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- W90392016 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 (RBF) and Learning Vector Quantization network (LVQ) as supervised methods and fuzzy Subtractive Clustering (FSC) as unsupervised method." @default.
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- W90392016 date "2005-07-11" @default.
- W90392016 modified "2023-09-24" @default.
- W90392016 title "Fuzzy systems and neural networks methods to identify hand and finger movements using surface EMG signals" @default.
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