Department of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, Hat Yai, Songkhla, 90112, Thailand.
School of Electrical, Mechanical and Mechatronic Systems, Faculty of Engineering and Information Technology, University of Technology Sydney, 15 Broadway, Ultimo, NSW, 2007, Australia.
Med Biol Eng Comput. 2018 Dec;56(12):2259-2271. doi: 10.1007/s11517-018-1857-5. Epub 2018 Jun 18.
Electromyography (EMG) in a bio-driven system is used as a control signal, for driving a hand prosthesis or other wearable assistive devices. Processing to get informative drive signals involves three main modules: preprocessing, dimensionality reduction, and classification. This paper proposes a system for classifying a six-channel EMG signal from 14 finger movements. A feature vector of 66 elements was determined from the six-channel EMG signal for each finger movement. Subsequently, various feature extraction techniques and classifiers were tested and evaluated. We compared the performance of six feature extraction techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), uncorrelated linear discriminant analysis (ULDA), orthogonal fuzzy neighborhood discriminant analysis (OFNDA), spectral regression linear discriminant analysis (SRLDA), and spectral regression extreme learning machine (SRELM). In addition, we also evaluated the performance of seven classifiers consisting of support vector machine (SVM), linear classifier (LC), naive Bayes (NB), k-nearest neighbors (KNN), radial basis function extreme learning machine (RBF-ELM), adaptive wavelet extreme learning machine (AW-ELM), and neural network (NN). The results showed that the combination of SRELM as the feature extraction technique and NN as the classifier yielded the best classification accuracy of 99%, which was significantly higher than those from the other combinations tested. Graphical abstract Mean of classification accuracies for 14 finger movements obtained with various pairs of SRELM and classifier.
在生物驱动系统中，肌电图 (EMG) 被用作控制信号，用于驱动手部假肢或其他可穿戴辅助设备。为了获得有意义的驱动信号，处理过程涉及三个主要模块：预处理、降维和分类。本文提出了一种用于分类来自 14 个手指运动的六通道 EMG 信号的系统。对于每个手指运动，从六通道 EMG 信号中确定了一个 66 个元素的特征向量。随后，测试和评估了各种特征提取技术和分类器。我们比较了六种特征提取技术的性能，即主成分分析 (PCA)、线性判别分析 (LDA)、不相关线性判别分析 (ULDA)、正交模糊邻域判别分析 (OFNDA)、谱回归线性判别分析 (SRLDA) 和谱回归极限学习机 (SRELM)。此外，我们还评估了由支持向量机 (SVM)、线性分类器 (LC)、朴素贝叶斯 (NB)、k-最近邻 (KNN)、径向基函数极限学习机 (RBF-ELM)、自适应小波极限学习机 (AW-ELM) 和神经网络 (NN) 组成的七种分类器的性能。结果表明，SRELM 作为特征提取技术和 NN 作为分类器的组合产生了最高的分类准确率为 99%，明显高于其他测试组合。
