Time-series based features for EMG pattern recognition: Preliminary results

Abstract

A summary of results for features obtained from upper limb electromyographic (EMG) signals is given. The features are based on the autoregressive (AR) model and include model coefficients, reflection coefficients, and cepstral coefficients. Some of these coefficients demonstrate potential for pattern recognition of upper limb movements for a EMG controlled prosthesis.<>