Sensor-based Badminton Stroke Classification by Machine Learning Methods

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages94-100
Number of pages7
ISBN (Electronic)9780738142623
DOIs
StatePublished - Dec 2020
Event1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020 - Taipei, Taiwan
Duration: 3 Dec 20205 Dec 2020

Publication series

NameProceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020

Conference

Conference1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020
CountryTaiwan
CityTaipei
Period3/12/205/12/20

Keywords

  • Badminton stroke type
  • cloud service
  • machine learning
  • mobile platform
  • wearable technology

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