传感技术学报2024，Vol.37Issue(2)：278-287,10.DOI:10.3969/j.issn.1004-1699.2024.02.012
一种基于循环时空深度神经网络的手势识别方法
A Gesture Recognition Method Based on Recurrent Spatiotemporal Depth Neural Network
摘要
Abstract
To solve the problem of weak robustness and low precision of existing hand gesture recognition models induced by lack of spa-tiotemporal information,a hand gesture recognition model based on recurrent spatial and temporal deep neural network is proposed to improve the characterization ability for surface EMG(sEMG)signals.Firstly,a multi-channel convolutional neural network is designed and integrated into the bidirectional recurrent neural network to extract the spatiotemporal characteristics information with strong dis-crimination.Secondly,channel attention mechanism is used to catch the channel importance information in spatiotemporal characteris-tics,then an attention module based on spatiotemporal characteristics is designed to further enhance the spatiotemporal characteristics information.Thirdly,based on the ideology of feature pyramid network,a multi-scale feature fusion module is designed to acquire multi-stage feature information based on multi-scale and multi-angle aspects to improve the decoding ability of the model to electromyography signals.Finally,the proposed hand gesture recognition model is tested based on a large hand gesture recognition database of Ninapro.The results show that the representation capability for sEMg signals is effectively improved by the proposed method.It provides reference for the deep learning modeling work of human body hand gesture recognition.关键词
手势识别/表面肌电信号/神经网络/特征融合/注意力机制Key words
gesture recognition/surface electromyography signals/neural network/feature fusion/attention mechanism分类
信息技术与安全科学引用本文复制引用
杨旭升,范京哲,胡佛,张文安..一种基于循环时空深度神经网络的手势识别方法[J].传感技术学报,2024,37(2):278-287,10.基金项目
浙江省"尖兵""领雁"研发攻关计划项目(2022C03114) （2022C03114）
国家自然科学基金项目(61903335) （61903335）
