肌电信号多通道相关性特征手势识别方法

doi:10.3778/j.issn.1002-8331.2111-0123

摘要/Abstract

摘要： 多通道表面肌电信号（surface electromyography，sEMG）传统手势识别方法，主要提取各个通道时域、频域和时频域特征作为分类器的输入，鲜有考虑通道间的相关性，在提升识别精度上遇到瓶颈。为了充分利用sEMG多通道信息以提高手势识别精度，提出一种以多通道相关性为特征的肌电手势识别方法。该方法计算多通道间一致性相关系数，作为多通道sEMG线性相关特征参数，同时获取多通道间的互信息，作为多通道sEMG非线性相关特征参数。实际运用中精确估计联合概率密度函数往往十分困难，根据互信息与copula熵关系，将互信息估计转化为copula熵的估计，通过经验分布函数进行概率积分变换，采用非参数估计方法估计copula熵，从而避免联合概率密度函数的估计。利用两种相关性特征参数构建多通道相关性特征进行对比实验，基于stacking模型使用多通道相关性特征与4种常用时域特征进行识别并对比结果，其次基于多通道相关性特征使用stacking模型与5种常用分类器进行对比识别，实验结果表明所提的多通道相关性特征能有效区分手势动作，在采集的健康受试者手势数据集上平均识别准确率达到94%。

关键词: 手势识别, 表面肌电信号, 一致性相关系数, 互信息, 多通道相关性

Abstract: The traditional multi-channel surface electromyography（sEMG） gesture recognition extracts the time domain, frequency domain and time frequency domain features of each channel as the input of the classifier. The correlation among channels is rarely considered, and the recognition accuracy is bottlenecked. In order to make full use of the multi-channel sEMG information to improve the accuracy of gesture recognition, a sEMG-based multi-channel correlation feature is proposed. The method calculates the concordance correlation coefficient among multiple channels as the linear correlation feature parameter, and simultaneously obtains the mutual information among the multiple channels as the nonlinear correlation feature parameter. In order to solve the problem of hard estimate of the joint probability density function in mutual information, the method converts mutual information estimate into the estimate of copula entropy, according to the inverse relationship between mutual information and copula entropy. The probability integral transformation is carried out through the empirical distribution function, and non-parametric estimation is adopted to estimate copula entropy, from which the estimate of mutual information is obtained. Then the method constructs the multi-channel correlation feature with the two feature parameters, and inputs the multi-channel correlation feature into the stacking model for recognition. Respectively compared with 4 common time domain features and 5 basic classifiers, the experimental results show that the proposed multi-channel correlation feature can effectively distinguish hand gesture with an average recognition accuracy rate of 94%, demonstrating the effectiveness of the method.

Key words: gesture recognition, surface electromyography（sEMG）, concordance correlation coefficient, mutual information, multi-channel correlation

江茜, 李沿宏, 邹可, 袁学东. 肌电信号多通道相关性特征手势识别方法[J]. 计算机工程与应用, 2023, 59(7): 102-109.

JIANG Xi, LI Yanhong, ZOU Ke, YUAN Xuedong. Myoelectric Gesture Recognition Based on Multi-Channel Correlation Feature[J]. Computer Engineering and Applications, 2023, 59(7): 102-109.

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