IDENTIFICATION OF HAND MOVEMENTS BASED ON MMG AND EMG SIGNALS

Abstract

This paper proposes a methodology that analysis and classifies the EMG and MMG signals using neural networks to control prosthetic members. Finger motions discrimination is the key problem in this study. Thus the emphasis is put on myoelectric signal processing approaches in this paper. The EMG and MMG signals classification system was established using the LVQ neural network. The experimental results show a promising performance in classification of motions based on both EMG and MMG patterns.