Hausdorff J M, Peng C K, Ladin Z, Wei J Y, Goldberger A L
Charles A. Dana Research Institute, Beth Israel Hospital, Boston, Massachusetts.
J Appl Physiol (1985). 1995 Jan;78(1):349-58. doi: 10.1152/jappl.1995.78.1.349.
Complex fluctuations of unknown origin appear in the normal gait pattern. These fluctuations might be described as being 1) uncorrelated white noise, 2) short-range correlations, or 3) long-range correlations with power-law scaling. To test these possibilities, the stride interval of 10 healthy young men was measured as they walked for 9 min at their usual rate. From these time series, we calculated scaling indexes by using a modified random walk analysis and power spectral analysis. Both indexes indicated the presence of long-range self-similar correlations extending over hundreds of steps; the stride interval at any time depended on the stride interval at remote previous times, and this dependence decayed in a scale-free (fractallike) power-law fashion. These scaling indexes were significantly different from those obtained after random shuffling of the original time series, indicating the importance of the sequential ordering of the stride interval. We demonstrate that conventional models of gait generation fail to reproduce the observed scaling behavior and introduce a new type of central pattern generator model that successfully accounts for the experimentally observed long-range correlations.
正常步态模式中会出现来源不明的复杂波动。这些波动可被描述为：1）不相关白噪声，2）短程相关性，或3）具有幂律标度的长程相关性。为了检验这些可能性，我们测量了10名健康年轻男性以平常速度行走9分钟时的步幅间隔。从这些时间序列中，我们通过使用修正的随机游走分析和功率谱分析计算了标度指数。两个指数均表明存在延伸数百步的长程自相似相关性；任何时刻的步幅间隔都取决于此前较远时刻的步幅间隔，且这种依赖性以无标度（类分形）幂律方式衰减。这些标度指数与原始时间序列随机重排后得到的指数显著不同，表明步幅间隔顺序排列的重要性。我们证明，传统的步态生成模型无法重现观察到的标度行为，并引入了一种新型的中枢模式发生器模型，该模型成功解释了实验观察到的长程相关性。
