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Sensor Fusion for Myoelectric Control Based on Deep Learning With Recurrent Convolutional Neural Networks

机译：基于深度学习与经常性卷积神经网络的磁体控制传感器融合

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摘要

Abstract Electromyogram (EMG) signal decoding is the essential part of myoelectric control. However, traditional machine learning methods lack the capability of learning and expressing the information contained in EMG signals, and the robustness of the myoelectric control system is not sufficient for real life applications. In this article, a novel model based on recurrent convolutional neural networks (RCNNs) is proposed for hand movement classification and tested on the noninvasive EMG dataset. The proposed model uses deep architecture, which has advantages of dealing with complex time‐series data, such as EMG signals. Transfer learning is used in the training of multimodal model. The classification performance is compared with support vector machine (SVM) and convolutional neural networks (CNNs) on the same dataset. To improve the adaptability to the effect of arm movements, we fused the EMG signals and acceleration data that are the multimodal input of the model. The parameter transferring of deep neural networks is used to accelerate the training process and avoid over‐fitting. The experimental results show that time domain input and 1‐dimensional convolution have higher accuracy in the RCNN model. Compared with SVM and CNNs, the proposed model has higher classification accuracy. Sensor fusion can improve the model performance in the condition of arm movements. The RCNN model is a promising decoder of EMG and the sensor fusion can increase the accuracy and robustness of the myoelectric control system.
机译：摘要电灰度（EMG）信号解码是磁铁控制的重要组成部分。然而，传统的机器学习方法缺乏学习和表达包含在EMG信号中的信息的能力，并且肌电控制系统的鲁棒性不足以用于现实生活应用。在本文中，提出了一种基于经常性卷积神经网络（RCNN）的新型模型，用于手动分类并在非侵入性EMG数据集上进行测试。该建议的模型使用深度架构，该架构具有处理复杂的时间序列数据，例如EMG信号。转移学习用于多式式模型的培训。将分类性能与支持向量机（SVM）和同一数据集上的卷积神经网络（CNNS）进行比较。为了提高对臂运动效果的适应性，我们融合了模型的多模式输入的EMG信号和加速度数据。深神经网络的参数传输用于加速训练过程并避免过度拟合。实验结果表明，时域输入和1维卷积在RCNN模型中具有更高的精度。与SVM和CNN相比，所提出的模型具有更高的分类精度。传感器融合可以提高臂运动条件下的模型性能。 RCNN模型是EMG的有希望的解码器，传感器融合可以提高肌电控制系统的精度和鲁棒性。

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