Pressure-based posture classification methods and algorithms: a systematic review
Autor(a) principal: | |
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Data de Publicação: | 2023 |
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/10400.11/8487 |
Resumo: | There are many uses for machine learning in everyday life and there is a steady increase in the field of medicine; the use of such technologies facilitates the tiresome work of health professionals by either automating repetitive tasks or making them simpler. Bed-related disorders are a great example where tedious tasks could be facilitated by machine learning algorithms, as suggested by many authors, by providing information on the posture of a particular bedded patient to health professionals. To assess the already existing studies in this field, this study provides a systematic review where the literature is analyzed to find correlations between the various factors involved in the making of such a system and how they perform. The overall findings suggest that there is only a significant relationship between the postures considered for classification and the resulting accuracy, despite some other factors such as the amount of data available providing some differences according to the type of algorithm used, with neural networks needing larger datasets. This study aims to increase awareness in this field and give future researchers information based on previous works’ strengths and limitations while giving some suggestions based on the literature review. |
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Pressure-based posture classification methods and algorithms: a systematic reviewPostureClassificationLyingBeddedPressureAlgorithmsThere are many uses for machine learning in everyday life and there is a steady increase in the field of medicine; the use of such technologies facilitates the tiresome work of health professionals by either automating repetitive tasks or making them simpler. Bed-related disorders are a great example where tedious tasks could be facilitated by machine learning algorithms, as suggested by many authors, by providing information on the posture of a particular bedded patient to health professionals. To assess the already existing studies in this field, this study provides a systematic review where the literature is analyzed to find correlations between the various factors involved in the making of such a system and how they perform. The overall findings suggest that there is only a significant relationship between the postures considered for classification and the resulting accuracy, despite some other factors such as the amount of data available providing some differences according to the type of algorithm used, with neural networks needing larger datasets. This study aims to increase awareness in this field and give future researchers information based on previous works’ strengths and limitations while giving some suggestions based on the literature review.Repositório Científico do Instituto Politécnico de Castelo BrancoFonseca, LuísRibeiro, Fernando ReinaldoMetrôlho, J.C.M.M.2023-05-16T12:45:18Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.11/8487engFonseca, Luís, Ribeiro, Fernando Reinaldo, Metrôlho, J.C.M.M. (2023) - Pressure-based posture classification methods and algorithms: a systematic review. Computers, 12:5, p. 104. DOI: http://dx.doi.org/10.3390/computers12050104https://doi.org/10.3390/computers12050104info: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:RCAAP2023-12-16T01:45:32Zoai:repositorio.ipcb.pt:10400.11/8487Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:54:13.173216Repositório 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 |
Pressure-based posture classification methods and algorithms: a systematic review |
title |
Pressure-based posture classification methods and algorithms: a systematic review |
spellingShingle |
Pressure-based posture classification methods and algorithms: a systematic review Fonseca, Luís Posture Classification Lying Bedded Pressure Algorithms |
title_short |
Pressure-based posture classification methods and algorithms: a systematic review |
title_full |
Pressure-based posture classification methods and algorithms: a systematic review |
title_fullStr |
Pressure-based posture classification methods and algorithms: a systematic review |
title_full_unstemmed |
Pressure-based posture classification methods and algorithms: a systematic review |
title_sort |
Pressure-based posture classification methods and algorithms: a systematic review |
author |
Fonseca, Luís |
author_facet |
Fonseca, Luís Ribeiro, Fernando Reinaldo Metrôlho, J.C.M.M. |
author_role |
author |
author2 |
Ribeiro, Fernando Reinaldo Metrôlho, J.C.M.M. |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico de Castelo Branco |
dc.contributor.author.fl_str_mv |
Fonseca, Luís Ribeiro, Fernando Reinaldo Metrôlho, J.C.M.M. |
dc.subject.por.fl_str_mv |
Posture Classification Lying Bedded Pressure Algorithms |
topic |
Posture Classification Lying Bedded Pressure Algorithms |
description |
There are many uses for machine learning in everyday life and there is a steady increase in the field of medicine; the use of such technologies facilitates the tiresome work of health professionals by either automating repetitive tasks or making them simpler. Bed-related disorders are a great example where tedious tasks could be facilitated by machine learning algorithms, as suggested by many authors, by providing information on the posture of a particular bedded patient to health professionals. To assess the already existing studies in this field, this study provides a systematic review where the literature is analyzed to find correlations between the various factors involved in the making of such a system and how they perform. The overall findings suggest that there is only a significant relationship between the postures considered for classification and the resulting accuracy, despite some other factors such as the amount of data available providing some differences according to the type of algorithm used, with neural networks needing larger datasets. This study aims to increase awareness in this field and give future researchers information based on previous works’ strengths and limitations while giving some suggestions based on the literature review. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-05-16T12:45:18Z 2023 2023-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.11/8487 |
url |
http://hdl.handle.net/10400.11/8487 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Fonseca, Luís, Ribeiro, Fernando Reinaldo, Metrôlho, J.C.M.M. (2023) - Pressure-based posture classification methods and algorithms: a systematic review. Computers, 12:5, p. 104. DOI: http://dx.doi.org/10.3390/computers12050104 https://doi.org/10.3390/computers12050104 |
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 |
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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 |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799131615249563648 |