TY - GEN
T1 - A sensor-based official basketball referee signals recognition system using deep belief networks
AU - Yeh, Chung Wei
AU - Pan, Tse Yu
AU - Hu, Min Chun
N1 - Funding Information:
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.
Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85009743270
UR - https://www.scopus.com/pages/publications/85009743270#tab=citedBy
U2 - 10.1007/978-3-319-51811-4_46
DO - 10.1007/978-3-319-51811-4_46
M3 - Conference contribution
AN - SCOPUS:85009743270
SN - 9783319518107
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 565
EP - 575
BT - MultiMedia Modeling - 23rd International Conference, MMM 2017, Proceedings
A2 - Amsaleg, Laurent
A2 - Gudmundsson, Gylfi Thór
A2 - Gurrin, Cathal
A2 - Jónsson, Björn Thór
A2 - Satoh, Shin’ichi
PB - Springer Verlag
T2 - 23rd International Conference on MultiMedia Modeling, MMM 2017
Y2 - 4 January 2017 through 6 January 2017
ER -