Gesture recognition of continuous wavelet transform and deep convolution attention network-Reference-Cited by-同舟云学术

Gesture recognition of continuous wavelet transform and deep convolution attention network

Published:2023 Issue:6 Volume:20 Page:11139-11154
ISSN:1551-0018
Container-title:Mathematical Biosciences and Engineering
language:
Short-container-title:MBE

Author:

Liu Xiaoguang¹²,Zhang Mingjin¹²,Wang Jiawei¹²,Wang Xiaodong³,Liang Tie¹²,Li Jun¹²,Xiong Peng¹²,Liu Xiuling¹²

Affiliation:

1. College of Electronic and Information Engineering, Hebei University, Baoding, China

2. Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding, China

3. Affiliated Hospital of Hebei University, Baoding, China

Abstract

<abstract> <p>To solve the problem of missing data features using a deep convolutional neural network (DCNN), this paper proposes an improved gesture recognition method. The method first extracts the time-frequency spectrogram of surface electromyography (sEMG) using the continuous wavelet transform. Then, the Spatial Attention Module (SAM) is introduced to construct the DCNN-SAM model. The residual module is embedded to improve the feature representation of relevant regions, and reduces the problem of missing features. Finally, experiments with 10 different gestures are done for verification. The results validate that the recognition accuracy of the improved method is 96.1%. Compared with the DCNN, the accuracy is improved by about 6 percentage points.</p> </abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine

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