《计算机应用研究》|Application Research of Computers

融合注意力机制的多流卷积肌电手势识别网络

Multi-stream convolutional myoelectric gesture recognition networks fusing attentional mechanisms

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作者 李沿宏,江茜,邹可,袁学东
机构 四川大学 计算机学院,成都 610065
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文章编号 1001-3695(2021)11-009-3258-06
DOI 10.19734/j.issn.1001-3695.2021.04.0100
摘要 针对目前表面肌电信号(surface electromyography,sEMG)端到端手势识别特征提取不充分、多手势识别准确率不高的问题,提出一种融合注意力机制的多流卷积肌电手势识别网络模型。该模型通过滑动窗口将多通道时域sEMG生成肌电子图,并使用多流卷积神经网络充分提取每个采集通道sEMG的语义特征,然后将其聚合得到丰富的多通道手势语义特征;同时从时间和特征通道维度上计算语义特征的注意力分布图,强化有用特征并弱化无用特征,进一步提高多手势识别准确率。实验使用Ninapro数据集进行训练和测试,并与主流的肌电手势识别模型进行对比。实验结果表明,该模型在识别准确率上具有更好的表现,证明了该模型的有效性。
关键词 手势识别; 表面肌电信号; 卷积神经网络; 注意力机制
基金项目 四川省重点研发计划资助项目(2020YFG0075)
本文URL http://www.arocmag.com/article/01-2021-11-009.html
英文标题 Multi-stream convolutional myoelectric gesture recognition networks fusing attentional mechanisms
作者英文名 Li Yanhong, Jiang Xi, Zou Ke, Yuan Xuedong
机构英文名 College of Computer Science,Sichuan University,Chengdu 610065,China
英文摘要 In order to solve the problems of insufficient feature extraction of sEMG end-to-end gesture recognition and low accuracy of multi-gesture recognition, this paper proposed a multi-stream convolutional EMG gesture recognition network model fusing attention mechanism. The proposed model generated sub-myoelectrograms from multi-channel time-domain sEMG through sliding windows and used a multi-stream convolutional neural network to fully extract the semantic features of each sampled channel sEMG, and then aggregated them to obtain rich multi-channel gesture semantic features. At the same time, the proposed model calculated the attention distribution map of semantic features from temporal and feature channel dimensions to strengthen the useful features and weaken the useless features, further improved the accuracy of multi-gesture recognition. The experiment used the Ninapro dataset for training and testing and compared with the mainstream electromyogram recognition model. The experimental results show that the proposed model has better performance in recognition accuracy, which proves the effectiveness of the proposed model.
英文关键词 gesture recognition; sEMG; convolution neural network; attention mechanism
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收稿日期 2021/4/6
修回日期 2021/5/31
页码 3258-3263
中图分类号 TP391.41
文献标志码 A