Autors: Zeghbib, A., Palis, F., Tsenov, G. T., Shoylev, N., Mladenov, V. M.
Title: Fuzzy systems and neural networks methods to identify hand and finger movements using surface EMG signals
Keywords: fuzzy systems; neural networks; EMG signals

Abstract: With help of exploitation of myoelectric signals, amputee persons can have a chance to improve their life with myoelectric prosthesis which are able to function with the amputee's muscle movements. The myoelectric signal (MES) is the electrical manifestation of muscular contraction. This signal recorded at the surface of the skin of the forearm has been exploited to provide the recognition of the movement of the hand and finger Movements of healthy subject. The objective of the paper is first to describe the identification procedure, based on EMG patterns of forearm activity using Fuzzy logic and Neural Networks methods. Second to show the advantage of using features in Time-frequency domain, in comparison to those in time domain. Suitable features in time-frequency domain give high classification rates. Third is to compare between different intelligent computational methods of identification, which are used in this work: Multi-Layer Perceptron (MLP), Radial Basis Function Networks (R

References

    Issue

    Proc. of the 9th Int. Conference on Systems, pp. 1-6, 2005, Greece, ISBN ISBN:960-8457-29-7

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Google Scholar