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 is 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 r...

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Bibliographic Details
Main Authors: Tsenov, G., Zeghbib, A.H., Palis, F., Shoylev, N., Mladenov, V.
Format: Conference Proceeding
Language:English
Subjects:
Computational intelligence
Electrodes
Electromyography
EMG signals
Feature extraction
Fingers
Hand and Finger Movements Identification
Muscles
Neural networks
Signal analysis
Signal processing
Testing
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Description
Summary:Myoelectric signals (MES) are the electrical manifestation of muscular contractions and they can be used to create myoelectric prosthesis which is 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 paper is to describe the identification procedure, based 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
DOI:10.1109/NEUREL.2006.341203