Application of a self-enhancing classification method to electromyography pattern recognition for multifunctional prosthesis control

Abstract

The experimental results show that the self-enhancing classifiers significantly outperform the original versions using both AR and FC coefficient feature sets. The performance of SEQDA is superior to SELDA. In addition, preliminary study on long-term EMG data is conducted to verify the performance of SEQDA.