传感技术学报Issue(12)：1805-1811,7.DOI:10.3969/j.issn.1004-1699.2015.12.013
基于改进多元多尺度熵的人体步态加速度信号分类
Human Gait Acceleration Signal Classification Based on Improved Multiple Multiscale Entropy
摘要
Abstract
Traditional Multiple multiscale entropy algorithm at moment of dealing with time series of limited length , would led to curve fluctuations larger and threshold selection will also have a greater impact on the results. There⁃fore,on the basis of traditional Multiple multiscale entropy,firstly this paper improved the way of traditional coarse-grained process,the method improved coarse-grained way of traditional multiple multiscale sample entropy by slid⁃ing mean filter so that coarse-grained time series equal to the length of original time series on each scale,reduce the compute discreteness of multivariate multiscale entropy. In addition,algorithm both maintain the advantage of hard threshold of multiple multiscale sample entroy and count the distance of two composite delay vector slightly greater than the threshold value by defining fuzzy membership function,not only reducing the dependence of the threshold of multiple multiscale sample entropy,but also solving the instability caused by the traditional threshold. Finally, the algorithm was validated in the emulated data,and applied it to different human gait acceleration signal complexi⁃ty evaluation and classification.The results show that improved multiple multiscale entropy recognition is better than traditional multivariate multiscale entropy.关键词
步态分类/加速度信号/改进多元多尺度熵/传统多元多尺度熵Key words
gait classification/acceleration signal/Improved multiple multiscale entropy/Traditional multiple mul-tiscale entropy分类
信息技术与安全科学引用本文复制引用
王旭尧,徐永红..基于改进多元多尺度熵的人体步态加速度信号分类[J].传感技术学报,2015,(12):1805-1811,7.基金项目
国家自然科学基金项目 （）
