Chih-Ta Yen1,*, Tz-Yun Chen2, Un-Hung Chen3, Guo-Chang Wang3, Zong-Xian Chen3
CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 83-99, 2023, DOI:10.32604/cmc.2023.032739
Abstract A system for classifying four basic table tennis strokes using wearable devices and deep learning networks is proposed in this study. The wearable device consisted of a six-axis sensor, Raspberry Pi 3, and a power bank. Multiple kernel sizes were used in convolutional neural network (CNN) to evaluate their performance for extracting features. Moreover, a multiscale CNN with two kernel sizes was used to perform feature fusion at different scales in a concatenated manner. The CNN achieved recognition of the four table tennis strokes. Experimental data were obtained from 20 research participants who wore sensors on the back of their… More >
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