Prediction of movement in handball with the use of inertial measurement units and machine learning

Inertial Measurement Units (IMU) and machine learning are strong tools in quantifying physical demands in sports, such as handball. However, the detection of both locomotion and throw events simultaneously has not been a topic for much investigation. Wherefore, the aim of this study was to publicise a method for training an extreme gradient boosting model capable of identifying low intensity, dynamic, running and throw events. Twelve adults with varying experience in handball wore an IMU on the back while being video recorded during a handball match. The video recordings were used for annotating the four events. Due to the small sample size, a leave-one-subject-out (LOSO) approach was conducted for the modelling and feature selection. The model had issues identifying dynamic movements (F1-score = 0.66 +/- 0.07), whereas throw (F1-score = 0.95 +/- 0.05), low intensity (F1-score = 0.93 +/- 0.02) and running (F1-score = 0.86 +/- 0.05) were easier to identify. Features such as IQR and first zero crossing for most of the kinematic characteristics were among the most important features for the model. Therefore, it is recommended for future research to look into these two features, while also using a LOSO approach to decrease likelihood of artificially high model performance.

Automated summary

The aim of this study was to propose a method for training an extreme gradient boosting model capable of identifying low intensity, dynamic, running and throw events simultaneously in handball. Twelve adults with varying experience in handball wore an IMU and were video recorded during a handball match. Features such as IQR and first zero crossing were found to be important for the model. The model had difficulty identifying dynamic movements, but performed well in identifying throw, low intensity, and running events.