An, Chen-Chi
(Graduate Institute of Sports Training, University of Taipei, Taiwan)
,
Hsu, Hua-Yi
(Department of Mechanical Engineering, National Taipei University of Technology, Taiwan)
,
Sun, Yin-Tung
(Graduate Institute of Manufacturing Technology, National Taipei University of Technology, Taiwan)
,
Ke, Li-Duan
(Graduate Institute of Mechatronic Engineering, National Taipei University of Technology, Taiwan)
,
Hsu, Tai-Ger
(Graduate Institute of Sports Training, University of Taipei, Taiwan)
,
Ting, Chen-Ching
(Department of Mechanical Engineering, National Taipei University of Technology, Taiwan)
Abstract This study has successfully developed an audio analyzer system to identify the stroke position on table tennis racket instantaneously, which can effectively help the technical training. It’s well known, a good stroke is often determined by the exact stroke position on table tennis ra...
Abstract This study has successfully developed an audio analyzer system to identify the stroke position on table tennis racket instantaneously, which can effectively help the technical training. It’s well known, a good stroke is often determined by the exact stroke position on table tennis racket during the play. Therefore, instantaneous identification of the stroke position on table tennis racket during play can provide good adjusting reference of action and effectively help the technical training. In this research, this developed audio analyzer consists of a microphone and the analysing program with fast Fourier transform (FFT) using LabVIEW software. The face of table tennis racket is divided into four blocks which are the center, the left-of-center, the right-of-center, and the trail. The stroke sound generated between table tennis racket and ball during play is captured by the microphone and synchronously analyzed through FFT to determine the principal frequency, where the different stroke position on the table tennis racket yields different principal frequency. The results of calibration show that every table tennis racket has three characteristic principal frequencies on the four blocks, which can distinguish center, right/left, and trail respectively. The positional verification rate of the center position identification is all higher than 90% even up to 100% which can assist technical training. Highlights This study has developed an audio analyzer system to identify the stroke position. This system can effectively help table tennis skill training. It’s a novel and unique idea which uses FFT to do stroke position identification. The whole analyzer system is home made with rigorous measurements and calibration. The identification rate of the center position is higher than 90% even up to 100%.
Abstract This study has successfully developed an audio analyzer system to identify the stroke position on table tennis racket instantaneously, which can effectively help the technical training. It’s well known, a good stroke is often determined by the exact stroke position on table tennis racket during the play. Therefore, instantaneous identification of the stroke position on table tennis racket during play can provide good adjusting reference of action and effectively help the technical training. In this research, this developed audio analyzer consists of a microphone and the analysing program with fast Fourier transform (FFT) using LabVIEW software. The face of table tennis racket is divided into four blocks which are the center, the left-of-center, the right-of-center, and the trail. The stroke sound generated between table tennis racket and ball during play is captured by the microphone and synchronously analyzed through FFT to determine the principal frequency, where the different stroke position on the table tennis racket yields different principal frequency. The results of calibration show that every table tennis racket has three characteristic principal frequencies on the four blocks, which can distinguish center, right/left, and trail respectively. The positional verification rate of the center position identification is all higher than 90% even up to 100% which can assist technical training. Highlights This study has developed an audio analyzer system to identify the stroke position. This system can effectively help table tennis skill training. It’s a novel and unique idea which uses FFT to do stroke position identification. The whole analyzer system is home made with rigorous measurements and calibration. The identification rate of the center position is higher than 90% even up to 100%.
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