Can Machine Learning With IMUs Be Used to Detect Different Throws and Estimate Ball Velocity in Team Handball?

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aut.relation.articlenumber2288en_NZ
aut.relation.issue7en_NZ
aut.relation.journalSensorsen_NZ
aut.relation.volume21en_NZ
aut.researcherDrabsch, Julie
dc.contributor.authorvan den Tillaar, Ren_NZ
dc.contributor.authorBhandurge, Sen_NZ
dc.contributor.authorStewart, Ten_NZ
dc.date.accessioned2021-05-10T20:59:58Z
dc.date.available2021-05-10T20:59:58Z
dc.date.copyright2021en_NZ
dc.date.issued2021en_NZ
dc.description.abstractInjuries in handball are common due to the repetitive demands of overhead throws at high velocities. Monitoring workload is crucial for understanding these demands and improving injury-prevention strategies. However, in handball, it is challenging to monitor throwing workload due to the difficulty of counting the number, intensity, and type of throws during training and competition. The aim of this study was to investigate if an inertial measurement unit (IMU) and machine learning (ML) techniques could be used to detect different types of team handball throws and predict ball velocity. Seventeen players performed several throws with different wind-up (circular and whip-like) and approach types (standing, running, and jumping) while wearing an IMU on their wrist. Ball velocity was measured using a radar gun. ML models predicted peak ball velocity with an error of 1.10 m/s and classified approach type and throw type with 80–87% accuracy. Using IMUs and ML models may offer a practical and automated method for quantifying throw counts and classifying the throw and approach types adopted by handball players.en_NZ
dc.identifier.citationSensors, 21(7), 2288. doi:10.3390/s21072288
dc.identifier.doi10.3390/s21072288en_NZ
dc.identifier.issn1424-8220en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/14174
dc.languageenen_NZ
dc.publisherMDPI AGen_NZ
dc.relation.urihttps://www.mdpi.com/1424-8220/21/7/2288
dc.rights© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectHandball; Throwing velocity; Artificial intelligence; Inertial sensors
dc.titleCan Machine Learning With IMUs Be Used to Detect Different Throws and Estimate Ball Velocity in Team Handball?en_NZ
dc.typeJournal Article
pubs.elements-id399551
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Health & Environmental Science
pubs.organisational-data/AUT/Faculty of Health & Environmental Science/School of Sport & Recreation
pubs.organisational-data/AUT/Faculty of Health & Environmental Science/School of Sport & Recreation/Physical Activity, Nutrition & the Outdoors Department
pubs.organisational-data/AUT/Faculty of Health & Environmental Science/School of Sport & Recreation/Sports Performance Research Institute New Zealand
pubs.organisational-data/AUT/Faculty of Health & Environmental Science/School of Sport & Recreation/Sports Performance Research Institute New Zealand/Human Potential Research Group
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Health and Environmental Sciences
pubs.organisational-data/AUT/PBRF/PBRF Health and Environmental Sciences/HS Sports & Recreation 2018 PBRF
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