Mechanisms of Energy Optimization in Human Locomotion

人体运动能量优化机制

• 批准号: RGPIN-2020-04638
• 负责人: Donelan, Max
• 金额: $ 5.13万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=746226
• 关键词: Mechanisms; Energy; Optimization; Human; Locomotion

Why do we move the way we do? Why do we prefer to shift our gaze with a particular timing, reach to a cup using particular muscles, and walk with a particular step width? Perhaps the most general answer we have for these questions is that people prefer movements that are, in some sense, optimal. This optimization principle underlies theories on the control of reaching, grasping, gaze and gait. The principle is not just general, but also useful-it has provided insight into healthy and pathological behaviour, as well as the functions of different brain areas. Remarkably, how the nervous system performs this optimization is largely unknown. Our long-term vision is to use both experiments and theory to understand how the nervous system learns optimal movements, and then apply our findings to accelerate motor learning. We have three short-term objectives. Our first objective is to further understand the algorithms that the nervous system uses to minimize energetic cost. Here we seek to answer one of the important mysteries from our recent work-how the nervous system reliably initiates optimization. We will also determine how the nervous system optimizes control across many gait variables, including step frequency and step width, as well as the limits to this multi-dimensional optimization. Our second objective is to determine how the nervous system senses energetic cost. Here we will quantify the role of feedback from ergoreceptors, which directly sense by-products of muscle metabolism and are known to play a role in controlling ventilation and sensing fatigue. We will also determine the role of indirect energetic cost sensing, arising from muscle proprioceptor feedback. The third objective is to leverage our understanding of the nervous system's learning mechanisms to develop methods for accelerating energy optimization. Here we will focus on learning to gain assistance from powered exoskeletons as a model system. We will manipulate experience with new controllers, the complexity of the controller, and the frequency with which the controllers are changed. The fundamental impact of this work is due, in part, to uncovering the mechanisms that underlie the most general principle we have for human walking-that we prefer to walk in ways that minimize energetic cost. This new understanding has relevance not just to the particular objective of energy minimization and the particular task of walking, but also for how the nervous system optimizes for other objectives, such as accuracy and stability, in other tasks, such as reaching and eye movements. One application of this work is to powered exoskeletons and prostheses where the acceleration of motor learning can be immediately applied to increase the effectiveness of this technology at improving users' lives. Accelerating motor learning in novel conditions will also be useful for the design of running shoes, the customization of rehabilitation programs, and the optimization of athletic training.

我们为什么要这样移动？为什么我们更喜欢在特定的时间转移视线，使用特定的肌肉到达杯子，以特定的步幅行走？也许我们对这些问题的最普遍的答案是，人们更喜欢在某种意义上是最佳的运动。这种优化原理是控制伸手、抓握、凝视和步态的理论基础。这一原理不仅具有普遍性，而且很有用它为健康和病态行为以及不同大脑区域的功能提供了深入的见解。值得注意的是，神经系统如何执行这种优化在很大程度上是未知的。我们的长期愿景是使用实验和理论来了解神经系统如何学习最佳运动，然后将我们的发现应用于加速运动学习。 我们有三个短期目标。我们的第一个目标是进一步了解神经系统用于最小化能量成本的算法。在这里，我们试图回答我们最近工作中的一个重要谜团-神经系统如何可靠地启动优化。我们还将确定神经系统如何优化对许多步态变量的控制，包括步频和步宽，以及这种多维优化的限制。我们的第二个目标是确定神经系统如何感知能量消耗。在这里，我们将量化来自运动感受器的反馈的作用，运动感受器直接感知肌肉代谢的副产品，并且已知在控制通气和感知疲劳方面发挥作用。我们还将确定间接能量成本感知的作用，由肌肉本体感受器反馈引起。第三个目标是利用我们对神经系统学习机制的理解来开发加速能量优化的方法。在这里，我们将专注于学习从动力外骨骼作为模型系统获得帮助。我们将操纵新控制器的经验，控制器的复杂性，以及控制器改变的频率。这项工作的基本影响部分是由于揭示了人类行走的最普遍原则的基础机制，即我们更喜欢以最小能量消耗的方式行走。这种新的理解不仅与能量最小化的特定目标和行走的特定任务有关，而且还与神经系统如何优化其他目标有关，例如在其他任务中的准确性和稳定性，例如达到和眼球运动。这项工作的一个应用是动力外骨骼和假肢，其中运动学习的加速可以立即应用于提高这项技术在改善用户生活方面的有效性。在新的条件下加速运动学习也将有助于跑鞋的设计，康复计划的定制和运动训练的优化。