为了提高上肢手势动作的识别准确率,通过三阶巴特沃斯滤波器进行表面肌电信号(surface electromyography,sEMG)去噪和时间滑动窗口合理分割sEMG信号预处理.特征提取使用了积分肌电值、均方根值和小波包变换系数,并提出了一种时域信号结合时频域信号的特征空间方法,包括了积分肌电结合小波包变换系数(integrated electromyography-max wavelet packet coefficient-energy wavelet packet coefficient,IEME)和均方根值结合小波包变换系数(root mean square-max wavelet packet coefficient-energy wavelet packet coefficient,RMSME).在特征空间构建基础上,提出了三种手势识别方法:支持向量机分类器(support vector machine,SVM)、人工鱼群算法优化支持向量机分类器(artificial fish swarm algorithm-support vector machine,AFSA-SVM)和卷积神经网络(convolutional neural network,CNN).实验共采集了 10 位受试者的 8 种上肢手势动作sEMG信号,并引用Nina Pro DB2 公开数据集进行对比.实验结果表明,无论在实验采集数据和Nina Pro DB2 公开数据集中特征空间IEME相对于RMSME都更具识别度,并且特征空间IEME在1D-CNN上识别平均准确率和平均训练用时均优于2D-CNN.在实验采集数据中1D-CNN识别平均准确率高达98.61%,相对于SVM和AFSA-SVM识别准确率提高了 6.77%和 10.61%,并且采用1D-CNN识别方法的平均训练时间为7.37 s较SVM和AFSA-SVM减少了68.32 s和221.53 s,因此在手势sEMG信号识别分类中采用特征空间IEME和分类模型1D-CNN具有优势.
In order to improve the recognition accuracy of upper limb gesture actions,a third-order Butterworth filter was used to denoise surface electromyography(sEMG)signals and a time sliding window was used to reasonably segment sEMG signals.Integrated EMG value、root mean square value and wavelet packet transform coeffi-cient were used for feature extraction.A feature space method combining time-domain signal with time-frequency signal was proposed,including integrated EMG combined with wavelet packet trans-form co-efficient(IEME)and root mean square value combined with wavelet packet transform coefficient(RMSME).On the basis of feature space construction,three gesture recognition methods were proposed:support vector machine classifier(SVM),artificial fish swarm algorithm optimized support vector machine classifier(AFSA-SVM)and convolutional neural network(CNN).A total of 8 up-per limb gesture sEMG signals of 10 subjects were collected and compared with the Nina Pro DB2 public dataset.The experimental re-sults show that the feature space IEME is more recognizable than RMSME in both the experimental acquisition data and the Nina Pro DB2 public dataset,and the average recognition accuracy and average training time of the eature space IEME on 1D-CNN are better than that of 2D-CNN.In the experimental acquisition data,the average recognition accuracy of 1D-CNN is as high as 98.61%,which is 6.77%and 10.61%higher than that of SVM and AFSA-SVM,and the average training time of 1D-CNN recognition method is 7.37 s less than that of SVM and AFSA-SVM by 68.32 s and 221.53 s,therefore,the use of feature space IEME and classification model 1D-CNN in gesture sEMG signal recognition classification has advantages.