Using Singular Eigenvalues of Wavelet coefficient as the Input of SVM to Recognize Motion Patterns of the Hand

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 the singular eigenvalues of wavelet coefficients as the eigenvector of support vector machine is presented to discriminate the motion pattern. Considering the non-steady character of electromyography signal, wavelet transform is employed to analyze electromyography on the basis of acquired signals that have been preprocessed earlier, consequently singular value decomposition of a wavelet coefficient matrix is adopted to extract features of surface electromyography and the directed acyclic graph support vector machine algorithm is utilized to implement the multi-motion pattern recognition of surface electromyography. Experimental results indicate that above method has a fast running speed, high discrimination rate and good robust, so it has a great potential in the area of bionic man-machine systems such as using electromyography signal to control powered prosthesis