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Summary

Wearable accelerometers objectively measure swimming performance by analyzing arm acceleration. This technology can accurately estimate lap times using key movement metrics, enhancing swim training.

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Area of Science:

Sports Science
Biomechanics
Wearable Technology

Background:

Objective swimming performance analysis is crucial for training.
Wearable accelerometers offer a method for quantifying movement.
Previous research has explored accelerometry in swimming, but comprehensive lap time prediction models are limited.

Purpose of the Study:

To examine arm acceleration during front crawl swimming.
To investigate the relationship between accelerometer-derived features and lap times.
To develop a regression model for estimating 50m lap times using accelerometer data.

Main Methods:

Thirteen swimmers performed 50m front crawl laps with tri-axial accelerometers on each upper arm.
Data were segmented into laps and strokes to calculate stroke time, root mean squared (RMS) acceleration, RMS jerk, and spectral edge frequencies (SEF).
Movement symmetry and a multivariate regression model were employed to correlate features with lap times.

Main Results:

Fifteen of 42 accelerometer-derived features significantly correlated with lap time.
A regression model using 5 features (stroke count, mean SEF X/Z axes, stroke count symmetry, coefficient of variation of stroke time symmetry) accurately estimated lap time (R=0.86).
The model achieved a cross-validated RMS error of 6.38s for 50m lap time prediction.

Conclusions:

Accelerometer-derived features provide quantitative insights into swimming performance.
The developed regression model offers a reliable tool for objective lap time estimation in front crawl swimming.
Wearable accelerometers can enhance swimming training and performance analysis.