Abstract
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.
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Acknowledgments
This research was supported by the Ministry of Science and Technology (contracts MOST-105-2221-E-006-066-MY3 and MOST-103-2221-E-006-157-MY2), Taiwan.
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Yeh, CW., Pan, TY., Hu, MC. (2017). A Sensor-Based Official Basketball Referee Signals Recognition System Using Deep Belief Networks. In: Amsaleg, L., Guðmundsson, G., Gurrin, C., Jónsson, B., Satoh, S. (eds) MultiMedia Modeling. MMM 2017. Lecture Notes in Computer Science(), vol 10132. Springer, Cham. https://doi.org/10.1007/978-3-319-51811-4_46
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DOI: https://doi.org/10.1007/978-3-319-51811-4_46
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