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Extracting features from EMG signal and using pattern recognition method to predict the user's intended movement are the core parts of myoelcrtic prostheses control strategy. To extract more information about muscle contraction strategy. To extract more information about muscle contraction state and body movement from EMG signals by selecting some efficient feature parameters is one of the most important researches in Rehabilitation field. Since EMG signal is a weak signal and affected easily by various interference factors in real time applications, studying the effect of these factors on the performance in classifying intended movements can provide guidelines for future research.

System identification modeling is an essential precondition and foundation for the analysis, design and intelligent control of control system. Theory and method of identification modeling for rehabilitation training system for stroke patients' lower limbs based on support vector regression are mainly researched in this paper. In addition, simulated test is carried out to verify such method, based on which it can be said that such method is superior to other conventional methods in the aspect of accuracy and relevance.

Learning algorithms of the support vector machine is to map the input vector to a high dimensional space through certain kernel function and separate the image of the original linear input vector with the maximum of interval under consideration. This paper is about the limb motion recognition problem of stroke patients, mapping the input vector to the reproducing kernel RKHS (reproducing Kernel Hilbert space) space and using the methods in linear space to solve nonlinear problems. Meanwhile, feature transformation is achieved by defining the inner product of samples in the feature space after its characteristics are changed. Experimental results show that the support vector machine which is made up of new Kernel function can greatly improve the recognition rate of action under the conditions of Mercer, providing theoretical basis for modeling of lower limb rehabilitation training system of stroke patients.