Postural control model interpretation of stabilogram diffusion analysis
Postural control model interpretation of stabilogram diffusion analysis
复制标题
DOI：
10.1007/s004220050587
复制
发表时间：
2000-04-01
影响因子：
1.9
通讯作者：
Peterka, RJ
中科院分区：
文献类型：
--
作者：
Peterka, RJ
柯林斯和德卢卡[柯林斯JJ，德卢卡CJ（1993）实验脑研究95. 308-318]介绍了一种称为稳定图扩散分析的新方法，该方法提供了对在人类安静直立站立期间记录的压力中心（COP）轨迹的明显随机变化的定量统计测量。该分析生成稳定图扩散函数（SDF），其将均方COP位移总结为COP比较之间的时间间隔的函数。SDF具有特征性的两部分形式，这表明存在两种不同的控制机制：短期开环控制行为和长期闭环行为。本文证明了一个非常简单的闭环控制模型的直立立场，可以产生现实的SDF。该模型由倒立摆体组成，踝关节处施加扭矩。该转矩包括随机干扰转矩和控制转矩。控制转矩是期望直立身体位置和实际身体位置之间的偏差（误差信号）的函数，并且与误差信号、误差信号的导数和误差信号的积分成比例地产生[即比例、积分和导数（PID）神经控制器]。控制扭矩以表示传导、处理和肌肉激活延迟的时间延迟施加。PID参数和时间延迟的变化产生模拟真实的实验SDF的SDF中的变化。该模型分析允许根据神经控制器和时间延迟参数的变化而不是根据开环与闭环行为来解释实验观察到的SDF中的变化。
Collins and De Luca [Collins JJ, De Luca CJ (1993) Exp Brain Res 95. 308-318] introduced a new method known as stabilogram diffusion analysis that provides a quantitative statistical measure of the apparently random variations of center-of-pressure (COP) trajectories recorded during quiet upright stance in humans. This analysis generates a stabilogram diffusion function (SDF) that summarizes the mean square COP displacement as a function of the time interval between COP comparisons. SDFs have a characteristic two-part form that suggests the presence of two different control regimes: a short-term open-loop control behavior and a longer-term closed-loop behavior. This paper demonstrates that a very simple closed-loop control model of upright stance can generate realistic SDFs. The model consists of an inverted pendulum body with torque applied at the ankle joint. This torque includes a random disturbance torque and a control torque. The control torque is a function of the deviation terror signal) between the desired upright body position and the actual body position, and is generated in proportion to the error signal, the derivative of the error signal, and the integral of the error signal [i.e. a proportional, integral and derivative (PID) neural controller]. The control torque is applied with a time delay representing conduction, processing, and muscle activation delays. Variations in the PID parameters and the time delay generate variations in SDFs that mimic real experimental SDFs, This model analysis allows one to interpret experimentally observed changes in SDFs in terms of variations in neural controller and time delay parameters rather than in terms of open-loop versus closed-loop behavior.