A Novel Signal Normalization Approach to Improve the Force Invariant Myoelectric Pattern Recognition of Transradial Amputees

Variation in the electromyogram pattern recognition (EMG-PR) performance with the muscle contraction force is a key limitation of the available prosthetic hand. To alleviate this problem, we propose a scheme to realize electromyogram signal normalization across channels before feature extraction. The proposed signal normalization scheme is validated over a dataset of nine transradial amputees that includes three force levels with six hand gestures. Moreover, we employ three classifiers, namely, linear discriminant analysis (LDA), support vector machine (SVM) and k-nearest neighbour (KNN), to evaluate the EMG-PR performance. In addition to the signal normalization scheme, we perform nonlinear transformation of the features by using the logarithm function. Both schemes facilitate merging of the muscle activation patterns of different force levels. The experimental results indicate that the force invariant EMG-PR performance (F1 score of at least 3.24% to 4.34%) of the proposed schemes is significantly enhanced compared to that obtained in recent studies. Therefore, we recommend using these features along with the proposed signal normalization scheme and nonlinear transformation of the features to improve the force invariant EMG-PR performance. The proposed feature extraction method achieves the highest F1 score of 91.28%, 91.39% and 90.56% when using the LDA, SVM and KNN classifiers, respectively.