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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.accessioned2022-11-02T09:55:25Z
dc.date.available2022-11-02T09:55:25Z
dc.date.issued2019-10-21
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_US
dc.descriptionPersonal 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_US
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_US
dc.identifier.cristinIDFRIDAID 1734971
dc.identifier.doi10.1109/CBMI.2019.8877406
dc.identifier.isbn978-1-7281-4673-7
dc.identifier.issn1949-3991
dc.identifier.issn1949-3983
dc.identifier.urihttps://hdl.handle.net/10037/27231
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.projectIDNorges forskningsråd: 250138en_US
dc.relation.projectIDNorges forskningsråd: 263248en_US
dc.relation.urihttps://ieeexplore.ieee.org/document/8877406/
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright © 2019 IEEEen_US
dc.titlePredicting peek readiness-to-train of soccer players using long short-term memory recurrent neural networksen_US
dc.type.versionacceptedVersionen_US
dc.typeConference objecten_US
dc.typeKonferansebidragen_US


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