Dingwell Jonathan B, Bohnsack-McLagan Nicole K, Cusumano Joseph P
Department of Kinesiology & Health Education, University of Texas, Austin, TX 78712, USA; Department of Kinesiology, Pennsylvania State University, University Park, PA 16802, USA. Electronic address: http://biomechanics.psu.edu/.
Department of Kinesiology & Health Education, University of Texas, Austin, TX 78712, USA.
J Biomech. 2018 Jul 25;76:144-151. doi: 10.1016/j.jbiomech.2018.05.034. Epub 2018 Jun 15.
As humans walk or run, external (environmental) and internal (physiological) disturbances induce variability. How humans regulate this variability from stride-to-stride can be critical to maintaining balance. One cannot infer what is "controlled" based on analyses of variability alone. Assessing control requires quantifying how deviations are corrected across consecutive movements. Here, we assessed walking and running, each at two speeds. We hypothesized differences in speed would drive changes in variability, while adopting different gaits would drive changes in how people regulated stepping. Ten healthy adults walked/ran on a treadmill under four conditions: walk or run at comfortable speed, and walk or run at their predicted walk-to-run transition speed. Time series of relevant stride parameters were analyzed to quantify variability and stride-to-stride error-correction dynamics within a Goal-Equivalent Manifold (GEM) framework. In all conditions, participants' stride-to-stride control respected a constant-speed GEM strategy. At each consecutively faster speed, variability tangent to the GEM increased (p ≤ 0.031), while variability perpendicular to the GEM decreased (p ≤ 0.044). There were no differences (p ≥ 0.999) between gaits at the transition speed. Differences in speed determined how stepping variability was structured, independent of gait, confirming our first hypothesis. For running versus walking, measures of GEM-relevant statistical persistence were significantly less (p ≤ 0.004), but showed minimal-to-no speed differences (0.069 ≤ p ≤ 0.718). When running, people corrected deviations both more quickly and more directly, each indicating tighter control. Thus, differences in gait determined how stride-to-stride fluctuations were regulated, independent of speed, confirming our second hypothesis.
当人类行走或奔跑时，外部（环境）和内部（生理）干扰会导致变异性。人类如何逐步步态调节这种变异性对于维持平衡可能至关重要。仅根据变异性分析无法推断出“受控”的内容。评估控制需要量化连续运动中偏差是如何校正的。在这里，我们评估了两种速度下的行走和奔跑。我们假设速度差异会导致变异性的变化，而采用不同的步态会导致人们调节步幅方式的变化。十名健康成年人在四种条件下在跑步机上行走/奔跑：以舒适速度行走或奔跑，以及以预测的步行到跑步转换速度行走或奔跑。分析相关步幅参数的时间序列，以在目标等效流形（GEM）框架内量化变异性和逐步步幅误差校正动态。在所有条件下，参与者的逐步步幅控制都遵循恒定速度的GEM策略。在每个连续更快的速度下，与GEM相切的变异性增加（p≤0.031），而与GEM垂直的变异性降低（p≤0.044）。在转换速度下，步态之间没有差异（p≥0.999）。速度差异决定了步幅变异性的结构方式，与步态无关，证实了我们的第一个假设。对于跑步与行走，与GEM相关的统计持续性测量值显著更低（p≤0.004），但速度差异极小至无差异（0.069≤p≤0.718）。跑步时，人们校正偏差的速度更快且更直接，每项都表明控制更严格。因此，步态差异决定了逐步步幅波动的调节方式，与速度无关，证实了我们的第二个假设。
