On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Feature extraction of forearm EMG signals for prosthetics
Expert Systems with Applications: An International Journal
Identification of EMG signals using discriminant analysis and SVM classifier
Expert Systems with Applications: An International Journal
Feature reduction and selection for EMG signal classification
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
A fundamental component of many modern prostheses is the myoelectric control system, which uses the electromyogram (EMG) signals from an individual's muscles to control the prosthesis movements. Despite the extensive research focus on the myoelectric control of arm and gross hand movements, more dexterous individual and combined fingers control has not received the same attention. The main contribution of this paper is an investigation into accurately discriminating between individual and combined fingers movements using surface EMG signals, so that different finger postures of a prosthetic hand can be controlled in response. For this purpose, two EMG electrodes located on the human forearm are utilized to collect the EMG data from eight participants. Various feature sets are extracted and projected in a manner that ensures maximum separation between the finger movements and then fed to two different classifiers. The second contribution is the use of a Bayesian data fusion postprocessing approach to maximize the probability of correct classification of the EMG data belonging to different movements. Practical results and statistical significance tests prove the feasibility of the proposed approach with an average classification accuracy of ~90% across different subjects proving the significance of the proposed fusion scheme in finger movement classification.