计算机应用与软件2024，Vol.41Issue(11)：220-227,8.DOI:10.3969/j.issn.1000-386x.2024.11.031
基于混合空洞卷积CNN和BiGRU的表面肌电信号手势识别
SURFACE EMG SIGNAL GESTURE RECOGNITION BASED ON HYBRID DILATED CONVOLUTION CNN AND BIGRU
摘要
Abstract
Aimed at the problem of low accuracy and large amount of calculation for gesture recognition based on surface electromyography(sEMG),a method for sEMG gesture recognition based on a hybrid dilated convolutional neural network combining bidirectional gated recurrent unit and attention mechanism is proposed.Compared with the ordinary CNN,HDC can expand the receptive field,reduce over-fitting,and extract more features by setting the dilation rate to parity hybrid and different sizes.The BiGRU module can extract and process the timing features of the data well,and attention module gives greater weight to important features,which can improve accuracy.The accuracy rates of 92.72%and 97.85%were achieved on the NinaproDB1 dataset and the self-acquisition dataset,respectively.关键词
表面肌电信号/手势识别/混合空洞卷积/双向门控循环单元/Attention机制Key words
sEMG/Gesture recognition/Hybrid dilated convolution/BiGRU/Attention mechanism分类
信息技术与安全科学引用本文复制引用
张凯,陈峰..基于混合空洞卷积CNN和BiGRU的表面肌电信号手势识别[J].计算机应用与软件,2024,41(11):220-227,8.基金项目
江苏省青年基金项目(BK20180953). （BK20180953）
