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 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.     

基于小波变换的人体步态序列提取

将小波变换用于处理人体行走时产生的加速度信号.利用离散小波变换的多尺度、多分辨率特性对原始加速度信号进行尺度分解,在对小波基以及分解尺度进行合理选取后准确地从加速度信号中提取出隐藏的步态节律.与利用阈值法直接对原始加速度信号提取峰值的算法比较后发现：利用小波分解得到与步态节律相关的特征尺度后再进行峰值检测能显著地提高信号峰值的检出率;即使当原始信号存在较严重的噪声干扰时,该方法也能保证所提取出的步态序列的准确性.这对于步态序列的后续分析具有至关重要的意义.研究表明,离散小波变换是一种有效的提取步态节律的方 关键词： 小波变换 步态序列 峰值检测 特征尺度