A MMSE feature extraction method based on MEMD was proposed to analyze multi-modal signals and evaluate the static balance ability of human body. First

the human multi-mode signal was collected. It was adaptively decomposed by multi-empirical mode from which a series of (IMFs) were obtained. The best IMF components were selected according to the T-test and correlation coefficients which was used for signal reconstruction. The multivariate multi-scale entropy algorithm was used to extract the features. Finally

K-means and support vector machine were used to compare with this paper’s methods about dealing with human body static balance problem

which was used to evaluate the optimal feature extraction method. Results shows that MMSE based on MEMD and support vector machine are optimal for feature extraction and classification in this paper.