Abstract
In this work, we propose a novel sports referee training system based on wearable sensors and a real-time Official Referee Signal (ORS) segmentation/recognition method which can recognize 65 kinds basketball ORSs with the accuracy of 95.3%. A hybrid neural network named ORSNet is designed for recognizing gestures based on IMU signals. The proposed ORSNet involves convolution layers and recurrent layers to learn more representative features and correlations in temporal domain, respectively. A novel loss function and a weight sharing strategy are proposed to learn a more robust ORS recognition model. Moreover, we investigate the influence of applying a semi-supervised network in the proposed ORSNet.