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的方法来询问人类步态的实验测量，以便更好地了解神经肌肉系统在这一重要过程中的功能。 将研究一种新的非线性系统识别策略，该策略利用适合于与标称周期运动（或平均步幅周期）的小偏差的系统模型。 将创建一个频域仅输出系统识别方法，以使用线性时间周期系统理论来表征系统关于该周期轨迹的运动，并且将使用该方法来找到非线性系统的数学模型。 一个基于视频的运动捕捉系统将被用来记录一些受试者的运动，因为他们走在仪器跑步机上，和测量将被用来识别一个简化的模型，他们的神经肌肉功能在步态。 然后，该模型将被纳入计算的肌肉控制前向动力学算法，以获得更多的见解，个别肌肉的作用和功能的神经肌肉系统在步态。这项工作预计将导致改善治疗步态障碍，可能受益于许多成千上万的人在美国谁目前患有这些疾病。 常见的步态障碍可能源于出生缺陷，疾病的残留影响，如脊髓灰质炎，或在体育活动，事故或军事战斗中持续受伤，尽管已经提出了许多治疗步态障碍的方法，但目前很难预测治疗的有效性。 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，将在这项工作中使用的框架是一般的，所以开发的方法也可以用来研究其他非线性系统，如车辆，风力涡轮机，机械等？ 将根据这项研究的结果开设一个短期课程，并将用于与广大受众分享这些新方法。