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This paper presents a novel approach to optimize pattern recognition system using genetic algorithm (GA) to identify the type of hand motion employing artificial neural networks (ANNs) with high performance and accuracy suited for practical implementations. To achieve this approach, electromyographic (EMG) signals were obtained from sixteen locations on the forearm of six subjects in ten hand motion...
Nowadays, it is common to identify some neuromuscular disorders from the myoelectric signals (MES). Often, these disorders are reflected in the basic components of the MES, the motor unit action potentials (MUAP). This work presents an approach for the decomposition of intramuscular MES in its essential MUAPs, through analysis (wavelet transform) and classification (neural networks) tools. Decomposition...
In this paper, surface electromyographic signal is analyzed by wavelet transform. The feature vectors are built by extracting the singular value of the wavelet coefficients. The multi-class support vector machine classifier is designed by using four kinds of multi-class classification approaches, and completed the eight class surface EMG pattern classification. The SVM classifier is applied to the...
For realizing seven hand gestures classification correctly, wavelet transform is used firstly to eliminate the noise in sEMG, because of its multi-resolution analysis characteristic. Then combine time domain features (such as EMG integral, variance, the third-order AR model coefficients) with frequency domain features (power-spectrum) as the inputs of neural network classifier to discriminate seven...
In this paper, we use one channel to collect the surface EMG signals of these actions separately such as elbow flexion, elbow extension, forearm supination and forearm pronation. Whereas the advantage of wavelet transform that it has fine frequency resolution at low frequencies, we can get a 4-dimension characteristic vector which is made up of 3 maximum values of detail coefficients (coefficients...
In this research, the artificial intelligent method based human motion pattern recognition for surface electromyographic (EMG) signal is proposed. As the EMG signal is a measurement of anatomical and physiological characteristic of the given muscle, the macroscopical movement patterns of the human body can be classified and recognized. By using the technology of wavelet packet transformation, the...
This paper makes use of the method of wavelet transform to analysis and extract wavelet coefficient as the eigenvalue from original two-channel surface electromyography signal (SEMG) in the Physics Labs. It is input to a radial base function (RBF) neural network as training sample to train. This network is used to pattern Qubi or Shenbi classification for the surface EMG of forearm. The experiment...
In this paper, a novel electromyographic (EMG) motion pattern classifier using wavelet packet transform (WPT) and Learning Vector Quantization (LVQ) Neural Networks is proposed. This motion pattern classifier can successfully identify wrist extension, wrist flexion, hand extension and hand grasp, by measuring the surface EMG signals through two electrodes mounted on forearm extensor carpi ulnaris...
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...
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