基于力度无关鲁棒特征的肌电手势识别方法  

EMG gesture recognition method based on robust feature independent of strength

作　　者：林恒 李玉榕[1,2] 施正义 朱菲菲 Lin Heng;Li Yurong;Shi Zhengyi;Zhu Feifei(College of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350108,China;Fujian Key Laboratory of Medical Instrument and Pharmaceutical Technology,Fuzhou 350108,China)

机构地区：[1]福州大学电气工程与自动化学院,福州350108 [2]福建省医疗器械和医药技术重点实验室,福州350108

出　　处：《仪器仪表学报》2022年第5期183-190,共8页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金(61773124);福建省自然科学基金(2019J01544)项目资助。

摘　　要：为了减小肌肉收缩力变化对肌电信号模式识别的影响,提出了DCSP特征。该特征首先通过CSP算法得到最大化类与类之间距离的空间投影矩阵,然后对投影后的新信号进行差分和归一化处理,最终通过非相关线性判别分析将数据投影到类内距离最小、类间距离最大的低维空间而得到。在两个数据集上验证基于DCSP特征的肌电手势识别正确率,第1个数据集包含10名完整肢体受试者的数据,第2个数据集包含9名上肢截肢者的数据。在识别率测试的4个方案中,DCSP特征的识别正确率均高于CSP特征,在全部力训练,全部力测试的方案上取得最高的识别率(数据集1:95.83%,数据集2:86.93%),相比CSP特征(数据集1:89.01%,数据集2:70.03%),分类准确率分别提升6%和16%。在特征空间分布的2个测试方案上,DCSP特征比CSP特征都具有更小的类内距离和更大的类间距离。相比较于其他研究的识别正确率,DCSP特征比现有的力度鲁棒特征提升了约5%(数据集1)和8%(数据集2),并且性能不依赖于分类器类型。In order to reduce the influence of the change of muscle contraction force on EMG pattern recognition,this paper proposes the feature of DCSP.Firstly,the spatial projection matrix that maximizes inter-class distance is obtained by the CSP.Then,the new signal after projection is differentiated and normalized.Finally,the data are projected into the low-dimensional space with the smallest intra-class distance and the largest inter-class distance by the uncorrelated linear discriminant analysis,the EMG gesture based on DCSP feature is verified on two datasets.The first dataset contains data from 10 complete limb subjects,and the second dataset contains data from 9 upper limb amputees.Among the four schemes of recognition rate testing,the recognition accuracy of the DCSP feature in this paper is higher than that of the CSP feature,and the highest recognition rate is achieved in all force training and all force testing schemes(dataset1:95.83%,dataset2:86.93%).Compared with CSP feature(dataset1:89.01%,dataset2:70.03%),the classification accuracy rates are increased by 6% and 16%,respectively.In the two test schemes of feature spatial distribution,the DCSP feature has a smaller intra-class distance and a larger inter-class distance than the CSP feature.In the comparison results of other studies,the DCSP feature improves the recognition accuracy by about 5%(dataset1) and 8%(dataset2) compared with the existing robust features,and the performance does not depend on the classifier.

关 键 词：肌肉收缩力 共空间模式 手势识别 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TH777[自动化与计算机技术—计算机科学与技术]