Title: Pattern recognition of surface electromyography based on multi-scale convolutional neural network with attention mechanism

Authors: Beibei Wang; Hui Zheng; Jing Jie; Miao Zhang; Yintao Ke; Yang Liu

Addresses: School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China ' School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China ' School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China ' School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China ' School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China ' School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China

Abstract: Natural control methods based on Surface Electromyography (sEMG) pattern recognition have been widely applied in the field of hand prostheses. However, the control robustness and accuracy are difficult to meet many real-life applications. This paper proposes a Multi-Scale Convolutional Neural Network (MSCNN) model based on the attention mechanism, which can automatically learn gesture features through convolution. The model generates features through convolution kernels of different sizes to achieve the fusion of features of different degrees firstly. After that, the attention mechanism is used to calculate the weights of different scales, and then the fused comprehensive features are obtained. The proposed model has been verified on the SIA_delsys_16_movement and NinaPro data sets. The experimental results showed that the proposed model has better classification accuracy, and the attention mechanism can validly improve the classification performance of the convolutional neural network.

Keywords: surface electromyography; convolutional neural network; gesture recognition; machine learning; attention mechanism.

DOI: 10.1504/IJWMC.2022.127594

International Journal of Wireless and Mobile Computing, 2022 Vol.23 No.3/4, pp.293 - 301

Received: 13 Dec 2021
Received in revised form: 14 Apr 2022
Accepted: 15 Apr 2022
Published online: 12 Dec 2022 *

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