Neural networks based approach to estimate body fat (%BF)

Detalhes bibliográficos
Autor(a) principal: Manuel R. Barbosa
Data de Publicação: 2010
Outros Autores: Teresa Amaral, Maria de Fátima Chousal, Maria Teresa Restivo
Tipo de documento: Livro
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/10216/79708
Resumo: Abstract: The amount of fat in human body composition relative to total body weight (%BF) is considered a determinant factor to a healthier and longer life. In this paper a neural network approach, that overcomes some of the current limitations of assessing %BF through skinfold thickness measurement with calliper devices, is presented. Neural networks recognised capabilities in modelling nonlinear problems can provide a valuable tool to deal with the inherent nonlinear behaviour of body tissues. The approach was tested on a sample of elder individuals, men and women, showing better performance when compared with two available alternative methodologies.
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spelling Neural networks based approach to estimate body fat (%BF)Ciências Tecnológicas, Ciências da Saúde, Engenharia electrotécnica, electrónica e informáticaTechnological sciences, Health sciences, Electrical engineering, Electronic engineering, Information engineeringAbstract: The amount of fat in human body composition relative to total body weight (%BF) is considered a determinant factor to a healthier and longer life. In this paper a neural network approach, that overcomes some of the current limitations of assessing %BF through skinfold thickness measurement with calliper devices, is presented. Neural networks recognised capabilities in modelling nonlinear problems can provide a valuable tool to deal with the inherent nonlinear behaviour of body tissues. The approach was tested on a sample of elder individuals, men and women, showing better performance when compared with two available alternative methodologies.20102010-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttps://hdl.handle.net/10216/79708engManuel R. BarbosaTeresa AmaralMaria de Fátima ChousalMaria Teresa Restivoinfo: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:RCAAP2023-11-29T13:03:13Zoai:repositorio-aberto.up.pt:10216/79708Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:32:40.597112Repositório 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 Neural networks based approach to estimate body fat (%BF)
title Neural networks based approach to estimate body fat (%BF)
spellingShingle Neural networks based approach to estimate body fat (%BF)
Manuel R. Barbosa
Ciências Tecnológicas, Ciências da Saúde, Engenharia electrotécnica, electrónica e informática
Technological sciences, Health sciences, Electrical engineering, Electronic engineering, Information engineering
title_short Neural networks based approach to estimate body fat (%BF)
title_full Neural networks based approach to estimate body fat (%BF)
title_fullStr Neural networks based approach to estimate body fat (%BF)
title_full_unstemmed Neural networks based approach to estimate body fat (%BF)
title_sort Neural networks based approach to estimate body fat (%BF)
author Manuel R. Barbosa
author_facet Manuel R. Barbosa
Teresa Amaral
Maria de Fátima Chousal
Maria Teresa Restivo
author_role author
author2 Teresa Amaral
Maria de Fátima Chousal
Maria Teresa Restivo
author2_role author
author
author
dc.contributor.author.fl_str_mv Manuel R. Barbosa
Teresa Amaral
Maria de Fátima Chousal
Maria Teresa Restivo
dc.subject.por.fl_str_mv Ciências Tecnológicas, Ciências da Saúde, Engenharia electrotécnica, electrónica e informática
Technological sciences, Health sciences, Electrical engineering, Electronic engineering, Information engineering
topic Ciências Tecnológicas, Ciências da Saúde, Engenharia electrotécnica, electrónica e informática
Technological sciences, Health sciences, Electrical engineering, Electronic engineering, Information engineering
description Abstract: The amount of fat in human body composition relative to total body weight (%BF) is considered a determinant factor to a healthier and longer life. In this paper a neural network approach, that overcomes some of the current limitations of assessing %BF through skinfold thickness measurement with calliper devices, is presented. Neural networks recognised capabilities in modelling nonlinear problems can provide a valuable tool to deal with the inherent nonlinear behaviour of body tissues. The approach was tested on a sample of elder individuals, men and women, showing better performance when compared with two available alternative methodologies.
publishDate 2010
dc.date.none.fl_str_mv 2010
2010-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/book
format book
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/10216/79708
url https://hdl.handle.net/10216/79708
dc.language.iso.fl_str_mv eng
language eng
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)
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instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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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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