Badminton Activity Recognition and Player Assessment based on Motion Signals using Deep Residual Network
3
Issued Date
2022-01-01
Resource Type
ISSN
23270586
eISSN
23270594
Scopus ID
2-s2.0-85141939152
Journal Title
Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS
Volume
2022-October
Start Page
80
End Page
83
Rights Holder(s)
SCOPUS
Bibliographic Citation
Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS Vol.2022-October (2022) , 80-83
Suggested Citation
Mekruksavanich S., Jantawong P., Hnoohom N., Jitpattanakul A. Badminton Activity Recognition and Player Assessment based on Motion Signals using Deep Residual Network. Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS Vol.2022-October (2022) , 80-83. 83. doi:10.1109/ICSESS54813.2022.9930147 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/84333
Title
Badminton Activity Recognition and Player Assessment based on Motion Signals using Deep Residual Network
Author's Affiliation
Other Contributor(s)
Abstract
With the fast expansion of digital technologies and sporting events, interpreting sports data has become an immensely complicated endeavor. Internet-sourced sports big data exhibit a significant development trend. Big data in sports offer a wealth of information on sportspeople, coaching, athletics, swimming, and badminton. Today, various sports data are freely accessible, and incredible data analysis tools based on wearable sensors have been established, allowing us to investigate the usefulness of these data thoroughly. In this research, we investigate the detection of badminton action and player evaluation based on movement data captured by wearable sensors. Movement data captured by an accelerometer, gyroscope, and magnetometer are utilized for training and validating a classification model for badminton actions. In addition, the movement signals are used to train a player evaluation model employing a deep residual network. To assess our suggested technique, we utilized a publicly available benchmark dataset consisting of inertial measurement unit (IMU) sensors attached to every investigator's dominant wrist, palm, and both legs. The experimental findings indicate that the proposed deep residual network obtained good performance with a maximum accuracy of 98.00% for identifying badminton activities and 98.56% for evaluating badminton players.