This paper proposes a feature fusion-based improved capsule network (FFiCAPS) to improve the performance of surface electromyogram (sEMG) signal recognition with the purpose of distinguishing hand gestures. Current deep learning models, especially convolution neural networks (CNNs), only take into account the existence of certain features and ignore the correlation among features. To overcome this problem, FFiCAPS adopts the capsule network with a feature fusion method. In order to provide rich information, sEMG signal information and feature data are incorporated together to form new features as input. Improvements made on capsule network are multilayer convolution layer and e-Squash function. The former aggregates feature maps learned by different layers and kernel sizes to extract information in a multiscale and multiangle manner, while the latter grows faster at later stages to strengthen the sensitivity of this model to capsule length changes. Finally, simulation experiments show that the proposed method exceeds other eight methods in overall accuracy under the condition of electrode displacement (86.58%) and among subjects (82.12%), with a notable improvement in recognizing hand open and radial flexion, respectively.
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