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Using artificial intelligence for pattern recognition in a sports context

2020Journal article Open access
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dc.contributor.authorRodrigues, Ana Cristina
dc.contributor.authorPereira, Alexandre Santos
dc.contributor.authorMendes, Rui
dc.contributor.authorAraújo, André Gonçalves
dc.contributor.authorCouceiro, Micael
dc.contributor.authorFigueiredo, António J.
dc.date.accessioned2023-09-27T09:28:06Z
dc.date.available2023-09-27T09:28:06Z
dc.date.issued2020
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_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doihttps://doi.org/10.3390/s20113040pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.26/46782
dc.language.isoengpt_PT
dc.publisherMDPIpt_PT
dc.subjectartificial intelligencept_PT
dc.subjectartificial neural networkpt_PT
dc.subjectlong short-term memorypt_PT
dc.subjectensemble classification methodpt_PT
dc.subjectwearable technologypt_PT
dc.subjectsportspt_PT
dc.titleUsing artificial intelligence for pattern recognition in a sports contextpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceBaselpt_PT
oaire.citation.issue11pt_PT
oaire.citation.titleSensorspt_PT
oaire.citation.volume20pt_PT
person.familyNameRodrigues
person.familyNameMendes
person.familyNameCouceiro
person.familyNameBarata Figueiredo
person.givenNameAna Cristina
person.givenNameRui
person.givenNameMicael
person.givenNameAntónio José
person.identifier722571
person.identifier.ciencia-idA71F-1E22-D496
person.identifier.ciencia-id0B14-ED18-23C4
person.identifier.ciencia-idF015-8DF0-6C6D
person.identifier.orcid0000-0002-9868-4679
person.identifier.orcid0000-0002-2433-5193
person.identifier.orcid0000-0001-6641-6090
person.identifier.orcid0000-0001-6956-0514
person.identifier.ridG-4342-2010
person.identifier.scopus-author-id54881476100
person.identifier.scopus-author-id35076901800
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication5cfce87c-ea97-4b95-8f16-60763e5d1544
relation.isAuthorOfPublicationb75ec7e9-b217-42e1-9df0-a340e34ca0d0
relation.isAuthorOfPublication7a6f0912-3613-4713-a58f-4e64c7ebb293
relation.isAuthorOfPublication35f6428a-4264-4075-8169-e44b2c524be6
relation.isAuthorOfPublication.latestForDiscoveryb75ec7e9-b217-42e1-9df0-a340e34ca0d0

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