A Dual Inertial Measurement Unit System for Classifying Standard Overhead Drill Movements in Elite Women’s Water Polo

Abstract

Introduction Water polo upper limb external load monitoring cannot be currently measured accurately due to technological and methodological challenges. This is problematic as large fluctuations in overhead movement volume and intensity may affect performance and alter injury risk. Inertial measurement units (IMUs) and machine learning techniques have been shown to accurately classify overhead movements in other sports. We investigated the model accuracy and class precision, sensitivity and specificity of IMUs and machine learning techniques to classify standard overhead drill movements in elite women’s water polo. Methods Ten women’s water polo players performed standard drills of swimming, blocking, low and high intensity throwing under training conditions. Athletes wore two IMUs: one on the upper back and the other on the distal forearm. Each movement was videoed and coded to a standard overhead drill movement. IMU and coded video data were merged to verify the IMU detected activity classification of each movement to that of the video. Data were partitioned into a training and test set and used to form a decision tree algorithm. Model accuracy and class precision, sensitivity and specificity were assessed. Results IMU resultant acceleration and angular velocity values displayed drill specific values. 194 activities were identified by the model in the test set with 8 activities being incorrectly classified. Model accuracy was 95.88%. Percentage class precision, sensitivity and specificity were as follows: blocking (96.15, 86.21, 99.39), high intensity throwing (100, 100, 100), low intensity throwing (93.48, 93.48, 97.97) and swimming (94.81, 98.65, 96.67). Conclusions IMUs and machine learning techniques can accurately classify standard overhead drill movements in elite women’s water polo.