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dc.contributor.authorFelix, Eduardo Ramos
dc.contributor.authorda Silva, Hugo Plácido
dc.contributor.authorOlstad, Bjørn Harald
dc.contributor.authorCabri, Jan
dc.contributor.authorCorreia, Paulo Lobato
dc.date.accessioned2020-05-12T08:53:22Z
dc.date.available2020-05-12T08:53:22Z
dc.date.created2020-01-10T11:14:14Z
dc.date.issued2019
dc.identifier.citationSports. 2019, 7(11), 238.en_US
dc.identifier.issn2075-4663
dc.identifier.urihttps://hdl.handle.net/11250/2654017
dc.descriptionThis is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.description.abstractIn a world where technology is assuming a pervasive role, sports sciences are also increasingly exploiting the possibilities opened by advanced sensors and intelligent algorithms. This paper focuses on the development of a convenient, practical, and low-cost system, SwimBIT, which is intended to help swimmers and coaches in performance evaluation, improvement, and injury reduction. Real-world data were collected from 13 triathletes (age 20.8 ± 3.5 years, height 173.7 ± 5.3 cm, and weight 63.5 ± 6.3 kg) with different skill levels in performing the four competitive styles of swimming in order to develop a representative database and allow assessment of the system’s performance in swimming conditions. The hardware collects a set of signals from swimmers based on an attitude and heading reference system (AHRS), and a machine learning workflow for data analysis is used to extract a selection of indicators that allows analysis of a swimmer’s performance. Based on the AHRS data, three novel indicators are proposed: trunk elevation, body balance, and body rotation. Experimental evaluation has shown promising results, with a 100% accuracy in swim lap segmentation, a precision of 100% in the recognition of backstroke, and a precision of 89.60% in the three remaining swimming techniques (butterfly, breaststroke, and front crawl). The performance indicators proposed here provide valuable information for both swimmers and coaches in their quest for enhancing performance and preventing injuries.en_US
dc.language.isoengen_US
dc.subjectswimmingen_US
dc.subjecttrainingen_US
dc.subjectperformanceen_US
dc.subjectswimming analysisen_US
dc.subjectinertial measurement units (IMU)en_US
dc.titleSwimBIT: A Novel Approach to Stroke Analysis During Swim Training Based on Attitude and Heading Reference System (AHRS)en_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber11en_US
dc.source.volume7en_US
dc.source.journalSportsen_US
dc.source.issue11en_US
dc.identifier.doi10.3390/sports7110238
dc.identifier.cristin1770092
dc.description.localcodeSeksjon for fysisk prestasjonsevne / Department of Physical Performanceen_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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