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- W2352679949 abstract "In order to recognize the movement patterns of electromyography(EMG) signals effectively,the acquired EMG signals were disposed first by the method of wavelet analysis to eliminate the noise to the maximum extent. Then the maximum of wavelet coefficients under each scale was taken as the input of Pi-Sigma neural network to recognize the movement patterns of EMG signals. At the same time,based on the energy characteristic of EMG signals,the points of motion start and stop were obtained to control the movement of powered prosthetic hands with the results of pattern recognition.The experimental results indicate that the method of the second wavelet de-noising can eliminate the noise of EMG effectively.Under the condition of correct movement pattern recognition,the real-time control of powered prosthetic hands can be realized expediently based on the time information of pattern recognition." @default.
- W2352679949 created "2016-06-24" @default.
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- W2352679949 date "2006-01-01" @default.
- W2352679949 modified "2023-09-24" @default.
- W2352679949 title "Electromyography Movement Pattern Recognition Based on the Wavelet Eigenvalues and Pi-sigma Network" @default.
- W2352679949 hasPublicationYear "2006" @default.
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