DETECTION OF DIFFERENT THROW TYPES AND BALL VELOCITY WITH IMUs AND MACHINE LEARNING IN TEAM HANDBALL

Authors

Roland Van den Tillaar, Department of Sports Sciences, Nord University, Levanger, NorwayFollow
shruti bhandurge, Sports Performance Research Institute New Zealand, Auckland University of Technology, Auckland, New ZealandFollow
Tom Stewart, Sports Performance Research Institute New Zealand, Auckland University of Technology, Auckland, New ZealandFollow

Category

Equipment

Document Type

Paper

Abstract

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.

Recommended Citation

Van den Tillaar, Roland; bhandurge, shruti; and Stewart, Tom (2020) "DETECTION OF DIFFERENT THROW TYPES AND BALL VELOCITY WITH IMUs AND MACHINE LEARNING IN TEAM HANDBALL," ISBS Proceedings Archive: Vol. 38: Iss. 1, Article 48.
Available at: https://commons.nmu.edu/isbs/vol38/iss1/48