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Toward characterizing cardiovascular fitness using machine learning based on unobtrusive data.
Frade, Maria Cecília Moraes; Beltrame, Thomas; Gois, Mariana de Oliveira; Pinto, Allan; Tonello, Silvia Cristina Garcia de Moura; Torres, Ricardo da Silva; Catai, Aparecida Maria.
Afiliação
  • Frade MCM; Department of Physical Therapy, Federal University of São Carlos, São Carlos, São Paulo, Brazil.
  • Beltrame T; Department of Physical Therapy, Federal University of São Carlos, São Carlos, São Paulo, Brazil.
  • Gois MO; Samsung R&D Institute Brazil-SRBR, Campinas, São Paulo, Brazil.
  • Pinto A; Department of Physical Therapy, Federal University of São Carlos, São Carlos, São Paulo, Brazil.
  • Tonello SCGM; Brazilian Synchrotron Light Laboratory (LNLS), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, São Paulo, Brazil.
  • Torres RDS; Department of Physical Therapy, Federal University of São Carlos, São Carlos, São Paulo, Brazil.
  • Catai AM; Department of ICT and Natural Sciences, Faculty of Information Technology and Electrical Engineering, NTNU-Norwegian University of Science and Technology, Ålesund, Norway.
PLoS One ; 18(3): e0282398, 2023.
Article em En | MEDLINE | ID: mdl-36862737
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Atividades Cotidianas / Sistema Cardiovascular Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Atividades Cotidianas / Sistema Cardiovascular Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article