Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor

Detalhes bibliográficos
Autor(a) principal: Ramos, Guilherme
Data de Publicação: 2020
Outros Autores: Vaz, João Rocha, Mendonça, Gonçalo Vilhena, Pezarat-Correia, Pedro, Rodrigues, João, Alfaras, Miquel, Gamboa, Hugo Filipe Silveira
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/114679
Resumo: PTDC/DTP-DES/5714/2014 NIH-P20GM109090 FCT grant I&D 2015-2020 CMUP-ERI/HCI/0046
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spelling Fatigue Evaluation through Machine Learning and a Global Fatigue DescriptorBiotechnologySurgeryBiomedical EngineeringHealth InformaticsSDG 3 - Good Health and Well-beingPTDC/DTP-DES/5714/2014 NIH-P20GM109090 FCT grant I&D 2015-2020 CMUP-ERI/HCI/0046Research in physiology and sports science has shown that fatigue, a complex psychophysiological phenomenon, has a relevant impact in performance and in the correct functioning of our motricity system, potentially being a cause of damage to the human organism. Fatigue can be seen as a subjective or objective phenomenon. Subjective fatigue corresponds to a mental and cognitive event, while fatigue referred as objective is a physical phenomenon. Despite the fact that subjective fatigue is often undervalued, only a physically and mentally healthy athlete is able to achieve top performance in a discipline. Therefore, we argue that physical training programs should address the preventive assessment of both subjective and objective fatigue mechanisms in order to minimize the risk of injuries. In this context, our paper presents a machine-learning system capable of extracting individual fatigue descriptors (IFDs) from electromyographic (EMG) and heart rate variability (HRV) measurements. Our novel approach, using two types of biosignals so that a global (mental and physical) fatigue assessment is taken into account, reflects the onset of fatigue by implementing a combination of a dimensionless (0-1) global fatigue descriptor (GFD) and a support vector machine (SVM) classifier. The system, based on 9 main combined features, achieves fatigue regime classification performances of 0.82±0.24, ensuring a successful preventive assessment when dangerous fatigue levels are reached. Training data were acquired in a constant work rate test (executed by 14 subjects using a cycloergometry device), where the variable under study (fatigue) gradually increased until the volunteer reached an objective exhaustion state.LIBPhys-UNLRUNRamos, GuilhermeVaz, João RochaMendonça, Gonçalo VilhenaPezarat-Correia, PedroRodrigues, JoãoAlfaras, MiquelGamboa, Hugo Filipe Silveira2021-03-29T22:22:23Z2020-01-072020-01-07T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/114679eng2040-2295PURE: 17174818https://doi.org/10.1155/2020/6484129info: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:RCAAP2024-03-11T04:57:19Zoai:run.unl.pt:10362/114679Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:42:35.896729Repositório 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 Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor
title Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor
spellingShingle Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor
Ramos, Guilherme
Biotechnology
Surgery
Biomedical Engineering
Health Informatics
SDG 3 - Good Health and Well-being
title_short Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor
title_full Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor
title_fullStr Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor
title_full_unstemmed Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor
title_sort Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor
author Ramos, Guilherme
author_facet Ramos, Guilherme
Vaz, João Rocha
Mendonça, Gonçalo Vilhena
Pezarat-Correia, Pedro
Rodrigues, João
Alfaras, Miquel
Gamboa, Hugo Filipe Silveira
author_role author
author2 Vaz, João Rocha
Mendonça, Gonçalo Vilhena
Pezarat-Correia, Pedro
Rodrigues, João
Alfaras, Miquel
Gamboa, Hugo Filipe Silveira
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv LIBPhys-UNL
RUN
dc.contributor.author.fl_str_mv Ramos, Guilherme
Vaz, João Rocha
Mendonça, Gonçalo Vilhena
Pezarat-Correia, Pedro
Rodrigues, João
Alfaras, Miquel
Gamboa, Hugo Filipe Silveira
dc.subject.por.fl_str_mv Biotechnology
Surgery
Biomedical Engineering
Health Informatics
SDG 3 - Good Health and Well-being
topic Biotechnology
Surgery
Biomedical Engineering
Health Informatics
SDG 3 - Good Health and Well-being
description PTDC/DTP-DES/5714/2014 NIH-P20GM109090 FCT grant I&D 2015-2020 CMUP-ERI/HCI/0046
publishDate 2020
dc.date.none.fl_str_mv 2020-01-07
2020-01-07T00:00:00Z
2021-03-29T22:22:23Z
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/10362/114679
url http://hdl.handle.net/10362/114679
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2040-2295
PURE: 17174818
https://doi.org/10.1155/2020/6484129
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
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
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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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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