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

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.