Evaluation of feature extraction techniques and classifiers for finger movement recognition using surface electromyography signal.
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.
['Phukpattaranont|Pornchai|P|http://orcid.org/0000-0003-0885-0176', 'Thongpanja|Sirinee|S|', 'Anam|Khairul|K|', 'Al-Jumaily|Adel|A|', 'Limsakul|Chusak|C|']
D000328:Adult D004576:Electromyography D005260:Female D005385:Fingers D006801:Humans D008297:Male D009068:Movement D016571:Neural Networks, Computer D010363:Pattern Recognition, Automated D012815:Signal Processing, Computer-Assisted D060388:Support Vector Machine D055815:Young Adult
Feature extraction Electromyography (EMG) Dimensionality reduction EMG pattern recognition Finger movement classification