Automatic detection of passing and shooting in water polo using machine learning: a feasibility study

ABSTRACT

There is currently no efficient way to quantify overhead throwing volume in water polo. Therefore, this study aimed to test the feasibility of a method to detect passes and shots in water polo automatically using inertial measurement units (IMU) and machine-learning algorithms. Eight water polo players wore one IMU sensor on the wrist (dominant hand) and one on the sacrum during six practices each. Sessions were filmed with a video camera and manually tagged for individual shots or passes. Data were synchronised between video tagging and IMU sensors using a cross-correlation approach. Support vector machine (SVM) and artificial neural networks (ANN) were compared based on sensitivity and specificity for identifying shots and passes. A total of 7294 actions were identified during the training sessions, including 945 shots and 5361 passes. Using SVM, passes and shots together were identified with 94.4% (95%CI = 91.8–96.4) sensitivity and 93.6% (95%CI = 91.4–95.4) specificity. Using ANN yielded similar sensitivity (93.0% [95%CI = 90.1–95.1]) and specificity (93.4% [95%CI = 91.1 = 95.2]). The results suggest that this method of identifying overhead throwing motions with IMU has potential for future field applications. A set-up with one single sensor at the wrist can suffice to measure these activities in water polo.

KEYWORDS:

• Water polo
• inertial measurement unit
• machine learning
• workload

Acknowledgments

The research team would like to thank Water Polo Canada and Institut National du Sport du Québec for granting access to their staff and facilities. More specifically, thank you to Evelyne Dubé for her assistance with data collection, as well as Lily Dong, Matt Slopecki, Caitlin Mazurek and Harry Brown for tagging the video footage.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed here

Additional information

Funding

This work was supported by the Ministère de l'Éducation du Québec and Research, Innovation and Dissemination of Information Program (PRIDI4).