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Hand gesture recognition using smooth wavelet packet transformation and hybrid CNN based on surface EMG and accelerometer signal
Wang, Le1,2; Fu, Jianting1; Chen, Hui1,2; Zheng, Bin1,2
2023-09-01
摘要Surface electromyography (sEMG), which has the advantages of being simple to acquire and quick to respond, is frequently utilized in domains like human-computer interface and prosthetic control as a control source for gesture recognition. Firstly, we propose a method to decompose the sEMG into the time-frequency domain in-formation using the smooth wavelet packet transform (SWPT), which has a faster processing speed compared to previous methods, such as the continuous wavelet transform (CWT) and wavelet packet transform (WPT), requiring only 12% of the time consumption of CWT and 66% of WPT. Secondly, to increase the recognition accuracy of hand gestures, a network model was built using a combination of convolutional neural network (CNN), long short term memory (LSTM), and convolutional block attention module (CBAM) with the acceler-ometer (ACC) data fusion. With an average accuracy of 92.159%, this approach significantly outperformed other similar research studies when evaluated on the public dataset NapiroDB5.
关键词Gesture recognition sEMG SWPT CNN LSTM Accelerometer
DOI10.1016/j.bspc.2023.105141
发表期刊BIOMEDICAL SIGNAL PROCESSING AND CONTROL
ISSN1746-8094
卷号86页码:10
通讯作者Zheng, Bin(zhengbin@cigit.ac.cn)
收录类别SCI
WOS记录号WOS:001040092300001
语种英语