Detection of different throw types and ball velocity with imus and machine learning in team handball
(Erkennen verschiedener Wurfarten und Ballgeschwindigkeiten im Handball mit IMUs und maschinellem Lernen)
The purpose of this study was to investigate if an inertial measurement unit (IMU) and machine learning could be used to detect different types of team handball throws and predict ball velocity. Throwing was measured using IMUs and a radar gun in seventeen participants during standing, running and jump throws with a circular and whip-like wind up. Using these data, machine learning could predict peak ball velocity with an error of 1.05 m/s and classify approach types and throw types with ~85-90% accuracy. It was concluded that to monitor throwing load, the combination of inertial measurement units and machine learning offers a practical and automated method of quantifying throw counts and discriminating throw types in handball players under standard conditions.
© Copyright 2020 ISBS Proceedings Archive (Michigan). Northern Michigan University. Veröffentlicht von International Society of Biomechanics in Sports. Alle Rechte vorbehalten.
Schlagworte: | Biomechanik Analyse Handball Schulter Schmerz Geschwindigkeit Wurf Untersuchungsmethode Inertialmesssystem |
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Notationen: | Trainingswissenschaft Naturwissenschaften und Technik Spielsportarten |
Tagging: | deep learning künstliche Intelligenz |
Veröffentlicht in: | ISBS Proceedings Archive (Michigan) |
Herausgeber: | M. Robinson, M. Lake, B. Baltzopoulos, J. Vanrenterghem |
Veröffentlicht: |
Liverpool
International Society of Biomechanics in Sports
2020
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Jahrgang: | 38 |
Heft: | 1 |
Seiten: | Article 48 |
Dokumentenarten: | Kongressband, Tagungsbericht elektronische Zeitschrift Artikel |
Sprache: | Englisch |
Level: | hoch |