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Humans use multi-objective control to regulate lateral foot placement when walking

机译：人类在行走时使用多目标控制来调节脚的外侧位置

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A fundamental question in human motor neuroscience is to determine how the nervous system generates goal-directed movements despite inherent physiological noise and redundancy. Walking exhibits considerable variability and equifinality of task solutions. Existing models of bipedal walking do not yet achieve both continuous dynamic balance control and the equifinality of foot placement humans exhibit. Appropriate computational models are critical to disambiguate the numerous possibilities of how to regulate stepping movements to achieve different walking goals. Here, we extend a theoretical and computational Goal Equivalent Manifold (GEM) framework to generate predictive models, each posing a different experimentally testable hypothesis. These models regulate stepping movements to achieve any of three hypothesized goals, either alone or in combination: maintain lateral position, maintain lateral speed or “heading”, and/or maintain step width. We compared model predictions against human experimental data. Uni-objective control models demonstrated clear redundancy between stepping variables, but could not replicate human stepping dynamics. Most multi-objective control models that balanced maintaining two of the three hypothesized goals also failed to replicate human stepping dynamics. However, multi-objective models that strongly prioritized regulating step width over lateral position did successfully replicate all of the relevant step-to-step dynamics observed in humans. Independent analyses confirmed this control was consistent with linear error correction and replicated step-to-step dynamics of individual foot placements. Thus, the regulation of lateral stepping movements is inherently multi-objective and balances task-specific trade-offs between competing task goals. To determine how people walk in their environment requires understanding both walking biomechanics and how the nervous system regulates movements from step-to-step. Analogous to mechanical “templates” of locomotor biomechanics, our models serve as “control templates” for how humans regulate stepping movements from each step to the next. These control templates are symbiotic with well-established mechanical templates, providing complimentary insights into walking regulation.
机译：人类运动神经科学中的一个基本问题是确定神经系统如何产生固有目标的运动，尽管存在固有的生理噪声和冗余。步行表现出很大的可变性和任务解决方案的均等性。现有的双足步行模型尚未实现连续的动态平衡控制和人类所展示的脚放置的均等性。适当的计算模型对于消除如何调节踩踏运动以实现不同步行目标的多种可能性至关重要。在这里，我们扩展了理论和计算上的目标当量歧管（GEM）框架，以生成预测模型，每个模型都构成了不同的可实验检验的假设。这些模型调节踏步运动以单独或组合实现三个假设的目标：保持横向位置，保持横向速度或“航向”和/或保持步幅。我们将模型预测与人类实验数据进行了比较。单目标控制模型显示了步进变量之间的明显冗余，但无法复制人的步进动力学。平衡维持三个假设目标中的两个目标的大多数多目标控制模型也无法复制人类的踩踏动态。但是，在目标位置上高度优先地调节步幅宽度的多目标模型确实成功地复制了人类观察到的所有相关的逐步动力学。独立分析证实，该控制与线性误差校正和单个脚的放置的逐步动态一致。因此，横向踏步运动的调节本质上是多目标的，并且在竞争任务目标之间平衡了特定于任务的权衡。要确定人们在环境中的行走方式，既需要了解步行生物力学，也要了解神经系统如何逐步调节其动作。类似于运动生物力学的机械“模板”，我们的模型用作“控制模板”，用于说明人类如何调节从每一步到下一步的踩踏运动。这些控制模板与完善的机械模板是共生的，可提供有关行走调节的免费见解。

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