Automatic recognition of gait patterns in human motor disorders using machine learning: A review
Autor(a) principal: | |
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Data de Publicação: | 2018 |
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/1822/71654 |
Resumo: | Background: automatic recognition of human movement is an effective strategy to assess abnormal gait patterns. Machine learning approaches are mainly applied due to their ability to work with multidimensional nonlinear features. Purpose: to compare several machine learning algorithms employed for gait pattern recognition in motor disorders using discriminant features extracted from gait dynamics. Additionally, this work highlights procedures that improve gait recognition performance. Methods: we conducted an electronic literature search on Web of Science, IEEE, and Scopus, using “human recognition”, “gait patterns’’, and “feature selection methods” as relevant keywords. Results: analysis of the literature showed that kernel principal component analysis and genetic algorithms are efficient at reducing dimensional features due to their ability to process nonlinear data and converge to global optimum. Comparative analysis of machine learning performance showed that support vector machines (SVMs) exhibited higher accuracy and proper generalization for new instances. Conclusions: automatic recognition by combining dimensional data reduction, cross-validation and normalization techniques with SVMs may offer an objective and rapid tool for investigating the subject's clinical status. Future directions comprise the real-time application of these tools to drive powered assistive devices in free-living conditions. |
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Automatic recognition of gait patterns in human motor disorders using machine learning: A reviewDimensional data reductionHuman gait pattern recognitionLower limb motor disordersMachine learning approachesEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e InformáticaScience & TechnologyBackground: automatic recognition of human movement is an effective strategy to assess abnormal gait patterns. Machine learning approaches are mainly applied due to their ability to work with multidimensional nonlinear features. Purpose: to compare several machine learning algorithms employed for gait pattern recognition in motor disorders using discriminant features extracted from gait dynamics. Additionally, this work highlights procedures that improve gait recognition performance. Methods: we conducted an electronic literature search on Web of Science, IEEE, and Scopus, using “human recognition”, “gait patterns’’, and “feature selection methods” as relevant keywords. Results: analysis of the literature showed that kernel principal component analysis and genetic algorithms are efficient at reducing dimensional features due to their ability to process nonlinear data and converge to global optimum. Comparative analysis of machine learning performance showed that support vector machines (SVMs) exhibited higher accuracy and proper generalization for new instances. Conclusions: automatic recognition by combining dimensional data reduction, cross-validation and normalization techniques with SVMs may offer an objective and rapid tool for investigating the subject's clinical status. Future directions comprise the real-time application of these tools to drive powered assistive devices in free-living conditions.This work was supported by the FCT - Fundação para a Ciência e Tecnologia - with the reference scholarship SFRH/BD/108309/2015, and the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalização (POCI) - with the reference project POCI-01-0145-FEDER-006941. Also, this work was partially supported by grant RYC-2014-16613 by Spanish Ministry of Economy and Competitiveness.ElsevierUniversidade do MinhoFigueiredo, JoanaSantos, CristinaMoreno, Juan C.20182018-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/71654engFigueiredo, J., Santos, C. P., & Moreno, J. C. (2018). Automatic recognition of gait patterns in human motor disorders using machine learning: A review. Medical Engineering & Physics, 53, 1-12. doi: https://doi.org/10.1016/j.medengphy.2017.12.0061350-453310.1016/j.medengphy.2017.12.00629373231https://www.sciencedirect.com/science/article/pii/S1350453318300043info: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:RCAAP2023-07-21T12:00:45Zoai:repositorium.sdum.uminho.pt:1822/71654Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:50:37.142760Repositório 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 |
Automatic recognition of gait patterns in human motor disorders using machine learning: A review |
title |
Automatic recognition of gait patterns in human motor disorders using machine learning: A review |
spellingShingle |
Automatic recognition of gait patterns in human motor disorders using machine learning: A review Figueiredo, Joana Dimensional data reduction Human gait pattern recognition Lower limb motor disorders Machine learning approaches Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática Science & Technology |
title_short |
Automatic recognition of gait patterns in human motor disorders using machine learning: A review |
title_full |
Automatic recognition of gait patterns in human motor disorders using machine learning: A review |
title_fullStr |
Automatic recognition of gait patterns in human motor disorders using machine learning: A review |
title_full_unstemmed |
Automatic recognition of gait patterns in human motor disorders using machine learning: A review |
title_sort |
Automatic recognition of gait patterns in human motor disorders using machine learning: A review |
author |
Figueiredo, Joana |
author_facet |
Figueiredo, Joana Santos, Cristina Moreno, Juan C. |
author_role |
author |
author2 |
Santos, Cristina Moreno, Juan C. |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Figueiredo, Joana Santos, Cristina Moreno, Juan C. |
dc.subject.por.fl_str_mv |
Dimensional data reduction Human gait pattern recognition Lower limb motor disorders Machine learning approaches Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática Science & Technology |
topic |
Dimensional data reduction Human gait pattern recognition Lower limb motor disorders Machine learning approaches Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática Science & Technology |
description |
Background: automatic recognition of human movement is an effective strategy to assess abnormal gait patterns. Machine learning approaches are mainly applied due to their ability to work with multidimensional nonlinear features. Purpose: to compare several machine learning algorithms employed for gait pattern recognition in motor disorders using discriminant features extracted from gait dynamics. Additionally, this work highlights procedures that improve gait recognition performance. Methods: we conducted an electronic literature search on Web of Science, IEEE, and Scopus, using “human recognition”, “gait patterns’’, and “feature selection methods” as relevant keywords. Results: analysis of the literature showed that kernel principal component analysis and genetic algorithms are efficient at reducing dimensional features due to their ability to process nonlinear data and converge to global optimum. Comparative analysis of machine learning performance showed that support vector machines (SVMs) exhibited higher accuracy and proper generalization for new instances. Conclusions: automatic recognition by combining dimensional data reduction, cross-validation and normalization techniques with SVMs may offer an objective and rapid tool for investigating the subject's clinical status. Future directions comprise the real-time application of these tools to drive powered assistive devices in free-living conditions. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018 2018-01-01T00:00:00Z |
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/1822/71654 |
url |
http://hdl.handle.net/1822/71654 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Figueiredo, J., Santos, C. P., & Moreno, J. C. (2018). Automatic recognition of gait patterns in human motor disorders using machine learning: A review. Medical Engineering & Physics, 53, 1-12. doi: https://doi.org/10.1016/j.medengphy.2017.12.006 1350-4533 10.1016/j.medengphy.2017.12.006 29373231 https://www.sciencedirect.com/science/article/pii/S1350453318300043 |
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.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
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 |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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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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1799132275220152320 |