Do humans optimally exploit redundancy to control step variability in walking?
Do humans optimally exploit redundancy to control step variability in walking?
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DOI：
10.1371/journal.pcbi.1000856
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发表时间：
2010-07-15
影响因子：
4.3
通讯作者：
Cusumano JP
中科院分区：
文献类型：
--
作者：
Dingwell JB;John J;Cusumano JP
人们普遍认为，人和动物走路时能量消耗最小。虽然这些原则预测了一般的行为，但它们并不能解释在行走中观察到的可变性。为了强健的表现，步行动作必须适应每一步，而不仅仅是平均水平。在这里，我们提出了一个分析框架，调和最优性，冗余性和随机性的问题。对于人类在跑步机上行走，我们定义了一个目标函数，以形成一种可能的控制策略的精确数学定义：在每一步保持恒定的速度。我们记录了健康受试者以五种速度行走时的步幅和步幅长度。指定的目标函数将步幅到步幅的变化分解为与实现假设策略明确相关的新步态变量。受试者表现出与目标相关的、与实现该策略直接相关的步态波动的可变性大大降低，但与目标无关的波动的可变性要大得多。更重要的是，人类在每一步中都会立即纠正与目标相关的偏差，而允许与目标无关的偏差在多个步幅中持续存在。为了证明这不是人们可以用来成功完成任务的唯一策略，我们创建了三个代理数据集。每个实验都测试了一个特定的替代假设，即受试者使用不同的策略，而不参考假设的目标函数。人类没有采取任何可行的替代策略。最后，我们建立了一系列基于最小干预原则的步行步幅变异性随机控制模型。我们证明，健康的人类并不是精确的“最佳”，而是在每一步的行走速度上持续地略微过度纠正小的偏差。我们的研究结果揭示了一种新的控制原则，用于调节人类行走中步幅的波动，这种波动独立于最小化能量消耗，但与最小化能量消耗并行。因此，人类利用任务冗余来实现鲁棒控制，同时最小化努力和允许潜在的有益的运动变异性。现有用于解释运动如何被控制的原理预测了平均的、长期的行为。然而，神经肌肉噪音不断扰乱这些运动，对神经系统提出了重大挑战。一种可能性是，神经系统必须克服所有神经肌肉的变异性作为限制性能的约束。相反，我们发现在跑步机上行走的人利用冗余来调整每一步的步伐动作并保持表现。这种策略不是任务本身所需要的，而是由适当的随机控制模型预测的。因此，神经系统通过强烈调节与目标相关的波动来简化控制，而在很大程度上忽略了非必要的变化。正确地确定随机性如何影响控制对于建立生物模型至关重要，因为神经运动波动是这些系统固有的。我们的工作统一了时间序列分析研究者、运动协调研究者和运动控制理论家的观点，为研究目标定向背景下的可变性提供了一个单一的动态框架。
It is widely accepted that humans and animals minimize energetic cost while walking. While such principles predict average behavior, they do not explain the variability observed in walking. For robust performance, walking movements must adapt at each step, not just on average. Here, we propose an analytical framework that reconciles issues of optimality, redundancy, and stochasticity. For human treadmill walking, we defined a goal function to formulate a precise mathematical definition of one possible control strategy: maintain constant speed at each stride. We recorded stride times and stride lengths from healthy subjects walking at five speeds. The specified goal function yielded a decomposition of stride-to-stride variations into new gait variables explicitly related to achieving the hypothesized strategy. Subjects exhibited greatly decreased variability for goal-relevant gait fluctuations directly related to achieving this strategy, but far greater variability for goal-irrelevant fluctuations. More importantly, humans immediately corrected goal-relevant deviations at each successive stride, while allowing goal-irrelevant deviations to persist across multiple strides. To demonstrate that this was not the only strategy people could have used to successfully accomplish the task, we created three surrogate data sets. Each tested a specific alternative hypothesis that subjects used a different strategy that made no reference to the hypothesized goal function. Humans did not adopt any of these viable alternative strategies. Finally, we developed a sequence of stochastic control models of stride-to-stride variability for walking, based on the Minimum Intervention Principle. We demonstrate that healthy humans are not precisely “optimal,” but instead consistently slightly over-correct small deviations in walking speed at each stride. Our results reveal a new governing principle for regulating stride-to-stride fluctuations in human walking that acts independently of, but in parallel with, minimizing energetic cost. Thus, humans exploit task redundancies to achieve robust control while minimizing effort and allowing potentially beneficial motor variability. Existing principles used to explain how locomotion is controlled predict average, long-term behavior. However, neuromuscular noise continuously disrupts these movements, presenting a significant challenge for the nervous system. One possibility is that the nervous system must overcome all neuromuscular variability as a constraint limiting performance. Conversely, we show that humans walking on a treadmill exploit redundancy to adjust stepping movements at each stride and maintain performance. This strategy is not required by the task itself, but is predicted by appropriate stochastic control models. Thus, the nervous system simplifies control by strongly regulating goal-relevant fluctuations, while largely ignoring non-essential variations. Properly determining how stochasticity affects control is critical to developing biological models, since neuro-motor fluctuations are intrinsic to these systems. Our work unifies the perspectives of time series analysis researchers, motor coordination researchers, and motor control theorists by providing a single dynamical framework for studying variability in the context of goal-directedness.
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影响因子：
2.4
作者：
Dingwell, JB;Marin, LC
通讯作者：
Marin, LC
DOI：
10.1016/j.physa.2007.02.061
发表时间：
2007-07-01
影响因子：
3.3
作者：
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通讯作者：
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DOI：
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发表时间：
2003-12-01
影响因子：
3.3
作者：
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通讯作者：
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作者：
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影响因子：
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作者：
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通讯作者：
BAI, BY