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.
CITATION STYLE
Yeh, C. W., Pan, T. Y., & Hu, M. C. (2017). A sensor-based official basketball referee signals recognition system using deep belief networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10132 LNCS, pp. 565–575). Springer Verlag. https://doi.org/10.1007/978-3-319-51811-4_46
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