Improved Multivariate Multi-scale Entropy Feature Algorithm for Mechanic Analysis of Static Balance of Human Body
Zhang Jianqiang
Luo Zhizeng
Zhang Qin
Abstract：Objective To analyze the mechanic signal of static balance of human body by using improved multivariate multi-scale entropy (MMSE).Methods When the dimension of MMSE increases,the multivariate delay vector needs to be extended.Improvement for the traditional method was proposed in this paper where all vectors were embedded at the same time instead of embedded one by one in the traditional algorithm.Results The experimental results of MMSE feature in different modes showed that the processing speed of improved algorithm was faster,the separation distance of entropy value in the different modes was bigger,the dispersed degree was lower,and the feature was easier to distinguish than that of the traditional algorithm.Conclusion The improved algorithm proposed in this paper could elevate the computational efficiency and make the distinguishability of the feature better,and can better analyze the static balance ability of the human body.
Keywords：static balancefeature extractionmultivariate multi-scale entropydispersed degree
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 321-326 )