Wanzhong Chen

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Background: While the classification of multifunctional finger and wrist movement based on surface electromyography (sEMG) signals in intact subjects can reach remarkable recognition rates, the performance obtained from amputated subjects remained low. Methods: In this paper, we proposed and evaluated the myoelectric control scheme of upper-limb prostheses(More)
In this paper, in order to solve the existing problems of the low recognition rate and poor real-time performance in limb motor imagery, the integrated back-propagation neural network (IBPNN) was applied to the pattern recognition research of motor imagery EEG signals (imagining left-hand movement, imagining right-hand movement and imagining no movement).(More)
To improve the accuracy of acoustic emission (AE) source location, the wavelet packet transform is proposed to process the AE signals in this paper. The AE waves change shape due to the dispersion characteristics when they propagate in thin plates. Therefore, with the conventional method for AE source location by calculating arrival time differences based(More)
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