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In this study, we have investigated usefulness of extraction of the surface electromiyogram (sEMG) features from multi-level wavelet decomposition of the yEMG signal. The first step of this method is to analyze sEMG signal detected from the subject's right upper forearm and extract features using the mean absolute value (MAV), MAV of wavelet approximation and details coefficients, MAV of wavelet approximation...
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...
Surface Electromyography (SEMG) analysis of dynamic contraction is becoming more important to understand the muscle condition in real life activities. The objective of the study is to discover the relationship between the changes of amplitude and frequency at high and low frequency bands. Continuous Wavelet Transform (CWT) is utilized in order to process the SEMG data in both time and frequency domain...
To date various signal processing techniques have been applied to surface electromyography (SEMG) for feature extraction and signal classification. Compared with traditional analysis methods which have been used in previous application, continuous wavelet transform (CWT) enhances the SEMG features more effectively. This paper presents methods of analysing SEMG signals using CWT and LabVIEW for extracting...
To date various signal processing techniques have been applied to surface electromyography (SEMG) for feature extraction and signal classification. Compared with traditional analysis methods which have been used in previous application, continuous wavelet transform (CWT) enhances the SEMG features more effectively. This paper presents methods of analysing SEMG signals using CWT and LabVIEW for extracting...
The surface electromyographic (SEMG) signal, which is produced by neural and muscular systems, is a complicated bioelectric signal recorded from skin surface using electrodes. It is very helpful for doctors to analyse the illness of patients. In the paper, four channel SEMG signals from four muscles (palmaris longus, brachioradialis, flexor carpi ulnaris, biceps brachii) are analyzed with wavelet...
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