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      EMG-Based Estimation of Limb Movement Using Deep Learning With Recurrent Convolutional Neural Networks.

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          Abstract

          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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          Author and article information

          Journal
          Artif Organs
          Artificial organs
          Wiley
          1525-1594
          0160-564X
          May 2018
          : 42
          : 5
          Affiliations
          [1 ] School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.
          Article
          10.1111/aor.13004
          29068076
          3598e209-e5c6-4cc3-a3fd-a295113d5288
          History

          Electromyogram,-Recurrent neural network,-Myoelectric control,-Deep learning,-Convolutional neural network

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