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Communication

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

1
Department of Sports Sciences, Nord University, 7600 Levanger, Norway
2
Sports Performance Research Institute New Zealand, Auckland University of Technology, Auckland 1010, New Zealand
3
Human Potential Centre, Auckland University of Technology, Auckland 1010, New Zealand
*
Author to whom correspondence should be addressed.
Academic Editors: Mark Robinson and Jacqueline Alderson
Sensors 2021, 21(7), 2288; https://doi.org/10.3390/s21072288
Received: 18 February 2021 / Revised: 22 March 2021 / Accepted: 23 March 2021 / Published: 25 March 2021
(This article belongs to the Special Issue Sensors in Sports Biomechanics)
Injuries 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. View Full-Text
Keywords: handball; throwing velocity; artificial intelligence; inertial sensors handball; throwing velocity; artificial intelligence; inertial sensors
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MDPI and ACS Style

van den Tillaar, R.; Bhandurge, S.; Stewart, T. Can Machine Learning with IMUs Be Used to Detect Different Throws and Estimate Ball Velocity in Team Handball? Sensors 2021, 21, 2288. https://doi.org/10.3390/s21072288

AMA Style

van den Tillaar R, Bhandurge S, Stewart T. Can Machine Learning with IMUs Be Used to Detect Different Throws and Estimate Ball Velocity in Team Handball? Sensors. 2021; 21(7):2288. https://doi.org/10.3390/s21072288

Chicago/Turabian Style

van den Tillaar, Roland, Shruti Bhandurge, and Tom Stewart. 2021. "Can Machine Learning with IMUs Be Used to Detect Different Throws and Estimate Ball Velocity in Team Handball?" Sensors 21, no. 7: 2288. https://doi.org/10.3390/s21072288

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