Xia Peng, Hu Jie, Peng Yinghong
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Artif Organs. 2018 May;42(5):E67-E77. doi: 10.1111/aor.13004. Epub 2017 Oct 25.
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.
提出了一种基于深度学习的新型模型，用于从多通道肌电图（EMG）信号中估计用于肌电控制的运动学信息。肢体运动的神经信息嵌入在受各种因素影响的EMG信号中。为了克服信号变异性的负面影响，所提出的模型采用了结合卷积神经网络（CNN）和循环神经网络（RNN）的深度架构。EMG信号被转换为时频帧作为模型的输入。通过使用梯度下降和反向传播过程训练的模型来估计肢体运动。我们在八名健康受试者中测试了该模型对肢体运动的同步和比例估计，并在同一数据集上与支持向量回归（SVR）和CNN进行了比较。实验研究表明，所提出的模型具有更高的估计精度和更好的时间鲁棒性。与单独使用CNN相比，CNN和RNN的组合可以提高模型性能。深度架构模型在EMG解码方面很有前景，网络结构的优化可以提高准确性和鲁棒性。
