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Open AccessFeature PaperArticle

SwimBIT: A Novel Approach to Stroke Analysis During Swim Training Based on Attitude and Heading Reference System (AHRS)

1
Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
2
IT—Instituto de Telecomunicações, 1049-001 Lisboa, Portugal
3
Department of Physical Performance, Norwegian School of Sport Sciences, 0863 Oslo, Norway
4
Luxembourg Institute of Research in Orthopedics, Sports Medicine and Science, 1460 Luxembourg, Luxembourg
*
Author to whom correspondence should be addressed.
Sports 2019, 7(11), 238; https://doi.org/10.3390/sports7110238
Received: 21 October 2019 / Revised: 14 November 2019 / Accepted: 14 November 2019 / Published: 16 November 2019
(This article belongs to the Special Issue Applied Sport Science for Elite Athletes)
In 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. View Full-Text
Keywords: swimming; training; performance; swimming analysis; inertial measurement units (IMU) swimming; training; performance; swimming analysis; inertial measurement units (IMU)
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MDPI and ACS Style

Ramos Félix, E.; da Silva, H.P.; Olstad, B.H.; Cabri, J.; Lobato Correia, P. SwimBIT: A Novel Approach to Stroke Analysis During Swim Training Based on Attitude and Heading Reference System (AHRS). Sports 2019, 7, 238.

AMA Style

Ramos Félix E, da Silva HP, Olstad BH, Cabri J, Lobato Correia P. SwimBIT: A Novel Approach to Stroke Analysis During Swim Training Based on Attitude and Heading Reference System (AHRS). Sports. 2019; 7(11):238.

Chicago/Turabian Style

Ramos Félix, Eduardo; da Silva, Hugo P.; Olstad, Bjørn H.; Cabri, Jan; Lobato Correia, Paulo. 2019. "SwimBIT: A Novel Approach to Stroke Analysis During Swim Training Based on Attitude and Heading Reference System (AHRS)" Sports 7, no. 11: 238.

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