Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/106727
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dc.contributor.authorRodrigues, Ana Cristina Nunes-
dc.contributor.authorPereira, Alexandre Santos-
dc.contributor.authorMendes, Rui Manuel Sousa-
dc.contributor.authorAraújo, André Gonçalves-
dc.contributor.authorCouceiro, Micael Santos-
dc.contributor.authorFigueiredo, António José-
dc.date.accessioned2023-04-20T07:50:21Z-
dc.date.available2023-04-20T07:50:21Z-
dc.date.issued2020-05-27-
dc.identifier.issn1424-8220-
dc.identifier.urihttps://hdl.handle.net/10316/106727-
dc.description.abstractOptimizing athlete's performance is one of the most important and challenging aspects of coaching. Physiological and positional data, often acquired using wearable devices, have been useful to identify patterns, thus leading to a better understanding of the game and, consequently, providing the opportunity to improve the athletic performance. Even though there is a panoply of research in pattern recognition, there is a gap when it comes to non-controlled environments, as during sports training and competition. This research paper combines the use of physiological and positional data as sequential features of different artificial intelligence approaches for action recognition in a real match context, adopting futsal as its case study. The traditional artificial neural networks (ANN) is compared with a deep learning method, Long Short-Term Memory Network, and also with the Dynamic Bayesian Mixture Model, which is an ensemble classification method. The methods were used to process all data sequences, which allowed to determine, based on the balance between precision and recall, that Dynamic Bayesian Mixture Model presents a superior performance, with an F1 score of 80.54% against the 33.31% achieved by the Long Short-Term Memory Network and 14.74% achieved by ANN.pt
dc.language.isoengpt
dc.publisherMDPIpt
dc.relationCORE R&D Project CENTRO-01-0247-FEDER-037082pt
dc.rightsopenAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt
dc.subjectartificial intelligencept
dc.subjectartificial neural networkpt
dc.subjectlong short-term memorypt
dc.subjectensemble classification methodpt
dc.subjectwearable technologypt
dc.subjectsportspt
dc.titleUsing Artificial Intelligence for Pattern Recognition in a Sports Contextpt
dc.typearticlept
degois.publication.firstPage3040pt
degois.publication.issue11pt
degois.publication.titleSensors (Switzerland)pt
dc.peerreviewedyespt
dc.identifier.doi10.3390/s20113040-
degois.publication.volume20pt
dc.date.embargo2020-05-27*
dc.identifier.pmid32471189-
uc.date.periodoEmbargo0pt
dc.identifier.eissn1424-8220-
item.grantfulltextopen-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypearticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextCom Texto completo-
crisitem.author.researchunitISR - Institute of Systems and Robotics-
crisitem.author.researchunitCIDAF - Research Unit for Sport and Physical Activity-
crisitem.author.researchunitCIDAF - Research Unit for Sport and Physical Activity-
crisitem.author.parentresearchunitUniversity of Coimbra-
crisitem.author.orcid0000-0001-6641-6090-
crisitem.author.orcid0000-0001-6956-0514-
Appears in Collections:I&D CIDAF - Artigos em Revistas Internacionais
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