Forearm Orientation and Muscle Force Invariant Feature Selection Method for Myoelectric Pattern Recognition

• Published in: IEEE access 2022, Vol.10, p.46442-46471
• Main Authors: Islam, Md. Johirul, Ahmad, Shamim, Haque, Fahmida, Reaz, Mamun Bin Ibne, Bhuiyan, Mohammad A. S., Islam, Md. Rezaul
• Format: Article
• Language: English
• Subjects: Classifiers; Complexity; Correlation coefficients; Electromyography; EMG pattern recognition; Feature extraction; feature selection; Force; Forearm; forearm orientation; Invariants; K-nearest neighbors algorithm; muscle force variation; Muscles; Myoelectricity; Orientation; Pattern recognition; Prostheses; Prosthetics; Training; Wrist
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3170483

Electromyogram (EMG) signal-based prosthetic hand can restore an amputee's missing functionalities, which requires a faithful electromyogram pattern recognition (EMG-PR) system. However, forearm orientation and muscle force variation make the EMG-PR system more complex, and the problem becomes more complicated when muscle force levels and forearm orientations arise simultaneously. The problems can be minimized using a more significant number of features or high-density surface EMG, but it increases design complexity and needs higher computational power. In this regard, we have proposed a feature selection method that selects both feature and channel simultaneously. The proposed feature selection method selects only 7 to 20 features among 162 features with comparable or better performance. In this study, these selected features achieve a significant improvement in the accuracy, sensitivity, specificity, precision, F1 score, and Matthew correlation coefficient (MCC) by 3.18% to 4.28%, 9.14% to 12.85%, 1.83% to 2.57%, 8.30% to 10.99%, 9.22% to 13.92%, and 0.11 to 0.15, respectively comparing with four existing feature selection methods. In this research, the proposed feature selection method achieves a forearm orientation and muscle force invariant F1 score of 91.46% for training the k-nearest neighbor (KNN) classifier with two orientations, wrist fully supinated (O1) and wrist fully pronated (O3), with a medium force level. We have also achieved an F1 score of 93.27% for training the KNN classifier with all orientations with a medium force level. So, the proposed feature selection method would be very much helpful for finding the least dimensional features and achieving improved EMG-PR performance with multiple limiting factors.