Surface Electromyographic (SEMG) signal is characterized by physiological noise contained,in order to eliminate inclusion noise. In wavelet packet analysis domain,after analysising the traditional soft-threshold and hard-threshold de-noising method of characteriscs,a new method which is μ rhythm threshold method is provided to improve threshold function. In Matlab7.0,the relevant procedures and simulation show that the method can not only solve the hard-threshold de-noising continuous problem,but also solve the detects before and after the threshold treatment of the soft-threshold wavelet coefficients constant deviation. Meanwhile,the application of the method in the wavelet analysis shows that wavelet packet μ rhythm threshold method de-noising significantly better than wavelet μ rhythm threshold method,and proves that the wavelet packet has remarkable capacity to de-noising.

With help training of the lower limbs rehabilitation robot, the hemiplegia patients can be helped effectively recover. Applicable control method plays an important part in performance of lower limbs rehabilitation robot. According to the preferred method, sEMG was collected from no necrosis and healthy muscle, then, the effective action signals which are extracted from the sEMG transit to Fuzzy-Neural network classifiers to identify the movements intention of paralyzed patients, and then the lower limbs rehabilitation robots can assist paralyzed patients to achieve their intent. The simulation results indicate that the Fuzzy-Neural network classifiers can identify the movements intention well, and control method of sEMG can satisfy the demand of lower limbs rehabilitation robot.