Possible biomechanical origins of the long-range correlations in stride intervals of walking
Possible biomechanical origins of the long-range correlations in stride intervals of walking
复制标题
DOI：
10.1016/j.physa.2007.02.061
复制
发表时间：
2007-07-01
影响因子：
3.3
通讯作者：
Dingwell, Jonathan B.
中科院分区：
文献类型：
--
作者：
Gates, Deanna H.;Su, Jimmy L.;Dingwell, Jonathan B.
当人类行走时，每一步的持续时间都有所不同。这些时间波动表现出长期的相关性。有人认为，这些相关性源于大脑中控制步态周期计时的高级神经系统中心。现有提出的这一现象的模型主要集中在可能引起这些长程相关性的神经生理学机制上，而通常忽略了潜在的替代机械解释。我们假设，一个简单的机械系统也可以在大步前进时产生类似的长程关联。我们修改了一个非常简单的两足行走的被动动力学模型，通过在每次推开时施加在后腿上的脉冲力来考虑向前推进。通过结合由简单的比例反馈控制器调节的“感觉”和“运动”噪声项，推出力一步一步地变化。我们生成了400个步行模拟，使用不同的感觉噪声、运动噪声和反馈增益的组合。使用去趋势波动分析来分析来自每个模拟的步幅时间数据，以计算缩放指数g。该指数量化了每个步幅间隔如何与不同时间尺度上的先前和后续步幅间隔相关。对于噪声项和反馈增益的不同变化，我们得到了短程相关(α<；0.5)、不相关时间序列(α=0.5)、长程相关(0.5~1.0)。我们的结果表明，一个简单的行走生物力学模型可以产生远程关联，因此，这些关联可能不是先前提出的更高水平神经元控制的复杂结果。(C)2007 Elsevier B.V.保留所有权利。
When humans walk, the time duration of each stride varies from one stride to the next. These temporal fluctuations exhibit long-range correlations. It has been suggested that these correlations stem from higher nervous system centers in the brain that control gait cycle timing. Existing proposed models of this phenomenon have focused on neurophysiological mechanisms that might give rise to these long-range correlations, and generally ignored potential alternative mechanical explanations. We hypothesized that a simple mechanical system could also generate similar long-range correlations in stride times. We modified a very simple passive dynamic model of bipedal walking to incorporate forward propulsion through an impulsive force applied to the trailing leg at each push-off. Push-off forces were varied from step to step by incorporating both "sensory" and "motor" noise terms that were regulated by a simple proportional feedback controller. We generated 400 simulations of walking, with different combinations of sensory noise, motor noise, and feedback gain. The stride time data from each simulation were analyzed using detrended fluctuation analysis to compute a scaling exponent, g. This exponent quantified how each stride interval was correlated with previous and subsequent stride intervals over different time scales. For different variations of the noise terms and feedback gain, we obtained short-range correlations (alpha < 0.5), uncorrelated time series (alpha=0.5), long-range correlations (0.5 1.0). Our results indicate that a simple biomechanical model of walking can generate long-range correlations and thus perhaps these correlations are not a complex result of higher level neuronal control, as has been previously suggested. (c) 2007 Elsevier B.V. All rights reserved.