Journal
ARTIFICIAL ORGANS
Volume 42, Issue 5, Pages E67-E77Publisher
WILEY
DOI: 10.1111/aor.13004
Keywords
Electromyogram; Myoelectric control; Deep learning; Convolutional neural network; Recurrent neural network
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Funding
- Shanghai Committee of Science and Technology [14142200700]
- National Basic Research Program of China (973 Program) [2011CB707503]
- National Natural Science Foundation of China [51605302, 51475288]
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A novel model based on deep learning is proposed to estimate kinematic information for myoelectric control from multi-channel electromyogram (EMG) signals. The neural information of limb movement is embedded in EMG signals that are influenced by all kinds of factors. In order to overcome the negative effects of variability in signals, the proposed model employs the deep architecture combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The EMG signals are transformed to time-frequency frames as the input to the model. The limb movement is estimated by the model that is trained with the gradient descent and backpropagation procedure. We tested the model for simultaneous and proportional estimation of limb movement in eight healthy subjects and compared it with support vector regression (SVR) and CNNs on the same data set. The experimental studies show that the proposed model has higher estimation accuracy and better robustness with respect to time. The combination of CNNs and RNNs can improve the model performance compared with using CNNs alone. The model of deep architecture is promising in EMG decoding and optimization of network structures can increase the accuracy and robustness.
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