Swarm-wavelet based extreme learning machine for finger movement classification on transradial amputees

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.

A robust myoelectric pattern recognition using online sequential extreme learning machine for finger movement classification

A robust myoelectric pattern-recognition-system requires a system that should work in the real application as good as in the laboratory. However, this demand should be handled properly and rigorously to achieve a robust myoelectric system. Electrode shift is an issue that usually emerges when dealing with robustness issue. In daily life, the placement of electrodes becomes a significant issue that can downgrade the performance of the system. This paper proposed a new way to overcome the robustness issue by conducting an update to the system to anticipate changes in the future such as electrode shift, improvement in muscle strength or any other issue. Such update will be used to generate an adaptation. The adaptation is done according to the user's need by employing an online sequential extreme learning (OS-ELM) to learn the training data chunk by chunk. OS-ELM enables the myoelectric system to learn from a small number of data to avoid cumbersome training process. The day-to-day experiment shows that the proposed system can maintain its performance on average accuracy around 85% whereas the non-adaptive system could not.