Cross-User Electromyography Pattern Recognition Based on a Novel Spatial-Temporal Graph Convolutional Network

With the goal of promoting the development of myoelectric control technology, this paper focuses on exploring graph neural network (GNN) based robust electromyography (EMG) pattern recognition solutions. Given that high-density surface EMG (HD-sEMG) signal contains rich temporal and spatial informat...

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Vydané v:IEEE transactions on neural systems and rehabilitation engineering Ročník 32; s. 72 - 82
Hlavní autori: Xu, Mengjuan, Chen, Xiang, Ruan, Yuwen, Zhang, Xu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Ablation
Algorithms
Artificial neural networks
Convolutional neural networks
Electrodes
Electromyography
Electromyography - methods
EMG
Experiments
Feature extraction
gesture recognition
Gestures
graph neural network
Graph neural networks
Humans
Modules
Muscles
Myoelectric control
Myoelectricity
Neural networks
Neural Networks, Computer
Pattern recognition
Spatial data
Task analysis
Transfer learning
User training
user-independent
ISSN:1534-4320, 1558-0210, 1558-0210
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Shrnutí:With the goal of promoting the development of myoelectric control technology, this paper focuses on exploring graph neural network (GNN) based robust electromyography (EMG) pattern recognition solutions. Given that high-density surface EMG (HD-sEMG) signal contains rich temporal and spatial information, the multi-view spatial-temporal graph convolutional network (MSTGCN)is adopted as the basic classifier, and a feature extraction convolutional neural network (CNN) module is designed and integrated into MSTGCN to generate a new model called CNN-MSTGCN. The EMG pattern recognition experiments are conducted on HD-sEMG data of 17 gestures from 11 subjects. The ablation experiments show that each functional module of the proposed CNN-MSTGCN network has played a more or less positive role in improving the performance of EMG pattern recognition. The user-independent recognition experiments and the transfer learning-based cross-user recognition experiments verify the advantages of the proposed CNN-MSTGCN network in improving recognition rate and reducing user training burden. In the user-independent recognition experiments, CNN-MSTGCN achieves the recognition rate of 68%, which is significantly better than those obtained by residual network-50 (ResNet50, 47.5%, p < 0.001) and long-short-term-memory (LSTM, 57.1%, p=0.045). In the transfer learning-based cross-user recognition experiments, TL-CMSTGCN achieves an impressive recognition rate of 92.3%, which is significantly superior to both TL-ResNet50 (84.6%, p = 0.003) and TL-LSTM (85.3%, p = 0.008). The research results of this paper indicate that GNN has certain advantages in overcoming the impact of individual differences, and can be used to provide possible solutions for achieving robust EMG pattern recognition technology.
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ISSN:1534-4320
1558-0210
1558-0210
DOI:10.1109/TNSRE.2023.3342050