融合多元经验模态分解与Hilbert空间填充曲线的sEMG处理方法

基于卷积神经网络(convolutional neural network, CNN)的表面肌电信号(surface electromygraphy, sEMG)手势识别算法通常将一维sEMG转换成二维肌电图作为CNN的输入。针对sEMG瞬时样本量偏少、以及一维sEMG转换成二维肌电图时带来的局部时序特征丢失等问题,提出了将多元经验模态分解(multivariate empirical mode decomposition, MEMD)算法与Hilbert空间填充曲线相结合的方法,以提升手势识别算法的准确率。采用开源数据集NinaPro-DB1作为实验数据集；通过MEMD算法对sEMG进行分解；将分解后的本征模态函数(intrinsic mode functions, IMFs)作为Hilbert曲线的填充域(Hilb-IMFs)映射成二维肌电图；选择DenseNet作为手势识别的基本网络。实验结果表明,提出的方法相对于传统信号升维方法在手势识别准确率上约有4%的性能提升,验证了该方法的有效性。

sEMG processing method fusing multivariate empirical mode decomposition and Hilbert space-filling curves

Surface electromyography (sEMG) gesture recognition algorithms based on convolutional neural network (CNN) usually convert one-dimensional sEMG to two-dimensional electromyogram (EMG) as the input of CNN.In order to solve the problems such as the lack of instantaneous samples of sEMG and the loss of local timing features caused by converting one-dimensional sEMG to two-dimensional EMG images,a processing method which fuses the multivariate empirical mode decomposition (MEMD) algorithm and the Hilbert space-filling curve is proposed to improve the accuracy of the gesture recognition algorithm.The open-source dataset NinaPro-DB1 is applied.Firstly,the sEMG is decomposed by the MEMD algorithm.Secondly,the decomposed intrinsic mode functions (IMFs) are used as the filled domain (Hilb-IMFs) of the Hilbert curve for mapping them to a two-dimensional EMG image.Finally,DenseNet is chosen as the basic network for gesture recognition.The experimental results show that the proposed method has a performance improvement of about 4% in gesture recognition accuracy compared with traditional signal dimensionalization method,which verifies the effectiveness of the method.