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Study of Myoelectric Prostheses Based on Improved LS-SVM and Fuzzy Control

The sticking point in studying multi-freedom myoelectric prostheses is based on multi-motion pattern recognition of surface electromyography, therefore, in this paper, a method that takes those singular eigen values of wavelet coefficients as the eigenvector of improved Least Squares Support Vector Machine (LS-SVM) is presented to discriminate the motion pattern. Considering the non-steady character of electromyography signal, wavelet transform is employed to analyse 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.