A method for extracting human gait series from accelerometer signals based on the ensemble empirical mode decomposition
A method for extracting human gait series from accelerometer signals based on the ensemble empirical mode decomposition
复制标题
一种基于集成经验模态分解的加速度信号中提取人体步态序列的方法
DOI：
10.1088/1674-1056/19/5/058701
复制
发表时间：
2010-05
影响因子：
1.7
通讯作者：
Fu, Mao-Jing
中科院分区：
文献类型：
--
作者：
Shoa, Yi;Zhuang, Jian-Jun;Zhan, Qing-Bo;Ning, Xin-Bao;Hou, Feng-Zhen;Fu, Mao-Jing
本文将集成经验模态分解（EEMD）应用于人体正常行走过程中采集的加速度计信号的分析。首先，EEMD的自适应功能被用来分解的加速度计信号，从而筛选出几个内在模式函数（IMF）在不同的尺度。然后，步态序列可以通过峰值检测从最能代表步态节律性的特征IMF中提取出来。与基于经验模态分解（EMD）的方法相比，基于EEMD的方法具有以下优点：即使在加速度计信号被间歇性噪声严重污染的情况下，也能显著提高隐藏在原始加速度计信号中的峰值的检测率;有效地防止了EMD过程中出现的模态混叠现象。合理选择滤波准则参数可以提高EEMD方法的计算速度。同时，利用自回归和滑动平均模型对短时间序列进行双向延拓，可以抑制端点效应。结果表明EEMD是提取步态节律性的有力工具，也为其他生理信号的特征节律提取提供了有价值的线索。
In this paper, the ensemble empirical mode decomposition (EEMD) is applied to analyse accelerometer signals collected during normal human walking. First, the self-adaptive feature of EEMD is utilised to decompose the accelerometer signals, thus sifting out several intrinsic mode functions (IMFs) at disparate scales. Then, gait series can be extracted through peak detection from the eigen IMF that best represents gait rhythmicity. Compared with the method based on the empirical mode decomposition (EMD), the EEMD-based method has the following advantages: it remarkably improves the detection rate of peak values hidden in the original accelerometer signal, even when the signal is severely contaminated by the intermittent noises; this method effectively prevents the phenomenon of mode mixing found in the process of EMD. And a reasonable selection of parameters for the stop-filtering criteria can improve the calculation speed of the EEMD-based method. Meanwhile, the endpoint effect can be suppressed by using the auto regressive and moving average model to extend a short-time series in dual directions. The results suggest that EEMD is a powerful tool for extraction of gait rhythmicity and it also provides valuable clues for extracting eigen rhythm of other physiological signals.
登录
查看更多内容
DOI：
10.1088/1009-1963/15/6/043
发表时间：
2006-06
期刊：
Chinese Physics
影响因子：
--
作者：
Ming-Wei Zou;Guo-Lin Feng;Xin-Quan Gao
通讯作者：
Ming-Wei Zou;Guo-Lin Feng;Xin-Quan Gao
影响因子：
3.4
作者：
Hausdorff, Jeffrey M.;Lowenthal, Justine;Giladi, Nir
通讯作者：
Giladi, Nir
DOI：
10.1016/s1007-4376(08)60012-x
发表时间：
2008
期刊：
Journal of Nanjing Medical University
影响因子：
--
作者：
Yan Zhang;Jing Yang;Wang‐ming Xu
通讯作者：
Yan Zhang;Jing Yang;Wang‐ming Xu
DOI：
10.1109/iembs.2004.1403234
发表时间：
2004-12
期刊：
The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
影响因子：
--
作者：
Hualou Liang;Qiuhua Lin;J.D.Z. Chen
通讯作者：
Hualou Liang;Qiuhua Lin;J.D.Z. Chen
影响因子：
3.4
作者：
Baltadjieva, Rossitza;Giladi, Nir;Hausdorff, Jeffrey M.
通讯作者：
Hausdorff, Jeffrey M.