ABSTRACT
INTRODUCTION: Water polo upper limb external load monitoring cannot be currently measured accurately because of 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 (IMU) 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 IMU 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-intensity throwing and high-intensity throwing under training conditions. Athletes wore two IMU: 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 a 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. A total of 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: IMU and machine learning techniques can accurately classify standard overhead drill movements in elite women's water polo.