CSAC-Net: Fast Adaptive sEMG Recognition through Attention Convolution Network and Model-Agnostic Meta-Learning

Gesture recognition through surface electromyography (sEMG) provides a new method for the control algorithm of bionic limbs, which is a promising technology in the field of human-computer interaction. However, subject specificity of sEMG along with the offset of the electrode makes it challenging to...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-05, Vol.22 (10), p.3661
Main Authors: Fan, Xinchen, Zou, Lancheng, Liu, Ziwu, He, Yanru, Zou, Lian, Chi, Ruan
Format: Article
Language:English
Subjects:
Ablation
Accuracy
Adaptation
Algorithms
Analysis
attention convolution network
Bionics
Classification
Control theory
Datasets
Deep learning
Discriminant analysis
Electrodes
Electromyography
Embedded systems
Gesture recognition
Machine learning
meta-learning
Neural networks
short time Fourier transform
Support vector machines
surface electromyography
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Summary:Gesture recognition through surface electromyography (sEMG) provides a new method for the control algorithm of bionic limbs, which is a promising technology in the field of human-computer interaction. However, subject specificity of sEMG along with the offset of the electrode makes it challenging to develop a model that can quickly adapt to new subjects. In view of this, we introduce a new deep neural network called CSAC-Net. Firstly, we extract the time-frequency feature from the raw signal, which contains rich information. Secondly, we design a convolutional neural network supplemented by an attention mechanism for further feature extraction. Additionally, we propose to utilize model-agnostic meta-learning to adapt to new subjects and this learning strategy achieves better results than the state-of-the-art methods. By the basic experiment on CapgMyo and three ablation studies, we demonstrate the advancement of CSAC-Net.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22103661