Abstract:
This article presents the performance results of a novel algorithm for swimming analysis in real-time within a low-power wrist-worn device. The estimated parameters are: ...Show MoreMetadata
Abstract:
This article presents the performance results of a novel algorithm for swimming analysis in real-time within a low-power 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.
Published in: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Date of Conference: 16-20 August 2016
Date Added to IEEE Xplore: 18 October 2016
ISBN Information:
ISSN Information:
PubMed ID: 28325014
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