{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T23:06:45Z","timestamp":1772060805307,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,5,27]],"date-time":"2020-05-27T00:00:00Z","timestamp":1590537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["37082"],"award-info":[{"award-number":["37082"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Optimizing athlete\u2019s 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.<\/jats:p>","DOI":"10.3390\/s20113040","type":"journal-article","created":{"date-parts":[[2020,5,28]],"date-time":"2020-05-28T12:36:58Z","timestamp":1590669418000},"page":"3040","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Using Artificial Intelligence for Pattern Recognition in a Sports Context"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9868-4679","authenticated-orcid":false,"given":"Ana Cristina Nunes","family":"Rodrigues","sequence":"first","affiliation":[{"name":"Coimbra Polytechnic, Instituto Superior de Engenharia de Coimbra, 3030-199 Coimbra, Portugal"}]},{"given":"Alexandre Santos","family":"Pereira","sequence":"additional","affiliation":[{"name":"Faculdade de Ci\u00eancia e Tecnologia, New University of Lisbon, 2829-516 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2433-5193","authenticated-orcid":false,"given":"Rui Manuel Sousa","family":"Mendes","sequence":"additional","affiliation":[{"name":"Coimbra Polytechnic, Escola Superior de Educa\u00e7\u00e3o de Coimbra, 3030-329 Coimbra, Portugal"}]},{"given":"Andr\u00e9 Gon\u00e7alves","family":"Ara\u00fajo","sequence":"additional","affiliation":[{"name":"Ingeniarius Lda, 3025-307 Coimbra, Portugal"}]},{"given":"Micael Santos","family":"Couceiro","sequence":"additional","affiliation":[{"name":"Ingeniarius Lda, 3025-307 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6956-0514","authenticated-orcid":false,"given":"Ant\u00f3nio Jos\u00e9","family":"Figueiredo","sequence":"additional","affiliation":[{"name":"Univ Coimbra, Research Unit for Sport and Physical Activity, Faculty of Sport Sciences and Physical Education, 3040-248 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,27]]},"reference":[{"key":"ref_1","first-page":"371","article-title":"Anatomy on pattern recognition","volume":"2","author":"Parasher","year":"2011","journal-title":"Indian J. 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