Mechanisms underlying the energy optimization of human walking

人类步行能量优化的机制

• 批准号: RGPIN-2019-05677
• 负责人: Selinger, Jessica
• 金额: $ 2.72万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715735
• 关键词: Mechanisms; underlying; energy; optimization; human

For many people, walking is something done every day, without much thought and with relative ease. People can gracefully adapt their gait to changing terrains, task demands, and constraints on their body. Yet, how the nervous system navigate the expanse of all possible movements, to rapidly arrive at what appears to be an optimal coordination strategy, is largely unknown. We have recently determined that an important objective in the real-time selection of our gait is energy minimizationpeople tend to walk in ways that minimize their metabolic energy expenditure. Here, we are interested in understanding how this energy optimization is accomplished by the nervous system. To do so, we will combine human walking experiments with a computational reinforcement learning model, to systematically investigate the energy optimization process. Specifically, we will investigate how three factors can facilitate or disrupt one's ability to optimize their gait, including i) variability in gait (i.e. the stride to stride changes in walking coordination); ii) saliency of the energetic consequences of gait (i.e. the ease with which one can sense changes in energy expenditure); and iii) constancy of the energetic consequences of gait (i.e. how frequently the energetic consequences of walking are changing). In each case, we will perturb the walking person or environment to exaggerate one of these factors and then study how rapidly, or if at all, the walking person can converge on the energy optimal gait. Finally, we will build a system that leverages this understanding to augment a walking persons optimization processprescribing changes to gait that will allow them to more rapidly converge on the energy optimal solution. The proposed research will provide a basic and foundational understanding of how the human nervous system so elegantly performs energy optimization during walking. This has a number of broad implications for Canadian research and innovation, including: understanding gait adaptations that accompany ageing or injury; designing rehabilitation programs that consider the nervous systems internal objectives; designing assistive technologies that work in concert with the user to augment movement; and expediating locomotor skill learning. In addition, this research program will provide a rich training environment for cross-disciplinary undergraduate and graduate students, equally prepared to benefit the future of Canadian research and innovation through careers in either academia or industry.

对许多人来说，走路是每天都在做的事情，没有太多的思考，相对轻松。人们可以优雅地调整自己的步态，以适应不断变化的地形、任务要求和身体上的限制。然而，神经系统如何在所有可能的运动中导航，快速达到似乎是最佳的协调策略，在很大程度上是未知的。 我们最近已经确定，在我们的步态实时选择的一个重要目标是能量最小化人们往往走的方式，最大限度地减少他们的代谢能量消耗。在这里，我们有兴趣了解这种能量优化是如何由神经系统完成的。为此，我们将结合联合收割机人类步行实验与计算强化学习模型，系统地研究能量优化过程。具体来说，我们将研究三个因素如何促进或破坏一个人优化步态的能力，包括i）步态的可变性（即步行协调中的步幅变化）; ii）步态的能量后果的显著性（即，人们可以感觉到能量消耗变化的容易程度）;以及iii）步态的能量结果的恒定性（即，步行的能量结果改变的频率）。在每一种情况下，我们都会扰乱行走的人或环境，夸大其中一个因素，然后研究行走的人能多快地收敛到能量最优步态。最后，我们将建立一个系统，利用这种理解来增强步行者的优化过程，描述步态的变化，使他们能够更快地收敛到能量最优解。 这项研究将为人类神经系统如何在步行过程中优雅地执行能量优化提供基本和基础的理解。这对加拿大的研究和创新有许多广泛的影响，包括：了解伴随衰老或受伤的步态适应;设计考虑到神经系统内部目标的康复方案;设计与使用者协同工作以增强运动的辅助技术;加速运动技能学习。此外，该研究计划将为跨学科的本科生和研究生提供丰富的培训环境，同样准备通过学术界或工业界的职业生涯来受益于加拿大研究和创新的未来。