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Chin. Phys. B, 2010, Vol. 19(5): 058701    DOI: 10.1088/1674-1056/19/5/058701
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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
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.
Keywords:  ensemble empirical mode decomposition      gait series      peak detection      intrinsic mode functions  
Received:  18 August 2009      Revised:  08 September 2009      Accepted manuscript online: 
PACS:  87.85.Ng (Biological signal processing) (Movement)  
  87.19.L- (Neuroscience)  
  02.30.Uu (Integral transforms)  
Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 60501003 and 60701002).

Cite this article: 

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 2010 Chin. Phys. B 19 058701

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