EMG pattern recognition using Support Vector Machines classifier for myoelectric control purposes

The present work reports the use of Support Vector Machines (SVMs) as classifier of myoelectric signals. This tool was recently used to analyze data and recognize patterns, but just a few studies report its use in myoelectric registers. The aim of this research is analyze and compare some classification schemes employing Artificial Neural Networks and Linear Discriminant Analysis in order to establish the benefits of SVMs models in pattern recognition tasks. The departure information consists in an Electromyographic (EMG) data base of 12 subjects considering 4 degrees of freedom. Before building interpretation models, a pre-processing stage was done to obtain either autoregressive or frequency domain features.