Differentiating types of muscle movements using a wavelet based fuzzy clustering neural network
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Date
2009
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Abstract
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.
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Keywords
Electromyography , Fourier transforms , Fuzzy clustering , Fuzzy neural networks , Fuzzy sets , Fuzzy systems , Muscle , Myoelectrically controlled prosthetics , Pascal (programming language) , Programming theory , Prosthetics , Wavelet transforms , Action potentials , Arm movements , Arm prosthesis , Elbow extensions , Electromyographic signals , Muscle contractions , Muscle movements , Myo-electric signals , Network programming languages , Neural network classifiers , Transform models , Wavelet parameters , Wavelet transform , Neural networks