%0 Journal Article
%T A method for extracting human gait series from accelerometer signals based on the ensemble empirical mode decomposition<br>
%A Fu Mao-Jing
%A Zhuang Jian-Jun
%A HouFeng-Zhen
%A Zhan Qing-Bo
%A Shao Yi
%A Ning Xin-Bao
%A <br>
%J 中国物理 B
%D 2010
%I 
%X 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.
%K ensemble empirical mode decomposition
%K gait series
%K peak detection
%K intrinsic mode functions<br>
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=6E709DC38FA1D09A4B578DD0906875B5B44D4D294832BB8E&cid=47EA7CFDDEBB28E0&jid=CD8D6A6897B9334F09D8D1648C376FB4&aid=4F47A3CDA154A97456CC2DE0B97FDC53&yid=140ECF96957D60B2&vid=2A8D03AD8076A2E3&iid=94C357A881DFC066&journal_id=1009-1963&journal_name=中国物理&referenced_num=0&reference_num=17