A Bayesian Approach for Pervasive Estimation of Breaststroke Velocity Using a Wearable IMU

An ubiquitous assessment of swimming velocity (main metric of the performance) is essential for the coach to provide a tailored feedback to the trainee. We present a probabilistic framework for the data-driven estimation of the swimming velocity at every cycle using a low-cost wearable inertial measurement unit (IMU). The statistical validation of the method on 15 swimmers shows that an average relative error of 0.1±9.6 % and high correlation with the tethered reference system (rX,Y = 0.91) is achievable. Besides, a simple tool to analyze the influence of sacrum kinematics on the performance is provided.


Published in:
Pervasive and Mobile Computing, 19, 37-46
Year:
2015
Publisher:
Amsterdam, Elsevier Science Bv
Keywords:
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 Record created 2014-03-03, last modified 2018-09-13

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