|Title:||Feature Fusion-Based Deep Learning Network to Recognize Table Tennis Actions||Authors:||Yen, Chih-Ta
|Keywords:||Wearable devices;deep learning;six-axis sensor;feature fusion;multi-scale convolutional neural networks;action recognition||Issue Date:||1-Jan-2023||Publisher:||TECH SCIENCE PRESS||Journal Volume:||74||Journal Issue:||1||Start page/Pages:||83-99||Source:||CMC-COMPUTERS MATERIALS & CONTINUA||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 partic-ipants who wore sensors on the back of their hands while performing the four table tennis strokes in a laboratory environment. The data were collected to verify the performance of the proposed models for wearable devices. Finally, the sensor and multi-scale CNN designed in this study achieved accuracy and F1 scores of 99.58% and 99.16%, respectively, for the four strokes. The accuracy for five-fold cross validation was 99.87%. This result also shows that the multi-scale convolutional neural network has better robustness after five-fold cross validation.
|Appears in Collections:||電機工程學系|
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