract: Wearable 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.
Funding Details: European Research Council
Science Foundation Ireland
metadata.dc.description.othersponsorship: Insight Research Centre
Type of material: Conference Publication
Publisher: IEEE
Copyright (published version): 2019 IEEE
Keywords: StrokeSportsAccelerationAccelerometersSensorsFeature extraction
DOI: 10.1109/embc.2019.8857026
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Language: en
Status of Item: Peer reviewed
Conference Details: The 41st International Engineering in Medicine and Biology Conference, Berlin, Germany, 23-27 July 2019
ISBN: 978-1-5386-1311-5/19
Appears in Collections:Electrical and Electronic Engineering Research Collection
Insight Research Collection

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