Options
Regression-based analysis of front crawl swimming using upper-arm mounted accelerometers
Date Issued
2019-07-27
Date Available
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.
Sponsorship
European Research Council
Science Foundation Ireland
Other Sponsorship
Insight Research Centre
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
2019 IEEE
Web versions
Language
English
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
ISSN
1094-687X
This item is made available under a Creative Commons License
File(s)
Owning collection
Scopus© citations
0
Acquisition Date
Mar 28, 2024
Mar 28, 2024
Views
635
Acquisition Date
Mar 28, 2024
Mar 28, 2024
Downloads
414
Last Week
1
1
Last Month
11
11
Acquisition Date
Mar 28, 2024
Mar 28, 2024