  The multichannel electrode array used for electromyogram (EMG) pattern
recognition provides good performance, but it has a high cost, is
computationally expensive, and is inconvenient to wear. Therefore, researchers
try to use as few channels as possible while maintaining improved pattern
recognition performance. However, minimizing the number of channels affects the
performance due to the least separable margin among the movements possessing
weak signal strengths. To meet these challenges, two time-domain features based
on nonlinear scaling, the log of the mean absolute value (LMAV) and the
nonlinear scaled value (NSV), are proposed. In this study, we validate the
proposed features on two datasets, existing four feature extraction methods,
variable window size and various signal to noise ratios (SNR). In addition, we
also propose a feature extraction method where the LMAV and NSV are grouped
with the existing 11 time-domain features. The proposed feature extraction
method enhances accuracy, sensitivity, specificity, precision, and F1 score by
1.00%, 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 experimental
results strongly suggest the proposed feature extraction method, for taking a
step forward with regard to improved myoelectric pattern recognition
performance.

Signal Processing