Anthropodynamics: Inferring the Control System Humans Use While Walking and Running

人体动力学：推断人类在行走和跑步时使用的控制系统

• 批准号: 1538342
• 负责人: Manoj Srinivasan
• 金额: $ 17.72万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1538342&HistoricalAwards=false
• 关键词: Anthropodynamics; Inferring; Control; System; Humans

Under normal circumstances, healthy adult humans can walk and run with a vanishingly small likelihood of falling. This level of assurance far exceeds the performance of state-of-the-art legged robots and robotic prosthetic devices. The goal of this project is to reverse engineer the dynamics and control of human locomotion using data from human subject experiments -- that is, to understand the control laws underlying human locomotion through observation of human subjects both walking naturally and responding to changes of terrain, sudden shoves, and other disturbances -- and to apply this understanding to more natural prosthetic devices and more capable legged robots. The results from the project will also give insight into movement disorders and other balance problems in the elderly and other at-risk populations, as well as allow design of more effective balance-improving devices. In this project, the physiological control laws governing human motion will be estimated, based on experiments from both natural unperturbed walking and running, and responses to carefully chosen external perturbations during locomotion. The dynamics and the control laws near periodic motions such as walking and running will be approximated using a factorized Poincare map -- a simple generalization of the classical Poincare map. Factorized Poincare maps are inferred from experimental data using statistical techniques such as maximum likelihood estimation. The specific results this inference will allow the prediction of how the human body will return to steady locomotion in the presence of a external perturbation, in particular, it will allow the estimation of how muscle forces and body movements are modulated to recover to steady state cyclic gait. These inferred control laws will be validated using three-dimensional mathematical biped models to demonstrate quantitative prediction of movement variability and accurate estimation of the likelihood of falls.

在正常情况下，健康的成年人可以行走和跑步，跌倒的可能性微乎其微。这种保证水平远远超过了最先进的腿式机器人和机器人假肢装置的性能。该项目的目标是利用人体实验数据对人体运动的动力学和控制进行逆向工程，即通过观察人体自然行走以及对地形变化、突然推挤和其他干扰的反应来了解人体运动的控制规律，并将这种理解应用于更自然的假肢装置和更强大的腿式机器人。该项目的结果还将深入了解老年人和其他高危人群的运动障碍和其他平衡问题，并有助于设计更有效的平衡改善设备。在该项目中，将根据自然不受干扰的行走和跑步的实验以及对运动过程中精心选择的外部扰动的反应来估计控制人体运动的生理控制定律。接近周期性运动（例如步行和跑步）的动力学和控制定律将使用因式分解的庞加莱图（经典庞加莱图的简单概括）来近似。因式分解庞加莱图是使用最大似然估计等统计技术从实验数据推断出来的。这一推论的具体结果将允许预测人体在存在外部扰动的情况下如何恢复稳定运动，特别是，它将允许估计如何调节肌肉力量和身体运动以恢复到稳态循环步态。这些推断的控制法则将使用三维数学双足模型进行验证，以演示运动变异性的定量预测和跌倒可能性的准确估计。