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.

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居算法估计时间延迟（Tau）和嵌入维数（EmD）。但是，这些变量（LyE，Tau，EmD）的估计可能会因事先对评估的时间序列进行过滤而受到影响。因此，我们研究了使用几种不同的计算代码来过滤运动学标记数据对Tau，EmD和LyE的计算的影响。在跑步机行走过程中记录了来自37位受试者的运动学标记数据，并使用具有一定截止频率范围（23.5–2Hz）的低通数字滤波器对其进行了滤波。随后，从所有截止频率计算出Tau，EmD和LyE。我们的结果表明，对于使用的所有计算代码，过滤级别都会影响Tau，EmD和LyE计算的结果。但是，对于高于10 Hz的截止频率，有一个更一致的结果，该频率对应于可以与该数据一起使用的最佳截止频率。这表明运动数据在计算Tau，EmD和LyE之前应保持未过滤或保守过滤。