Neural network based identification of hand movements using biomedical signals

This paper proposes a methodology that analysis and classifies the EMG and MMG signals using a linear neural network 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 a linear neural network and it is presented the comparison with the classification based on the LVQ neural network. Experimental results show a promising performance in classification of motions based on both MMG and EMG signals.