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dc.contributor.authorDelgado-Gonzalo, Ricard
dc.contributor.authorLemkaddem, Alia
dc.contributor.authorRenevey, Philippe
dc.contributor.authorCalvo, E. Muntane
dc.contributor.authorLemay, Mathieu
dc.contributor.authorCox, Kade
dc.contributor.authorAshby, Darren
dc.contributor.authorWillardson, Jared
dc.contributor.authorBertschi, Mattia
dc.identifier.citation2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL (USA), pp. 4743-4746
dc.description.abstractThis 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.
dc.titleReal-time monitoring of swimming performance
dc.typeProceedings Article
dc.type.csemresearchareasDigital Health

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