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dc.contributor.authorWiik, Theodor
dc.contributor.authorJohansen, Håvard D.
dc.contributor.authorPettersen, Svein Arne
dc.contributor.authorMatias Do Vale Baptista, Ivan Andre
dc.contributor.authorKupka, Tomas
dc.contributor.authorJohansen, Dag
dc.contributor.authorRiegler, Michael
dc.contributor.authorHalvorsen, Pål
dc.date.accessioned2020-02-03T10:30:41Z
dc.date.accessioned2020-03-22T13:34:17Z
dc.date.available2020-02-03T10:30:41Z
dc.date.available2020-03-22T13:34:17Z
dc.date.issued2019
dc.identifier.citationWiik, Johansen HJ, Pettersen SA, Matias Do Vale Baptista IA, Kupka T, Johansen D, Riegler M, Halvorsen P: Predicting peek readiness-to-train of soccer players using long short-term memory recurrent neural networks. In: Gurrin CG, Jónsson BT, Peteri R, Rudinac, Marchand-Maillet S, Quénot, McGuinness, Guðmundsson, Little, Katsurai, Healy G. Proceedings of Content Based Multimedia Information (CBMI 2019), 2019. IEEE conference proceedingsen
dc.identifier.isbn978-1-7281-4673-7
dc.identifier.issn1949-3991
dc.identifier.issn1949-3983
dc.identifier.urihttps://hdl.handle.net/10642/8299
dc.description.abstractWe are witnessing the emergence of a myriad of hardware and software systems that quantifies sport and physical activities. These are frequently touted as game changers and important for future sport developments. The vast amount of generated data is often visualized in graphs and dashboards, for use by coaches and other sports professionals to make decisions on training and match strategies. Modern machine-learning methods has the potential to further fuel this process by deriving useful insights that are not easily observable in the raw data streams. This paper tackles the problem of deriving peaks in soccer players' ability to perform from subjective self-reported wellness data collected using the PMSys system. For this, we train a long short-term memory recurrent neural network model using data from two professional Norwegian soccer teams. We show that our model can predict performance peaks in most scenarios with a precision and recall of at least 90%. Equipped with such insight, coaches and trainers can better plan individual and team training sessions, and perhaps avoid over training and injuries.en
dc.description.sponsorshipThis work was supported in part by the Norwegian ResearchCouncil project numbers 263248/O70 and 250138.en
dc.language.isoenen
dc.publisherIEEE Xploreen
dc.relation.ispartofProceedings of Content Based Multimedia Information (CBMI 2019)
dc.relation.ispartofseriesInternational Workshop on Content-Based Multimedia Indexing, CBMI;
dc.relation.urihttps://ieeexplore.ieee.org/document/8877406/
dc.rights© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectSportsen
dc.subjectTrainingen
dc.subjectMoodsen
dc.subjectData modelsen
dc.subjectPredictive modelsen
dc.subjectInjuriesen
dc.titlePredicting peek readiness-to-train of soccer players using long short-term memory recurrent neural networksen
dc.typeConference objecten
dc.date.updated2020-02-03T10:30:41Z
dc.description.versionacceptedVersionen
dc.identifier.doihttps://dx.doi.org/10.1109/cbmi.2019.8877406
dc.identifier.cristin1734971
dc.relation.projectIDNorges forskningsråd: 250138
dc.relation.projectIDNorges forskningsråd: 263248
dc.source.isbn978-1-7281-4673-7


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