CDS&E: A Next-Generation Computation Framework for Predicting Optimal Walking Motion

CDS

基本信息

批准号：
1404767
负责人：
Anil Rao
金额：
$ 50万
依托单位：
University of Florida
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2014
资助国家：
美国
起止时间：
2014-08-15 至 2018-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1404767&HistoricalAwards=false
关键词：
CDS&amp Next Generation Computation Framework

项目摘要

CBET-1404767FreglyCommon clinical examples of neuromusculoskeletal impairments include osteoarthritis, stroke, and Parkinson's disease, which together affect roughly 15% of the U.S. adult population. Such impairments result in reduced mobility, an increased risk of associated health conditions (e.g., heart disease, diabetes, high blood pressure, obesity), and a decreased quality of life. Because extent and characteristics of impairment vary from individual to individual, customized approaches are needed to address this important societal problem. However, current approaches tend to be highly subjective and follow a "one size fits all" approach, resulting in limited restoration of walking function for individuals afflicted with these impairments.The long-term goal of this research is to use computer models to design novel walking function approaches for individuals affected by neuromusculoskeletal disorders. The objective of this project is to develop and distribute fast and easy-to-use computer simulation technology that can predict individual walking changes resulting from a proposed treatment. If successful, the project could have wide-reaching benefits to the field, society, and education. For the field, neuromusculoskeletal modeling researchers who are not familiar with the proposed technology or do not possess strong programming skills will be able to develop predictive walking simulations with relative ease. In addition, researchers will be exposed to and have the chance to interact with the new technology through planned workshops at national and international conferences, as well as through broad distribution via the web. For society, researchers will be able to generate customized rehabilitation strategies. For example, customized walking predictions could be used to identify new ways to minimize knee contact forces for individuals with knee osteoarthritis or maximize walking speed and symmetry for individuals who have had a stroke or have Parkinson's disease. For education, "at risk" high school students from underrepresented groups will be exposed to ways that technology is being used to improve human health. This project proposes to develop novel optimal control technology tailored to the unique needs of predictive human walking simulations. Optimal control is a branch of engineering theory that predicts a control strategy that will produce the best-possible performance of a specified dynamical system (for example, determine how to fire rocket thrusters such that a rocket reaches a desired orbit with minimum fuel expenditure). Although optimal control theory has been used extensively to solve aerospace problems, its capabilities have not been exploited for human movement applications. This project will integrate the two traditionally unrelated fields of neuromusculoskeletal modeling and optimal control. The integrated technology will make it easy to perform complex three-dimensional walking simulations that reproduce and predict heterogeneous walking data sets. The technology will be custom tailored to the unique challenges of walking simulations (e.g., intermittent contact between the feet and the ground) and will be able to solve three-dimensional walking problems that are currently intractable or extremely time consuming. The primary development challenge will be to use the known structure of the optimal control problem formulation to improve dramatically the computational speed and robustness of the solution process for walking problems. The primary utilization challenge will be to integrate neuromusculoskeletal models with diverse types of walking data so that models and data are consistent with one another. The technology will use the Matlab programming environment and will be based on the freely-available OpenSim musculoskeletal modeling software developed by researchers at Stanford University. A suite of three benchmark problems involving complex three-dimensional walking problems will be used to evaluate the technology. The technology and benchmark problems will be broadly distributed to the research community via the web and conferences to help advance the entire field. The ability to calibrate individual-specific neuromusculoskeletal walking models and predict the corresponding walking motions in minutes rather than hours or days of CPU time would be an engineering breakthrough that has the potential to transform the way musculoskeletal modeling researchers perform large-scale human moment simulations.

CBET-1404767FREGLYCOMMON的临床临床实例包括骨骨骼障碍，包括骨关节炎，中风和帕金森氏病，这会影响美国成年人群的15％。这种障碍导致流动性降低，相关健康状况的风险增加（例如心脏病，糖尿病，高血压，肥胖症）以及生活质量降低。由于障碍的程度和特征因个人而异，因此需要定制的方法来解决这一重要的社会问题。但是，当前的方法往往是高度主观的，并且遵循“一件尺寸适合所有方法”的方法，从而使患有这些障碍的个体的步行功能恢复有限。这项研究的长期目标是使用计算机模型为受神经肌肉骨骼疾病影响的个体设计新的步行功能方法。该项目的目的是开发和分发快速，易于使用的计算机仿真技术，以预测拟议治疗的单个步行变化。如果成功，该项目可能会对现场，社会和教育有广泛的好处。对于该领域，不熟悉所提出的技术或不具备强大编程技能的神经肌肉骨骼建模研究人员将能够相对轻松地开发预测性的步行模拟。此外，研究人员将通过国家和国际会议的计划研讨会以及通过网络进行广泛分发，并有机会通过计划的研讨会与新技术进行互动。对于社会，研究人员将能够生成定制的康复策略。例如，可以使用定制的步行预测来确定新方法，以最大程度地减少膝关节骨关节炎的人的膝关节接触力，或者对于患有中风或患有帕金森氏病的人的步行速度和对称性最大化。对于教育，来自代表性不足群体的“处于危险”的高中生将暴露于用于改善人类健康的技术的方式。该项目建议开发针对预测性人行模拟的独特需求而量身定制的新型最佳控制技术。最佳控制是工程理论的一个分支，它可以预测一种控制策略，该策略将产生指定的动态系统的最佳性能（例如，确定如何启动火箭推进器，以便火箭到达具有最低燃料消耗的所需轨道）。尽管最佳控制理论已被广泛用于解决航空航天问题，但其功能并未用于人类运动应用。该项目将整合神经肌肉骨骼建模的两个传统无关的领域和最佳控制。集成技术将使进行复杂的三维步行模拟变得容易，从而繁殖并预测异质的步行数据集。该技术将针对步行模拟的独特挑战（例如，脚和地面之间的间歇接触）定制，并能够解决目前棘手或非常耗时的三维步行问题。主要的发展挑战将是使用最佳控制问题公式的已知结构来显着提高步行问题解决方案过程的计算速度和鲁棒性。主要的利用挑战将是将神经肌肉骨骼模型与不同类型的步行数据集成，以便模型和数据彼此一致。该技术将使用MATLAB编程环境，并基于斯坦福大学研究人员开发的自由开放式肌肉骨骼建模软件。涉及复杂三维步行问题的三个基准问题将用于评估该技术。技术和基准问题将通过网络和会议广泛分发给研究社区，以帮助促进整个领域。校准个体特定的神经肌肉骨骼步行模型并预测在几分钟而不是数小时的CPU时间内进行相应的步行运动的能力将是一个工程突破，有可能改变肌肉骨骼建模研究人员进行大规模的人类力矩模拟。