Framework for Intelligent Swimming Analytics with Wearable Sensors for Stroke Classification
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/105465 https://doi.org/10.3390/s21155162 |
Resumo: | 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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Framework for Intelligent Swimming Analytics with Wearable Sensors for Stroke Classificationwearable sensorsdata acquisitionsensor data representationfeature representationintelligent systemsensemble methodsAthletesHumansSwimmingAthletic PerformanceBiosensing TechniquesWearable Electronic DevicesIntelligent 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.MDPI2021-07-30info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/105465http://hdl.handle.net/10316/105465https://doi.org/10.3390/s21155162eng1424-8220Costa, JoanaSilva, CatarinaSantos, MiguelFernandes, TelmoFaria, Sérgioinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-01T11:29:25Zoai:estudogeral.uc.pt:10316/105465Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:22:02.190735Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Framework for Intelligent Swimming Analytics with Wearable Sensors for Stroke Classification |
title |
Framework for Intelligent Swimming Analytics with Wearable Sensors for Stroke Classification |
spellingShingle |
Framework for Intelligent Swimming Analytics with Wearable Sensors for Stroke Classification Costa, Joana wearable sensors data acquisition sensor data representation feature representation intelligent systems ensemble methods Athletes Humans Swimming Athletic Performance Biosensing Techniques Wearable Electronic Devices |
title_short |
Framework for Intelligent Swimming Analytics with Wearable Sensors for Stroke Classification |
title_full |
Framework for Intelligent Swimming Analytics with Wearable Sensors for Stroke Classification |
title_fullStr |
Framework for Intelligent Swimming Analytics with Wearable Sensors for Stroke Classification |
title_full_unstemmed |
Framework for Intelligent Swimming Analytics with Wearable Sensors for Stroke Classification |
title_sort |
Framework for Intelligent Swimming Analytics with Wearable Sensors for Stroke Classification |
author |
Costa, Joana |
author_facet |
Costa, Joana Silva, Catarina Santos, Miguel Fernandes, Telmo Faria, Sérgio |
author_role |
author |
author2 |
Silva, Catarina Santos, Miguel Fernandes, Telmo Faria, Sérgio |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Costa, Joana Silva, Catarina Santos, Miguel Fernandes, Telmo Faria, Sérgio |
dc.subject.por.fl_str_mv |
wearable sensors data acquisition sensor data representation feature representation intelligent systems ensemble methods Athletes Humans Swimming Athletic Performance Biosensing Techniques Wearable Electronic Devices |
topic |
wearable sensors data acquisition sensor data representation feature representation intelligent systems ensemble methods Athletes Humans Swimming Athletic Performance Biosensing Techniques Wearable Electronic Devices |
description |
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. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-07-30 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/105465 http://hdl.handle.net/10316/105465 https://doi.org/10.3390/s21155162 |
url |
http://hdl.handle.net/10316/105465 https://doi.org/10.3390/s21155162 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1424-8220 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799134110262755328 |