Methods for Experimental Identification of Nonlinear Dynamic Systems of Unknown Form and Order With Application to Human Gait

未知形式和阶数的非线性动态系统的实验识别方法及其应用于人类步态的方法

• 批准号: 0969224
• 负责人: Matthew Allen
• 金额: $ 28万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-15 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0969224&HistoricalAwards=false
• 关键词: Methods; Experimental; Identification; Nonlinear; Dynamic

This research seeks to develop new methods for interrogating experimental measurements of human gait in order to better understand how the neuromuscular system functions during this important process. A new nonlinear system identification strategy will be studied that utilizes a model for the system that is appropriate for small deviations from a nominal periodic motion (or average stride cycle). A frequency-domain output-only system identification method will be created to characterize the motion of the system about this periodic trajectory using linear time-periodic systems theory, and that will be used to find a mathematical model for the nonlinear system. A video-based motion capture system will be used to record the motion of a few subjects as they walk on an instrumented treadmill, and the measurements will be used to identify a reduced model for their neuromuscular function during gait. That model will then be incorporated into the computed muscle control forward dynamics algorithm in order to gain additional insights into the roles of individual muscles and the function of the neuromuscular system during gait.This work is expected to lead to improved treatments for gait disorders, potentially benefitting many of the thousands of people in the United States who currently suffer from these disorders. Common gait disorders may stem from birth defects, residual effects of a disease, such as polio, or from injuries sustained in athletic activities, accidents or military combat, and although numerous treatments have been proposed for gait disorders, it is currently very difficult to predict how effective treatment will be. This work is expected to increase our understanding of gait so that appropriate treatments can be chosen more reliably. Also, the framework that will be used in this work is general, so the methods developed can also be used to study other nonlinear systems such as vehicles, wind turbines, machinery, etc? A short course will be created based on the results of this research and it will be used to share these new methods with a broad audience.

这项研究旨在开发新方法来询问人类步态的实验测量结果，以便更好地了解神经肌肉系统在这一重要过程中如何发挥作用。 将研究一种新的非线性系统识别策略，该策略利用适合与标称周期性运动（或平均步幅周期）的小偏差的系统模型。 将创建一种仅频域输出的系统识别方法，以使用线性时间周期系统理论来表征系统围绕该周期轨迹的运动，并将用于寻找非线性系统的数学模型。 基于视频的运动捕捉系统将用于记录一些受试者在仪表跑步机上行走时的运动，并且测量结果将用于识别他们步态期间神经肌肉功能的简化模型。 然后，该模型将被纳入计算的肌肉控制正向动力学算法中，以便进一步了解步态过程中个体肌肉的作用和神经肌肉系统的功能。这项工作有望改进步态障碍的治疗方法使美国目前患有这些疾病的数千人中的许多人受益。 常见的步态障碍可能源于出生缺陷、疾病（如脊髓灰质炎）的后遗症，或体育活动、事故或军事战斗中受伤，尽管已经提出了多种针对步态障碍的治疗方法，但目前很难预测治疗效果如何。 这项工作有望增加我们对步态的理解，以便更可靠地选择适当的治疗方法。 此外，这项工作将使用的框架是通用的，因此开发的方法是否也可以用于研究其他非线性系统，例如车辆、风力涡轮机、机械等？ 将根据这项研究的结果创建一个短期课程，用于与广大受众分享这些新方法。