融合双层注意力与多流卷积的肌电手势识别记忆网络

针对表面肌电信号(surface electromyography, sEMG)手势识别使用卷积神经网络(convolutional neural network, CNN)提取特征不够充分，且忽略时序信息而导致识别精度不高的问题，本文创新性地提出了一种融合双层注意力与多流卷积神经网络(multi-stream convolutional neural network, MS-CNN)的sEMG手势识别记忆网络模型。首先，利用滑动窗口生成的表面肌电图像作为该模型的输入；然后在MS-CNN中嵌入通道注意力层(channel attention module, CAM),弱化无关信息，使网络能够更加专注sEMG的有效特征；其次，通过长短期记忆网络(long short term memory network, LSTM)对输入的特征进行时序上的激励，关注更多sEMG的时序信息，让网络在时间维度上拥有更强的学习能力；最后，采用时序注意力(time-sequence attention, TSA)层对LSTM的状态进行关注，从而更好地学习重要肌肉信息，提高手势识别精度。在NinaPro数据集上...

Incorporating two-layer attention and multi-stream convolutional for sEMG gesture recognition memory networks

To solve the problems of insufficient feature extraction and ignoring time series information using convolutional neural network (CNN) to extract features of surface electromyography (sEMG) gesture recognition,resulting in low recognition accuracy,this paper innovatively proposed incorporating two-layer attention and multi-stream convolutional neural network (MS-CNN) for sEMG gesture recognition memory networks model.Firstly,sEMG images generated by sliding windows are used as the input of this model;then the channel attention module (CAM) is embedded in an MS-CNN to weaken irrelevant information and enable the network to focus more on the key features of sEMG;secondly,using the long short term memory network (LSTM) for motivating the input features in time-sequence to pay more attention to the time-sequence information of sEMG,which enables network has a stronger learning ability in the time-dimension;finally,focusing on states of LSTM by time-sequence attention (TSA) layer to learn important muscle information better for improving gesture recognition accuracy.Performing experimental tests on the NinaPro dataset and the results show that the gesture recognition accuracy in the DB1 dataset and DB2 dataset has reached 86.42% and 80.60% using the network model proposed by this paper,which is higher than most mainstream models, which fully verifies the effectiveness of the model.