Accuracy of pattern detection methods in the performance of golf putting
Autor(a) principal: | |
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Data de Publicação: | 2013 |
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/10400.26/46642 |
Resumo: | The authors present a comparison of the classification accuracy of 5 pattern detection methods in the performance of golf putting. The detection of the position of the golf club was performed using a computer vision technique followed by the estimation algorithm Darwinian particle swarm optimization to obtain a kinematical model of each trial. The estimated parameters of the models were subsequently used as sample of five classification algorithms: (a) linear discriminant analysis, (b) quadratic discriminant analysis, (c) naive Bayes with normal distribution, (d) naive Bayes with kernel smoothing density estimate, and (e) least squares support vector machines. Beyond testing the performance of each classification method, it was also possible to identify a putting signature that characterized each golf player. It may be concluded that these methods can be applied to the study of coordination and motor control on the putting performance, allowing for the analysis of the intra- and interpersonal variability of motor behavior in performance contexts. |
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Accuracy of pattern detection methods in the performance of golf puttingclassificationdetectiongolf puttingindividualizedkinematic patternThe authors present a comparison of the classification accuracy of 5 pattern detection methods in the performance of golf putting. The detection of the position of the golf club was performed using a computer vision technique followed by the estimation algorithm Darwinian particle swarm optimization to obtain a kinematical model of each trial. The estimated parameters of the models were subsequently used as sample of five classification algorithms: (a) linear discriminant analysis, (b) quadratic discriminant analysis, (c) naive Bayes with normal distribution, (d) naive Bayes with kernel smoothing density estimate, and (e) least squares support vector machines. Beyond testing the performance of each classification method, it was also possible to identify a putting signature that characterized each golf player. It may be concluded that these methods can be applied to the study of coordination and motor control on the putting performance, allowing for the analysis of the intra- and interpersonal variability of motor behavior in performance contexts.Taylor & FrancisRepositório ComumCouceiro, MicaelDias, GonçaloMendes, RuiAraújo, Duarte2023-09-20T10:36:05Z20132013-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.26/46642enghttps://doi.org/10.1080/00222895.2012.740100info: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:RCAAP2023-09-21T02:17:16Zoai:comum.rcaap.pt:10400.26/46642Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:29:49.019893Repositório 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 |
Accuracy of pattern detection methods in the performance of golf putting |
title |
Accuracy of pattern detection methods in the performance of golf putting |
spellingShingle |
Accuracy of pattern detection methods in the performance of golf putting Couceiro, Micael classification detection golf putting individualizedkinematic pattern |
title_short |
Accuracy of pattern detection methods in the performance of golf putting |
title_full |
Accuracy of pattern detection methods in the performance of golf putting |
title_fullStr |
Accuracy of pattern detection methods in the performance of golf putting |
title_full_unstemmed |
Accuracy of pattern detection methods in the performance of golf putting |
title_sort |
Accuracy of pattern detection methods in the performance of golf putting |
author |
Couceiro, Micael |
author_facet |
Couceiro, Micael Dias, Gonçalo Mendes, Rui Araújo, Duarte |
author_role |
author |
author2 |
Dias, Gonçalo Mendes, Rui Araújo, Duarte |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Repositório Comum |
dc.contributor.author.fl_str_mv |
Couceiro, Micael Dias, Gonçalo Mendes, Rui Araújo, Duarte |
dc.subject.por.fl_str_mv |
classification detection golf putting individualizedkinematic pattern |
topic |
classification detection golf putting individualizedkinematic pattern |
description |
The authors present a comparison of the classification accuracy of 5 pattern detection methods in the performance of golf putting. The detection of the position of the golf club was performed using a computer vision technique followed by the estimation algorithm Darwinian particle swarm optimization to obtain a kinematical model of each trial. The estimated parameters of the models were subsequently used as sample of five classification algorithms: (a) linear discriminant analysis, (b) quadratic discriminant analysis, (c) naive Bayes with normal distribution, (d) naive Bayes with kernel smoothing density estimate, and (e) least squares support vector machines. Beyond testing the performance of each classification method, it was also possible to identify a putting signature that characterized each golf player. It may be concluded that these methods can be applied to the study of coordination and motor control on the putting performance, allowing for the analysis of the intra- and interpersonal variability of motor behavior in performance contexts. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013 2013-01-01T00:00:00Z 2023-09-20T10:36:05Z |
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/10400.26/46642 |
url |
http://hdl.handle.net/10400.26/46642 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://doi.org/10.1080/00222895.2012.740100 |
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 |
Taylor & Francis |
publisher.none.fl_str_mv |
Taylor & Francis |
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) |
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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1799133566259429376 |