A method for extracting human gait series from accelerometer signals based on the ensemble empirical mode decomposition

doi:10.1088/1674-1056/19/5/058701

中国物理B ›› 2010, Vol. 19 ›› Issue (5): 58701-058701.doi: 10.1088/1674-1056/19/5/058701

A method for extracting human gait series from accelerometer signals based on the ensemble empirical mode decomposition

符懋敬¹, 庄建军¹, 展庆波¹, 邵毅¹, 宁新宝¹, 侯凤贞²

(1)Key Laboratory of Modern Acoustics, Institute for Biomedical Electronic Engineering, Department of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China; (2)Key Laboratory of Modern Acoustics, Institute for Biomedical Electronic Engineering, Department of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China;Division of Basic Science, China Pharmaceutical University, Nanjing 210009, Ch

收稿日期:2009-08-18 修回日期:2009-09-08 出版日期:2010-05-15 发布日期:2010-05-15
基金资助:
Project supported by the National Natural Science Foundation of China (Grant Nos. 60501003 and 60701002).

A method for extracting human gait series from accelerometer signals based on the ensemble empirical mode decomposition

Fu Mao-Jing(符懋敬)^a), Zhuang Jian-Jun(庄建军)^a)^†, Hou Feng-Zhen(侯凤贞)^a)b), Zhan Qing-Bo(展庆波)^a),Shao Yi(邵毅)^a), and Ning Xin-Bao(宁新宝)^a)

^a Key Laboratory of Modern Acoustics, Institute for Biomedical Electronic Engineering, Department of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China; ^b Division of Basic Science, China Pharmaceutical University, Nanjing 210009, China

Received:2009-08-18 Revised:2009-09-08 Online:2010-05-15 Published:2010-05-15
Supported by:
Project supported by the National Natural Science Foundation of China (Grant Nos. 60501003 and 60701002).

摘要/Abstract

摘要： In this paper, the ensemble empirical mode decomposition ({EEMD}) is applied to analyse accelerometer signals collected during human normal 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 {\rm IMF} that best represents gait rhythmicity. Compared with the method based on the empirical mode decomposition ({EMD}), the {EEMD}-based method has 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.

Abstract: In this paper, the ensemble empirical mode decomposition (EEMD) is applied to analyse accelerometer signals collected during human normal 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 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.

Key words: ensemble empirical mode decomposition, gait series, peak detection, intrinsic mode functions

中图分类号: (Biological signal processing)

87.85.Ng

87.19.rs (Movement) 87.19.L- (Neuroscience) 02.30.Uu (Integral transforms)

符懋敬, 庄建军, 侯凤贞, 展庆波, 邵毅, 宁新宝. A method for extracting human gait series from accelerometer signals based on the ensemble empirical mode decomposition[J]. 中国物理B, 2010, 19(5): 58701-058701.

Fu Mao-Jing(符懋敬), Zhuang Jian-Jun(庄建军), Hou Feng-Zhen(侯凤贞), Zhan Qing-Bo(展庆波),Shao Yi(邵毅), and Ning Xin-Bao(宁新宝). A method for extracting human gait series from accelerometer signals based on the ensemble empirical mode decomposition[J]. Chin. Phys. B, 2010, 19(5): 58701-058701.

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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

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