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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.date.accessioned2022-02-14T17:07:46Z
dc.date.available2022-02-14T17:07:46Z
dc.date.issued2016
dc.identifier.citation2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL (USA), pp. 4743-4746
dc.identifier.isbn978-1-4577-0220-4
dc.identifier.urihttps://yoda.csem.ch/handle/20.500.12839/650
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.csemdivisionsDiv-E
dc.type.csemresearchareasDigital Health
dc.identifier.urlhttp://ieeexplore.ieee.org/document/7591787/
dc.identifier.doi10.1109/EMBC.2016.7591787


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