Browsing by Subject "Electromyographic signals"

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    Using LBG algorithm for extracting the features of EMG signals; [EMG i̇şaretlerinin özniteliklerinin belirlenmesinde LBG algoritmasinin kullanimi]
    (2008) Koçyiǧit Y.; Kiliç I.
    The Electromyographic (EMG) signals observed at the surface of the skin is the sum of many small action potentials generated in the muscle fibers. There is only a pattern for each EMG signals, which are generated by biceps and triceps muscles. There are different types of signal processing in order to find out the feature values for true classification in this pattern. In this study, the Feature values belong to 4 different arm movements are obtained by using clustering methods, i.e K-means, Fuzzy C-means, and LBG after applying Wavelet Transform to EMG signals . Then these feature values are compared each other by KEYK and Quadratic Discriminant Analysis classifier. ©2008 IEEE.
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    Differentiating types of muscle movements using a wavelet based fuzzy clustering neural network
    (2009) Karlk B.; Koçyìit Y.; Korürek M.
    The electromyographic signals observed at the surface of the skin are the sum of many small action potentials generated in the muscle fibres. After the signals are processed, they can be used as a control source of multifunction prostheses. The myoelectric signals are represented by wavelet transform model parameters. For this purpose, four different arm movements (elbow extension, elbow flexion, wrist supination and wrist pronation) are considered in studying muscle contraction. Wavelet parameters of myoelectric signals received from the muscles for these different movements were used as features to classify the electromyographic signals in a fuzzy clustering neural network classifier model. After 1000 iterations, the average recognition percentage of the test was found to be 97.67% with clustering into 10 features. The fuzzy clustering neural network programming language was developed using Pascal under Delphi. © 2009 Blackwell Publishing Ltd.