Framework for Intelligent Swimming Analytics with Wearable Sensors for Stroke Classification

Detalhes bibliográficos
Autor(a) principal: Costa, Joana
Data de Publicação: 2021
Outros Autores: Silva, Catarina, Santos, Miguel, Fernandes, Telmo, Faria, Sérgio
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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spelling 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
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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
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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
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