人类步态动力学的多尺度熵分析
Multiscale entropy analysis of human gait dynamics.
作者信息
Costa M, Peng C-K, Goldberger Ary L, Hausdorff Jeffrey M
机构信息
Cardiovascular Division, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA.
Institute of Biophysics and Biomedical Engineering, Faculty of Sciences, University of Lisbon, Campo Grande 1749-016 Lisbon, Portugal.
出版信息
Physica A. 2003 Dec 1;330(1-2):53-60. doi: 10.1016/j.physa.2003.08.022. Epub 2003 Sep 21.
Abstract
We compare the complexity of human gait time series from healthy subjects under different conditions. Using the recently developed multiscale entropy algorithm, which provides a way to measure complexity over a range of scales, we observe that normal spontaneous walking has the highest complexity when compared to slow and fast walking and also to walking paced by a metronome. These findings have implications for modeling locomotor control and for quantifying gait dynamics in physiologic and pathologic states.
摘要
我们比较了不同条件下健康受试者的人类步态时间序列的复杂性。使用最近开发的多尺度熵算法，该算法提供了一种在一系列尺度上测量复杂性的方法，我们观察到，与慢走、快走以及跟着节拍器行走相比，正常的自然行走具有最高的复杂性。这些发现对运动控制建模以及量化生理和病理状态下的步态动力学具有重要意义。
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