Convolutional neural network human gesture recognition algorithm based on phase portrait of surface electromyography energy kernel

表面肌电信号（sEMG）是一种不平稳非周期的微弱信号，基于时域和频域特征提取的 sEMG 信号分类方法识别率低、稳定性差。本文通过对 sEMG 信号能量核特征的建模分析，提出一种利用卷积神经网络（CNN）对 sEMG 信号能量核相图进行分类的新架构，来对人体手势动作进行识别。首先，利用矩阵计数方法将 sEMG 信号能量核相图处理为灰度图像；其次，利用移动平均对灰度图进行预处理；最后，采用 CNN 对手势 sEMG 信号进行识别。利用手势 sEMG 信号数据集进行了实验验证，结果表明选用 CNN 识别框架的有效性以及 CNN 结合能量核相图的识别方法相比于能量核面积提取方法，在识别精度和计算效率上具有明显的优势。本文算法为 sEMG 信号的建模分析与实时识别问题提供了新的可行方法。.; Surface electromyography (sEMG) is a weak signal which is non-stationary and non-periodic. The sEMG classification methods based on time domain and frequency domain features have low recognition rate and poor stability. Based on the modeling and analysis of sEMG energy kernel, this paper proposes a new method to recognize human gestures utilizing convolutional neural network (CNN) and phase portrait of sEMG energy kernel. Firstly, the matrix counting method is used to process the sEMG energy kernel phase portrait into a grayscale image. Secondly, the grayscale image is preprocessed by moving average method. Finally, CNN is used to recognize sEMG of gestures. Experiments on gesture sEMG signal data set show that the effectiveness of the recognition framework and the recognition method of CNN combined with the energy kernel phase portrait have obvious advantages in recognition accuracy and computational efficiency over the area extraction methods. The algorithm in this paper provides a new feasible method for sEMG signal modeling analysis and real-time identification.