Search

In this paper, through the study of the characteristic rule of rolling massage experts' forearm surface EMG signal (sEMG) in rolling massage, the rolling massage analysis and evaluation methods based on the analysis of sEMG is presented. Therefore, during the implementation of rolling massage, muscle semg signals of the massage's three block muscle was collected at the real time, the relations between the electromyographic signal changeable characteristics and rolling method massage strength was discussed, meanwhile massager's muscle force situation was monitored, the ratio of muscle force was analyzed to assess and correct the massage action for the person whose action is not standard.

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.