Real-time monitoring of swimming performance

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Delgado-Gonzalo, Ricard
Lemkaddem, Alia
Renevey, Philippe
Calvo, E. Muntane
Lemay, Mathieu
Cox, Kade
Ashby, Darren
Willardson, Jared
Bertschi, Mattia
This article presents the performance results of a novel algorithm for swimming analysis in real-time within a lowpower wrist-worn device. The estimated parameters are: lap count, stroke count, time in lap, total swimming time, pace/speed per lap, total swam distance, and swimming efficiency (SWOLF). In addition, several swimming styles are automatically detected. Results were obtained using a database composed of 13 different swimmers spanning 646 laps and 858.78 min of total swam time. The final precision achieved in lap detection ranges between 99.7% and 100%, and the classification of the different swimming styles reached a sensitivity and specificity above 98%. We demonstrate that a swimmers performance can be fully analyzed with the smart bracelet containing the novel algorithm. The presented algorithm has been licensed to ICON Health & Fitness Inc. for their line of wearables under the brand iFit.
Publication Reference
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL (USA), pp. 4743-4746