Variability is commonly observed in complex behavior, such as the maintenance of upright posture. The current study examines the value added by using nonlinear measures of variability to identify dynamic stability instead of linear measures that reflect average fluctuations about a mean state. The largest Lyapunov exponent (λ1) and SD were calculated on mediolateral movement as participants performed a sit-to-stand task on a stable and unstable platform. Both measures identified changes in movement across postures, but results diverged when participants stood on the unstable platform. Large SD indicated an increase in movement variability, but small λ1 identified those movements as stable and controlled. The results suggest that a combination of linear and nonlinear analyses is useful in identifying the proportion of observed variability that may be attributed to structured, controlled sources. Nonlinear measures of variability, like λ1, can further be used to make predictions about transitions between stable postures and to identify a system's resistance to disruption from external perturbations. Those features make nonlinear analyses highly applicable to both human movement research and clinical practice.

中文翻译：

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区分站立任务中的两种类型的可变性。

通常会在复杂行为（例如保持直立姿势）中观察到变异性。当前的研究通过使用非线性的可变性度量来确定动态稳定性，而不是使用线性度量来反映关于平均状态的平均波动，来检验增加的值。当参与者在稳定和不稳定的平台上执行从站到站的任务时，最大的Lyapunov指数（λ1）和SD是根据中外侧运动计算的。两种方法都可以识别出跨姿势运动的变化，但是当参与者站在不稳定的平台上时，结果却有分歧。SD越大表明运动变异性增加，但小λ1表明这些运动稳定且受控。结果表明，线性分析和非线性分析的组合可用于识别观察到的可归因于结构化受控源的变异性比例。像λ1这样的可变性的非线性度量还可以用于做出有关稳定姿势之间的过渡的预测，并确定系统对外部干扰干扰的抵抗力。这些功能使非线性分析非常适用于人体运动研究和临床实践。