基于时序二维化和卷积特征融合的表面肌电信号分类方法

The traditional pattern recognition methods are prone to ignore characteristics of non-linearity and timing in the classification of surface electromyography(sEMG). Aiming at this problem, a sEMG signal classification method based on temporal two-dimensionalization and convolution feature fusion is proposed. Temporal two-dimensionalization is realized by Gramian angular field conversion to preserve the time dependence and correlation of original time series of sEMG. To highlight the local information and fully retain details simultaneously, a capsule network and a convolutional neural network are introduced to extract features together. In addition, the feature fusion is performed to realize the gesture recognition under different conditions. Experimental results show that the proposed method is more robust than other classification methods and it effectively enhances the electrode offset and the overall recognition level of hand movements facing new objects.