Re-interpreting detrended fluctuation analyses of stride-to-stride variability in human walking.
Re-interpreting detrended fluctuation analyses of stride-to-stride variability in human walking.
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DOI：
10.1016/j.gaitpost.2010.06.004
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发表时间：
2010-07
期刊：
影响因子：
2.4
通讯作者：
Cusumano, Joseph P.
中科院分区：
文献类型：
--
作者：
Dingwell, Jonathan B.;Cusumano, Joseph P.
去趋势波动分析（DFA）已被广泛用于量化人类步行中的步幅时间相关性。然而，重要的问题仍然是如何正确地解释这些统计特性的生理。在这里，我们提出了一个更简单，更简约的解释比以前建议。17名年轻健康的成年人在电动跑步机上以5种速度行走。记录连续步幅（SL）和步幅时间（ST）的时间序列。步速时间序列的计算公式为SS = SL/ST，SL和ST具有较强的统计持续性（α ≥ 0.5）。然而，SS始终表现出轻微的反持久性（α < 0.5）动力学。我们创建了三个替代数据集，以直接测试可能产生这些时间序列的可能控制过程的特定假设。受试者没有选择连续的SL和ST根据独立不相关或统计独立的自回归移动平均（阿尔马）过程。然而，保留原始SL和ST时间序列的自相关和互相关特性的互相关替代项成功复制了所有相关步态变量的平均值、标准差和（在计算限值内）DFA α指数。这些结果表明，受试者根据二维阿尔马过程控制他们的运动，该过程专门寻求最小化步行速度（SS）的步幅变化。这种解释与实验结果完全一致，也与统计持久性和反持久性的基本定义一致。我们的研究结果强调了在所涉及的控制过程和固有的生物力学和神经运动冗余的背景下解释DFA α指数的必要性。
Detrended fluctuation analyses (DFA) have been widely used to quantify stride-to-stride temporal correlations in human walking. However, significant questions remain about how to properly interpret these statistical properties physiologically. Here, we propose a simpler and more parsimonious interpretation than previously suggested. Seventeen young healthy adults walked on a motorized treadmill at each of 5 speeds. Time series of consecutive stride lengths (SL) and stride times (ST) were recorded. Time series of stride speeds were computed as SS = SL/ST. SL and ST exhibited strong statistical persistence (α ≫ 0.5). However, SS consistently exhibited slightly anti-persistent (α < 0.5) dynamics. We created three surrogate data sets to directly test specific hypotheses about possible control processes that might have generated these time series. Subjects did not choose consecutive SL and ST according to either independently uncorrelated or statistically independent auto-regressive moving-average (ARMA) processes. However, cross-correlated surrogates, which preserved both the auto-correlation and cross-correlation properties of the original SL and ST time series successfully replicated the means, standard deviations, and (within computational limits) DFA α exponents of all relevant gait variables. These results suggested that subjects controlled their movements according to a two-dimensional ARMA process that specifically sought to minimize stride-to-stride variations in walking speed (SS). This interpretation fully agrees with experimental findings and also with the basic definitions of statistical persistence and anti-persistence. Our findings emphasize the necessity of interpreting DFA α exponents within the context of the control processes involved and the inherent biomechanical and neuro-motor redundancies available.
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影响因子：
2.4
作者：
Hausdorff, JM;Peng, CK
通讯作者：
Peng, CK
影响因子：
3.3
作者：
Hausdorff, JM;Purdon, PL;Goldberger, AL
通讯作者：
Goldberger, AL
影响因子：
1.9
作者：
Peterka, RJ
通讯作者：
Peterka, RJ
影响因子：
2.4
作者：
Dingwell, JB;Marin, LC
通讯作者：
Marin, LC
DOI：
10.1016/j.physa.2007.02.061
发表时间：
2007-07-01
影响因子：
3.3
作者：
Gates, Deanna H.;Su, Jimmy L.;Dingwell, Jonathan B.
通讯作者：
Dingwell, Jonathan B.