电子测量与仪器学报

赵世昊,周建华,伏云发.注意力机制 CNN 结合肌电特征矩阵的手势识别研究[J].电子测量与仪器学报,2023,37(6):59-67

注意力机制 CNN 结合肌电特征矩阵的手势识别研究

Investigation of gesture recognition using attention mechanism CNNcombined electromyography feature matrix

DOI：

英文关键词:gesture recognition electromyography feature matrix ( EFM) efficient channel attention ( ECA) convolutional neural network (CNN)

基金项目:国家自然科学基金(61763022)项目资助

作者	单位
赵世昊	1. 昆明理工大学信息工程与自动化学院，2. 昆明理工大学脑认知与脑机智能融合创新团队
周建华	1. 昆明理工大学信息工程与自动化学院，2. 昆明理工大学脑认知与脑机智能融合创新团队
伏云发	1. 昆明理工大学信息工程与自动化学院，2. 昆明理工大学脑认知与脑机智能融合创新团队

Author	Institution
Zhao Shihao	1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology,2. Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology
Zhou Jianhua	1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology,2. Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology
Fu Yunfa	1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology,2. Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology

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中文摘要:

当前基于卷积神经网络(CNN)的手势识别研究集中于增加网络深度,较少关注改善样本数据分布带来的性能提升。针对此类问题,提出一种量化表面肌电信号(sEMG)特征相关性的肌电特征矩阵(EFM)样本输入有效通道注意力(ECA)机制 CNN,用于识别 NinaproDB1 中 52 类手势。首先使用时间窗截取低通滤波后的 sEMG,计算多种信号时域特征;然后利用笛卡尔积组合并相乘不同特征,对特征相乘值进行归一化后得到 EFM。同时,引入 ECA 机制使网络关注重要的深层特征,从而提升手势分类效果。分别输入 sEMG、肌电时域特征和 EFM 到注意力机制 CNN 进行手势识别,EFM 识别准确率最高,达到了 86. 39%, 高于近年来手势识别研究方法精度。验证了提出方法的有效性,为多类别手势准确分类提供可行新方案。

英文摘要:

Current research on gesture recognition based on convolutional neural network (CNN) focuses on increasing the depth of the network, and pays less attention to improve the distribution of sample data which can brought the performance improvement. Aimed at these problems, a kind of electromyography feature matrix ( EFM) sample that quantifies the correlation of surface electromyography (sEMG) features is fed into the efficient channel attention (ECA) mechanism CNN, which is used to identify the 52 types gesture in NinaproDB1. Firstly, the time window is used to truncate the low-pass filtered sEMG and calculate various signal time domain features. Then, the cartesian product is used to combine and multiply different features. The EFM is obtained after normalizing the feature multiplication values. At the same time, ECA mechanism is introduced to make the network focus on the important deep features, thereby improving the effect of gesture classification. sEMG, EMG time-domain features and EFM are fed to the attention mechanism CNN respectively for gesture recognition. The recognition accuracy of EFM is the highest and reached 86. 39%, which is higher than the accuracy of gesture recognition research methods in recent years. The effectiveness of the proposed method is verified, and a feasible new scheme for accurate multi-category gesture classification is provided.

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