电子学报 ›› 2020, Vol. 48 ›› Issue (4): 670-674.DOI: 10.3969/j.issn.0372-2112.2020.04.008

• 学术论文 • 上一篇    下一篇

基于多元经验模态分解的多元多尺度熵静态平衡能力评估

石鹏, 张启忠, 张华平, 席旭刚   

  1. 杭州电子科技大学自动化学院, 浙江杭州 310018
  • 收稿日期:2019-01-08 修回日期:2019-05-24 出版日期:2020-04-25 发布日期:2020-04-25
  • 作者简介:石鹏 男.1996年2月出生,安徽亳州人.现为杭州电子科技大学研究生,研究方向为生物医学信号处理、模式识别.E-mail:sp0399@foxmail.com;张启忠 男.1967年2月出生,浙江金华人.现为杭州电子科技大学副教授,主要研究方向为生物医学信息检测、模式识别、机器人技术.
  • 基金资助:
    浙江省公益技术研究计划(No.LGF18F010006);浙江省自然科学基金(No.LY17F030021);浙江省教育厅科研项目(No.Y201840742)

Static Balance Capability Assessment of Multivariate Multiscale Entropy Based on Multivariate Empirical Mode Decomposition

SHI Peng, ZHANG Qi-zhong, ZHANG Hua-ping, XI Xu-gang   

  1. School of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China
  • Received:2019-01-08 Revised:2019-05-24 Online:2020-04-25 Published:2020-04-25

摘要: 提出了一种基于多元经验模态分解(Multivariate Empirical Mode Decomposition,MEMD)的多元多尺度熵(Multivarite Multiscale Entropy,MMSE)特征提取方法分析多模态信号,进行人体静态平衡能力评估.首先,采集人体多模态信号,采用多元经验模态分解对多通道信号进行自适应分解,得到一系列多元固有模态函数(Intrinsic Mode Functions,IMFs),依据T检验和相关系数从中选取最佳的IMF分量进行信号重构;然后,采用多元多尺度熵算法提取特征,用K-均值与支持向量机对比本文特征提取方法与两种传统特征提取方法在处理人体静态平衡能力评估问题时分类效果,并分析两种分类器的人体静态平衡能力评估效果;最后,得出本文最优的特征为基于多元经验模态分解的多元多尺度熵特征,最优的分类方法为支持向量机.

关键词: 静态平衡能力评估, 多模态信号, 多元经验模态分解, 多元多尺度熵

Abstract: 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.

Key words: assessment of static equilibrium, multimodal signal, MEMD, MMSE

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