Sensor-based Badminton Stroke Classification by Machine Learning Methods

Juyi Lin, Chia Wei Chang, Tsi Ui Ik*, Yu-Chee Tseng

*此作品的通信作者

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

The use of stroke types is frequently the decisive factor in a well-matched badminton competition. It is essential to have stroke by stroke logs in practices and competitions. In this work, a smart racket system is developed to recognize and record each stroke. The racket is equipped with an acoustic sensor and an inertial measurement unit, and sensing data are transmitted to a smartphone via BT connections for further processing. The shuttlecock hitting events are detected by utilizing voiceprint, and the stroke types are classified by various machine learning algorithms including random forests, Bayesian models, and support vector machines. In our experiments, over 99.9% hitting events can be detected by the proposed voiceprint-based algorithm that outperforms most commercial solutions on the market. In addition, the average accuracy of stroke type classification is 96.5% by personalized models and 84% by generalized models.

原文English
主出版物標題Proceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面94-100
頁數7
ISBN(電子)9781665404839
DOIs
出版狀態Published - 3 12月 2020
事件1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020 - Taipei, Taiwan
持續時間: 3 12月 20205 12月 2020

出版系列

名字Proceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020

Conference

Conference1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020
國家/地區Taiwan
城市Taipei
期間3/12/205/12/20

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