改进S变换谱估计的动态手势肌电特征处理

Dynamic gesture sEMG feature processing based on estimation of the improved S-transform spectral

手语是各种手势动态变化的一种复杂运动模式,手势特征处理效果直接关系到手语识别的准确性。本文提出一种基于改进S变换谱估计的动态手势肌电特征处理新方法。对采集的表面肌电信号进行S变换,引入优化因子调节时频分辨率并生成改进S变换谱;定义谱的时间和频率分量为二维随机变量,以改进S变换谱元素为二维随机变量样本,通过高斯核密度估计得到二维核密度函数。仿真和实验均表明,改进S变换谱估计方法有效抑制了白噪声,并使动态手势的肌电暂态突变特征得到加强。与经验模态分解、自排序熵、奇异值排序熵等方法对比,基于该方法的动态手势识别率分别提高了10.0%、6.67%和11.67%,特征处理方法的效果明显。

The sign language is a complex motion pattern with dynamic changes in various gestures.The effect of gesture feature processing is directly related to the accuracy of sign language recognition.In this article,a new dynamic gesture feature processing method based on the estimation of improved S-transform(IST) spectral of surface electromyography(sEMG) is proposed.The collected sEMG signal is transformed by S-transform,the optimization factor is introduced to adjust the time-frequency resolution,and the IST spectrum is generated.The time and frequency components of the IST spectrum are defined as 2-D random variables,and the matrix elements of the IST spectrum are taken as 2-D random variables.The 2-D kernel density function is obtained by Gaussian kernel density estimation.Simulation and experiments show that the estimation method of the IST spectrum effectively suppresses white noise and strengthens the sEMG transient mutation characteristics of dynamic gestures.Compared with empirical mode decomposition,self-permutation entropy,and singular value permutation entropy,the accuracy of dynamic gesture recognition based on this method is improved by 10.0%,6.67% and 11.67%,respectively.