Gesture recognition of continuous wavelet transform and deep convolution attention network

Xiaoguang Liu; Mingjin Zhang; Jiawei Wang; Xiaodong Wang; Tie Liang; Jun Li; Peng Xiong; Xiuling Liu

doi:10.3934/mbe.2023493

Gesture recognition of continuous wavelet transform and deep convolution attention network

Math Biosci Eng. 2023 Apr 25;20(6):11139-11154. doi: 10.3934/mbe.2023493.

Authors

Xiaoguang Liu^{1

2}, Mingjin Zhang^{1

2}, Jiawei Wang^{1

2}, Xiaodong Wang³, Tie Liang^{1

2}, Jun Li^{1

2}, Peng Xiong^{1

2}, Xiuling Liu^{1

2}

Affiliations

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.

PMID: 37322975
DOI: 10.3934/mbe.2023493

Abstract

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.

Keywords: DCNN; SAM; continuous wavelet transform; gesture recognition; residual module; sEMG.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Electromyography / methods
Gestures*
Neural Networks, Computer
Wavelet Analysis*