West Bruce J, Latka Miroslaw
Mathematical and Informational Sciences Directorate US Army Research Office, Research Triangle Park, NC 27709, USA.
J Neuroeng Rehabil. 2005 Aug 2;2:24. doi: 10.1186/1743-0003-2-24.
The stride interval in healthy human gait fluctuates from step to step in a random manner and scaling of the interstride interval time series motivated previous investigators to conclude that this time series is fractal. Early studies suggested that gait is a monofractal process, but more recent work indicates the time series is weakly multifractal. Herein we present additional evidence for the weakly multifractal nature of gait. We use the stride interval time series obtained from ten healthy adults walking at a normal relaxed pace for approximately fifteen minutes each as our data set. A fractional Langevin equation is constructed to model the underlying motor control system in which the order of the fractional derivative is itself a stochastic quantity. Using this model we find the fractal dimension for each of the ten data sets to be in agreement with earlier analyses. However, with the present model we are able to draw additional conclusions regarding the nature of the control system guiding walking. The analysis presented herein suggests that the observed scaling in interstride interval data may not be due to long-term memory alone, but may, in fact, be due partly to the statistics.
健康人类步态中的步幅间隔在每一步之间以随机方式波动,步幅间隔时间序列的标度促使先前的研究者得出该时间序列是分形的结论。早期研究表明步态是一个单分形过程,但最近的工作表明该时间序列是弱多重分形的。在此我们提供步态弱多重分形性质的额外证据。我们使用从十名健康成年人以正常放松步伐行走约十五分钟所获得的步幅间隔时间序列作为我们的数据集。构建一个分数阶朗之万方程来对潜在的运动控制系统进行建模,其中分数阶导数的阶数本身是一个随机量。使用该模型,我们发现十个数据集中每个数据集的分形维数与早期分析结果一致。然而,使用当前模型我们能够得出关于引导行走的控制系统性质的其他结论。本文所呈现的分析表明,观察到的步幅间隔数据中的标度可能并非仅归因于长期记忆,实际上可能部分归因于统计数据。