EMG-based hand gesture recognition for realtime biosignal interfacing
Proceedings of the 13th international conference on Intelligent user interfaces
GMAI '08 Proceedings of the 2008 3rd International Conference on Geometric Modeling and Imaging
Telehealth/AT '08 Proceedings of the IASTED International Conference on Telehealth/Assistive Technologies
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
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The sticking point in studying multifreedom myoelectric prostheses is based on multimotion pattern recognition of surface electromyography, therefore, in this paper, a method that takes those singular eigenvalues of wavelet coefficients as the eigenvector of improved Least Squares Support Vector Machine (LS-SVM) is presented to discriminate the motion. pattern. Considering the nonsteady character of electromyography signal, wavelet transform is employed toanalyse electromyography on the basis of acquired signals that have been pre-processed earlier, consequently singular value decomposition of a wavelet coefficient matrix is adopted to extract features of surface electromyography and the Least Squares Support Vector Machine algorithm is utilized to implement the multi-motion pattern recognition of surface electromyography. Then a fuzzy controller is designed specially to control the adjustment of myoelectric prosthetic hand's movement, which can make the system of myoelectric prosthetic hand grasp object stably. Experimental results indicate that above method has a fast running speed, high discrimination rate and good robust, it can increase the correct ratio of movement pattern recognition and decrease the possible damage to grasped objects, so it has a great potential in the area of bionic man-machine systems such as using electromyography signal to control powered prosthesis.