Classification of Australian football kick types in-situation via ankle-mounted inertial measurement units

ABSTRACT

The utility of inertial measurement units (IMUs) for sporting skill and performance analysis during training and competition is advantageous for enhancing the objectivity of athlete monitoring. This study aimed to classify Australian Rules football (AF) kick types in an applied environment using ankle-mounted IMUs. IMUs and video capture of a controlled protocol, including four kick types at varying distances, were recorded during a single testing session with female AF athletes (n = 20). Processed IMU data were modelled using support vector machine classifier, random forest, and k-nearest neighbour algorithms under a 2-Kick, 4-Kick, and kick distance (10, 20, 30 m) conditions. The random forest model showed the highest results for overall classification accuracy (83% 2-Kick and 80% 4-Kick), test F1-score (0.76 2-Kick and 0.81 4-Kick), and AUC score (0.58 2-Kick and 0.60 4-Kick). Kick distance classification showed a model test and class weighted F1-score of 0.63 and overall accuracy of 64%, respectively. This study highlights the potential for an applied semi-automated AF training kick detection and type classification system using IMUs.

KEYWORDS:

• Wearable inertial sensors
• Australian rules football
• skill analysis

Acknowledgments

There was no financial assistance associated with this study.

The authors would like to thank the research assistants who helped during in data collection process, also the athletes who generously gave their time to take part in this study.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors received no specific funding for this work.