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The multichannel electrode array used for electromyogram (EMG) patternrecognition provides good performance, but it has a high cost, iscomputationally expensive, and is inconvenient to wear. Therefore, researcherstry to use as few channels as possible while maintaining improved patternrecognition performance. However, minimizing the number of channels affects theperformance due to the least separable margin among the movements possessingweak signal strengths. To meet these challenges, two time-domain features basedon nonlinear scaling, the log of the mean absolute value (LMAV) and thenonlinear scaled value (NSV), are proposed. In this study, we validate theproposed features on two datasets, existing four feature extraction methods,variable window size and various signal to noise ratios (SNR). In addition, wealso propose a feature extraction method where the LMAV and NSV are groupedwith the existing 11 time-domain features. The proposed feature extractionmethod enhances accuracy, sensitivity, specificity, precision, and F1 score by1.00%, 5.01%, 0.55%, 4.71%, and 5.06% for dataset 1, and 1.18%, 5.90%, 0.66%,5.63%, and 6.04% for dataset 2, respectively. Therefore, the experimentalresults strongly suggest the proposed feature extraction method, for taking astep forward with regard to improved myoelectric pattern recognitionperformance.

Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features