Adjacent Features for High-Density EMG Pattern Recognition.

In the infancy of electromyography (EMG) based pattern recognition (PR) limited numbers of electrode channels were anatomically placed over muscles of interest. Modern methods have shown that regularly spaced electrodes around the circumference of a limb are equally effective and have been demonstrated in consumer-ready myoelectric control systems such as Thalmic Labs' Myo armband. In addition to linear arrays, grid arrays have also been applied in this field of research. Although electrode arrays have mainly been adopted to simplify placement, other benefits will be exploited in this work.Presented in this paper is a novel spatial-temporal feature set that separately analyzes the intensity and structure of the measured electrical signals (MES) and evaluates the similarities between adjacent electrodes, hence the name Adjacent Features (AF). Results in this paper show that AF produced classification accuracies about 4%-6% greater than autoregression (AR) coefficients and Hudgins' time-domain (TD) features for classifying 47 hand and wrist gestures, while having a computational simplicity similar to the TD features.