Neural networks for online classification of hand and finger movements using surface EMG signals

Myoelectric signals (MES) are the electrical manifestation of muscular contractions and they can be used to create myoelectric prosthesis which are able to function with the amputee's muscle movements. This signal recorded at the surface of the skin of the forearm has been exploited to provide recognition of the movement of the hand and finger Movements of healthy subject. The objective of the p a per is to describe the identification procedure, base(] on EMG patterns of forearm activity using various Neural Networks methods and to make a comparison between different intelligent computational methods of identification, which are used in this work, Then an online algorithm for movement identification and classification that utilises the trained Neural Networks is presented.