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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.

神经肌肉骨骼损伤的常见临床例子包括骨关节炎、中风和帕金森氏症，这些疾病总共影响了大约15%的美国成年人。这种损伤导致行动能力降低，相关健康状况（如心脏病、糖尿病、高血压、肥胖）的风险增加，生活质量下降。由于损害的程度和特征因人而异，因此需要定制方法来解决这一重要的社会问题。然而，目前的方法往往是高度主观的，并遵循“一刀切”的方法，导致患有这些损伤的个体的行走功能恢复有限。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行走功能方法。该项目的目标是开发和分发快速且易于使用的计算机模拟技术，该技术可以预测拟议治疗导致的个人行走变化。如果成功，该项目将对该领域、社会和教育产生广泛的影响。对于该领域，神经肌肉骨骼建模研究人员不熟悉所提出的技术或不具备强大的编程技能，将能够相对容易地开发预测步行模拟。此外，研究人员将通过计划在国家和国际会议上举办的讲习班以及通过网络广泛分发，接触并有机会与新技术进行互动。对于社会，研究人员将能够产生定制的康复策略。例如，定制的步行预测可用于确定新的方法，以最大限度地减少膝部骨关节炎患者的膝关节接触力，或为中风或帕金森病患者最大限度地提高步行速度和对称性。在教育方面，来自代表性不足群体的“有风险”的高中生将接触到技术被用于改善人类健康的方式。该项目提出开发新的最优控制技术，以适应预测人类步行模拟的独特需求。最优控制是工程理论的一个分支，它预测一种控制策略，该策略将使指定的动力系统产生最佳可能的性能（例如，确定如何发射火箭推进器，使火箭以最小的燃料消耗到达所需的轨道）。尽管最优控制理论已广泛用于解决航空航天问题，但其能力尚未被用于人体运动应用。该项目将整合神经肌肉骨骼建模和最优控制这两个传统上不相关的领域。集成技术将使执行复杂的三维步行模拟变得容易，再现和预测异构步行数据集。该技术将针对行走模拟的独特挑战（例如，脚与地面之间的间歇性接触）进行定制，并将能够解决目前难以解决或极其耗时的三维行走问题。主要的发展挑战将是使用最优控制问题公式的已知结构来显著提高步行问题解决过程的计算速度和鲁棒性。主要的应用挑战将是将神经肌肉骨骼模型与不同类型的步行数据相结合，使模型和数据彼此一致。该技术将使用Matlab编程环境，并将基于斯坦福大学研究人员开发的免费OpenSim肌肉骨骼建模软件。一套涉及复杂三维行走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，以帮助推动整个领域的发展。校准个体特定的神经肌肉骨骼行走模型，并在几分钟内预测相应的行走动作，而不是几小时或几天的CPU时间，这将是一项工程突破，有可能改变肌肉骨骼建模研究人员进行大规模人体动作模拟的方式。