Statistical precision and sensitivity of measures of dynamic gait stability
Statistical precision and sensitivity of measures of dynamic gait stability
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DOI：
10.1016/j.jneumeth.2008.12.015
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发表时间：
2009-04-15
影响因子：
3
通讯作者：
Beek, Peter J.
中科院分区：
文献类型：
--
作者：
Bruijn, Sjoerd M.;van Dieen, Jaap H.;Beek, Peter J.
最近两种用于量化系统动态稳定性的方法已经应用于人类运动：局部稳定性（通过有限时间最大李雅普诺夫指数，λ（S-stride）和λ（L-stride）量化）和轨道稳定性（量化为最大Floquet乘数，MaxFm）。然而，到目前为止，仍然不清楚需要多少数据点来获得行走期间这些测量的精确估计，以及这些估计对行走行为变化的敏感程度。我们收集了健康受试者（n = 9）在三种情况下在跑步机上行走的长数据系列（正常行走速度为0.83 m/s（3 km/h）和1.38 m/s（5 km/h），以及在执行Stroop双重任务时行走速度为1.38 m/s（5 km/h））。从0.83和1.38 m/s试验的数据系列提交到bootstrap程序和配对t检验的样本的不同的数据系列长度之间进行0.83和1.38 m/s和1.38 m/s之间有和没有Stroop task.Longer数据系列导致更精确的估计lambda（S-步幅），lambda（L-步幅）。MaxFm的。所有变量均显示出数据序列长度的影响。因此，在估计和比较各种条件下的这些变量时，应分析涵盖相同步幅数的数据系列。lambda（S-stride）、lambda（L-stride）和MaxFm对步行速度的变化敏感，而只有lambda（S-stride）和MaxFm足够敏感以捕获由Stroop任务引起的步行的调制。尽管如此，这些调制只能在使用大量步幅（>150）时才能检测到。(C)2008 Elsevier B. V.保留所有权利。
Recently. two methods for quantifying a system's dynamic stability have been applied to human locomotion: local stability (quantified by finite time maximum Lyapunov exponents, lambda(S-stride) and lambda(L-stride)) and orbital stability (quantified as maximum Floquet multipliers, MaxFm). Thus far, however, it has remained unclear how many data points are required to obtain precise estimates of these measures during walking, and to what extent these estimates are sensitive to changes in walking behaviour.To resolve these issues, we collected long data series of healthy subjects (n = 9) walking on a treadmill in three conditions (normal walking at 0.83 m/s (3 km/h) and 1.38 m/s (5 km/h), and walking at 1.38 m/s (5 km/h) while performing a Stroop dual task). Data series from 0.83 and 1.38 m/s trials were submitted to a bootstrap procedure and paired t-tests for samples of different data series lengths were performed between 0.83 and 1.38 m/s and between 1.38 m/s with and without Stroop task.Longer data series led to more precise estimates for lambda(S-stride), lambda(L-stride). and MaxFm. All variables showed an effect of data series length. Thus, when estimating and comparing these variables across conditions, data series covering an equal number of strides should be analysed. lambda(S-stride), lambda(L-stride), and MaxFm were sensitive to the change in walking speed while only lambda(S-stride), and MaxFm were sensitive enough to capture the modulations of walking induced by the Stroop task. Still, these modulations could only be detected when using a substantial number of strides (>150). (C) 2008 Elsevier B.V. All rights reserved.