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Framework for Intelligent Swimming Analytics with Wearable Sensors for Stroke Classification

2021-07-30Artigo científico Acesso aberto
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datacite.subject.fosCiências Naturais::Ciências Físicas
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.fosCiências Naturais::Ciências Químicas
datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.fosCiências Naturais::Ciências Biológicas
datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg11:Cidades e Comunidades Sustentáveis
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorCosta, Joana
dc.contributor.authorSilva, Catarina
dc.contributor.authorSantos, Miguel
dc.contributor.authorFernandes, Telmo
dc.contributor.authorFaria, Sérgio
dc.date.accessioned2026-02-18T18:29:05Z
dc.date.available2026-02-18T18:29:05Z
dc.date.issued2021-07-30
dc.descriptionSantos, Miguel - Scopus ID: 57213610002
dc.description.abstractIntelligent approaches in sports using IoT devices to gather data, attempting to optimize athlete’s training and performance, are cutting edge research. Synergies between recent wearable hardware and wireless communication strategies, together with the advances in intelligent algorithms, which are able to perform online pattern recognition and classification with seamless results, are at the front line of high-performance sports coaching. In this work, an intelligent data analytics system for swimmer performance is proposed. The system includes (i) pre-processing of raw signals; (ii) feature representation of wearable sensors and biosensors; (iii) online recognition of the swimming style and turns; and (iv) post-analysis of the performance for coaching decision support, including stroke counting and average speed. The system is supported by wearable inertial (AHRS) and biosensors (heart rate and pulse oximetry) placed on a swimmer’s body. Radio-frequency links are employed to communicate with the heart rate sensor and the station in the vicinity of the swimming pool, where analytics is carried out. Experiments were carried out in a real training setup, including 10 athletes aged 15 to 17 years. This scenario resulted in a set of circa 8000 samples. The experimental results show that the proposed system for intelligent swimming analytics with wearable sensors effectively yields immediate feedback to coaches and swimmers based on real-time data analysis. The best result was achieved with a Random Forest classifier with a macro-averaged F1 of 95.02%. The benefit of the proposed framework was demonstrated by effectively supporting coaches while monitoring the training of several swimmers.eng
dc.identifier.citationCosta, J.; Silva, C.; Santos, M.; Fernandes, T.; Faria, S. Framework for Intelligent Swimming Analytics with Wearable Sensors for Stroke Classification. Sensors 2021, 21, 5162. https://doi.org/10.3390/s21155162.
dc.identifier.doi10.3390/s21155162
dc.identifier.eissn1424-8220
dc.identifier.urihttp://hdl.handle.net/10400.8/15680
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relation.hasversionhttps://www.mdpi.com/1424-8220/21/15/5162
dc.relation.ispartofSensors
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectwearable sensors
dc.subjectdata acquisition
dc.subjectsensor data representation
dc.subjectfeature representation
dc.subjectintelligent systems
dc.subjectensemble methods
dc.titleFramework for Intelligent Swimming Analytics with Wearable Sensors for Stroke Classificationeng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage17
oaire.citation.issue15
oaire.citation.startPage1
oaire.citation.titleJournal of Sensors
oaire.citation.volume21
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameCosta
person.familyNameFernandes
person.familyNameFaria
person.givenNameJoana
person.givenNameTelmo
person.givenNameSergio
person.identifier.ciencia-id391F-E0B5-14B5
person.identifier.ciencia-id8815-4101-28DD
person.identifier.orcid0000-0002-4053-5718
person.identifier.orcid0000-0003-0882-7478
person.identifier.orcid0000-0002-0993-9124
person.identifier.ridB-7909-2018
person.identifier.ridC-5245-2011
person.identifier.scopus-author-id24779209500
person.identifier.scopus-author-id14027853900
relation.isAuthorOfPublication23d200dc-1a81-4bd9-9a4a-0efc28af6ce4
relation.isAuthorOfPublication5c77bf6a-79cb-4cfd-b316-b2ed1890bb29
relation.isAuthorOfPublicationf69bd4d6-a6ef-4d20-8148-575478909661
relation.isAuthorOfPublication.latestForDiscovery23d200dc-1a81-4bd9-9a4a-0efc28af6ce4

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Intelligent approaches in sports using IoT devices to gather data, attempting to optimize athlete’s training and performance, are cutting edge research. Synergies between recent wearable hardware and wireless communication strategies, together with the advances in intelligent algorithms, which are able to perform online pattern recognition and classification with seamless results, are at the front line of high-performance sports coaching. In this work, an intelligent data analytics system for swimmer performance is proposed. The system includes (i) pre-processing of raw signals; (ii) feature representation of wearable sensors and biosensors; (iii) online recognition of the swimming style and turns; and (iv) post-analysis of the performance for coaching decision support, including stroke counting and average speed. The system is supported by wearable inertial (AHRS) and biosensors (heart rate and pulse oximetry) placed on a swimmer’s body. Radio-frequency links are employed to communicate with the heart rate sensor and the station in the vicinity of the swimming pool, where analytics is carried out. Experiments were carried out in a real training setup, including 10 athletes aged 15 to 17 years. This scenario resulted in a set of circa 8000 samples. The experimental results show that the proposed system for intelligent swimming analytics with wearable sensors effectively yields immediate feedback to coaches and swimmers based on real-time data analysis. The best result was achieved with a Random Forest classifier with a macro-averaged F1 of 95.02%. The benefit of the proposed framework was demonstrated by effectively supporting coaches while monitoring the training of several swimmers.
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