A novel attention-guided ECA-CNN architecture for sEMG-based gait classification

Zhangjie Wu; Minming Gu; Zhangjie Wu; Minming Gu

doi:10.3934/mbe.2023308

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 4: 7140-7153. doi: 10.3934/mbe.2023308

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Research article Special Issues

A novel attention-guided ECA-CNN architecture for sEMG-based gait classification

Zhangjie Wu ,
Minming Gu ^,

School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China

Academic Editor: Yang Kuang

Received: 08 November 2022 Revised: 12 January 2023 Accepted: 01 February 2023 Published: 10 February 2023

Gait recognition and classification technology is one of the essential technologies for detecting neurodegenerative dysfunction. This paper presents a gait classification model based on a convolutional neural network (CNN) with an efficient channel attention (ECA) module for gait detection applications using surface electromyographic (sEMG) signals. First, the sEMG sensor was used to collect the experimental sample data, and various gaits of different persons were collected to construct the sEMG signal data sets of different gaits. The CNN is used to extract the features of the one-dimensional input sEMG signal to obtain the feature vector, which is input into the ECA module to realize cross-channel interaction. Then, the next part of the convolutional layer is input to learn the signal features further. Finally, the model is output and tested to obtain the results. Comparative experiments show that the accuracy of the ECA-CNN network model can reach 97.75%.
- surface electromyography,
- convolutional neural networks,
- efficient channel attention mechanism,
- gait classification,
- neurodegenerative disease prevention
Citation: Zhangjie Wu, Minming Gu. A novel attention-guided ECA-CNN architecture for sEMG-based gait classification[J]. Mathematical Biosciences and Engineering, 2023, 20(4): 7140-7153. doi: 10.3934/mbe.2023308

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Abstract

Gait recognition and classification technology is one of the essential technologies for detecting neurodegenerative dysfunction. This paper presents a gait classification model based on a convolutional neural network (CNN) with an efficient channel attention (ECA) module for gait detection applications using surface electromyographic (sEMG) signals. First, the sEMG sensor was used to collect the experimental sample data, and various gaits of different persons were collected to construct the sEMG signal data sets of different gaits. The CNN is used to extract the features of the one-dimensional input sEMG signal to obtain the feature vector, which is input into the ECA module to realize cross-channel interaction. Then, the next part of the convolutional layer is input to learn the signal features further. Finally, the model is output and tested to obtain the results. Comparative experiments show that the accuracy of the ECA-CNN network model can reach 97.75%.

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