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滤波会影响最大Lyapunov指数的计算。

Filtering affects the calculation of the largest Lyapunov exponent.

作者信息

Raffalt Peter C, Senderling Benjamin, Stergiou Nick

机构信息

Institute of Physical Performance, Norwegian School of Sport Sciences, Sognsveien 220, 0806, Oslo, Norway; Department of Biomechanics and Center for Research in Human Movement Variability, University of Nebraska at Omaha, 6160 University Drive, Omaha, NE, 68182-0860, USA.

Department of Biomechanics and Center for Research in Human Movement Variability, University of Nebraska at Omaha, 6160 University Drive, Omaha, NE, 68182-0860, USA.

出版信息

Comput Biol Med. 2020 Jul;122:103786. doi: 10.1016/j.compbiomed.2020.103786. Epub 2020 Apr 30.

Abstract

The calculation of the largest Lyapunov exponent (LyE) requires the reconstruction of the time series in an N-dimensional state space. For this, the time delay (Tau) and embedding dimension (EmD) are estimated using the Average Mutual Information and False Nearest Neighbor algorithms. However, the estimation of these variables (LyE, Tau, EmD) could be compromised by prior filtering of the time series evaluated. Therefore, we investigated the effect of filtering kinematic marker data on the calculation of Tau, EmD and LyE using several different computational codes. Kinematic marker data were recorded from 37 subjects during treadmill walking and filtered using a low pass digital filter with a range of cut-off frequencies (23.5-2Hz). Subsequently, the Tau, EmD and LyE were calculated from all cut-off frequencies. Our results demonstrated that the level of filtering affected the outcome of the Tau, EmD and LyE calculations for all computational codes used. However, there was a more consistent outcome for cut-off frequencies above 10 Hz which corresponded to the optimal cut-off frequency that could be used with this data. This suggested that kinematic data should remain unfiltered or filtered conservatively before calculating Tau, EmD and LyE.

摘要

最大Lyapunov指数(LyE)的计算需要在N维状态空间中重建时间序列。为此,使用平均互信息和伪最近邻算法估计时间延迟(Tau)和嵌入维度(EmD)。然而,这些变量(LyE、Tau、EmD)的估计可能会受到所评估时间序列的预先滤波的影响。因此,我们使用几种不同的计算代码研究了对运动学标记数据进行滤波对Tau、EmD和LyE计算的影响。在跑步机行走过程中记录了37名受试者的运动学标记数据,并使用具有一系列截止频率(23.5 - 2Hz)的低通数字滤波器进行滤波。随后,计算所有截止频率下的Tau、EmD和LyE。我们的结果表明,滤波水平影响了所使用的所有计算代码的Tau、EmD和LyE计算结果。然而,对于高于10Hz的截止频率,结果更为一致,该频率对应于可用于此数据的最佳截止频率。这表明在计算Tau、EmD和LyE之前,运动学数据应保持未滤波状态或进行保守滤波。

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