Constructing predictive models of human running
Constructing predictive models of human running
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DOI：
10.1098/rsif.2014.0899
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发表时间：
2015-02-06
影响因子：
3.9
通讯作者：
Seyfarth, Andre
中科院分区：
文献类型：
--
作者：
Maus, Horst-Moritz;Revzen, Shai;Seyfarth, Andre
跑步是人类运动的基本模式，在此期间，弹道空中阶段与单脚接触地面时的阶段交替。弹簧加载倒立摆（SLIP）为跑步建模提供了一个起点，并产生类似于人类跑步者质心（CoM）的地面反作用力。在这里，我们表明，虽然SLIP再现内步运动学的CoMin三维，它无法重现稳定性和预测未来的运动。我们使用数据驱动的Floquet分析构建SLIP控制模型，并展示了如何使用这些模型来获得人类跑步的预测模型，其中包括摆动腿脚踝的位置和速度的六个附加状态。我们的方法是通用的，可以应用于任何节奏的物理系统。我们提供了一种方法，用于确定一个事件驱动的线性控制器，近似所观察到的稳定策略，并产生一个减少的状态模型，密切恢复所观察到的动态。
Running is an essential mode of human locomotion, during which ballistic aerial phases alternate with phases when a single foot contacts the ground. The spring-loaded inverted pendulum (SLIP) provides a starting point for modelling running, and generates ground reaction forces that resemble those of the centre of mass (CoM) of a human runner. Here, we show that while SLIP reproduces within-step kinematics of the CoMin three dimensions, it fails to reproduce stability and predict future motions. We construct SLIP control models using data-driven Floquet analysis, and show how these models may be used to obtain predictive models of human running with six additional states comprising the position and velocity of the swing-leg ankle. Our methods are general, and may be applied to any rhythmic physical system. We provide an approach for identifying an event-driven linear controller that approximates an observed stabilization strategy, and for producing a reduced-state model which closely recovers the observed dynamics.