A Sensor-based Official Basketball Referee Signals Recognition System Using Deep Belief Networks

In a basketball game, basketball referees who have the responsibility to enforce the rules and maintain the order of the basketball game has only a brief moment to determine if an infraction has occurred, later they communicate with the scoring table using hand signals. In this paper, we propose a novel system which can not only recognize the basketball referees' signals but also communicate with the scoring table in real-time. Deep belief network and time-domain feature are utilized to analyze two heterogeneous signals, surface electromyography (sEMG) and three-axis accelerometer (ACC) to recognize dynamic gestures. Our recognition method is evaluated by a dataset of 9 various official hand signals performed by 11 subjects. Our recognition model achieves acceptable accuracy rate, which is 97.9% and 90.5% for 5-fold Cross Validation (5-foldCV) and Leave-One-Participant-Out Cross Validation (LOPOCV) experiments, respectively. The accuracy of LOPOCV experiment can be further improved to 94.3% by applying user calibration.