EAGER: Treatment Planning for Gait Pathologies Based on Whole-Body Angular and Linear Momentum

EAGER：基于全身角动量和线性动量的步态病理治疗计划

• 批准号: 1052754
• 负责人: Benjamin Fregly
• 金额: $ 11.68万
• 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: 2010
• 资助国家: 美国
• 起止时间: 2010-08-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1052754&HistoricalAwards=false
• 关键词: EAGER; Treatment; Planning; Gait; Pathologies

PI: Fregly, Benjamin J. and Hass, Christopher J.Proposal Number: 1052754 This proposal seeks to develop a computational framework that will facilitate identification of effective personalized rehabilitation strategies for individuals with gait pathologies. The four main objectives are to: 1) Demonstrate that whole-body momentum variations for normal and pathological gait cluster differently from one another and that these clusters can be viewed as "momentum signatures" for different gait patterns; 2) Develop an optimization methodology to predict different subject-specific gait patterns using a subject-specific computational model that matches a specified momentum signature; 3) Predict what normal gait should look like for individuals with a gait pathology by applying a normal gait momentum signature to a computational model of the patient; and 4) Predict where to focus rehabilitation efforts for specific patients by imposing patient-specific limitations (e.g., on coordination, strength, power, or joint ranges of motion) on the patient's computational model and evaluating how different limitations affect the patient's ability to approach a normal gait momentum signature. Computational models are valuable for developing and testing hypotheses that otherwise would be impossible to explore experimentally. They can also provide a theoretical framework to explain experimental observations. In the case of pathological gait, despite many studies reporting that the central nervous system (CNS) regulates angular momentum during walking, no simple control law currently exists to explain how the CNS makes walking efficient or even possible. Fewer studies have looked at how linear momentum is conserved during human locomotion, although recent findings indicate conservation occurs for various locomotion tasks. A computational model that uses basic momentum considerations to predict achievable, improved gait patterns for individuals with pathological gait could be a valuable tool to aid clinicians in making objective, highly effective treatment decisions.Intellectual Merit of the Proposed ActivityThe intellectual merit of the proposed research will be the development of a simple method for generating different gait patterns on a patient-specific basis. Recent results indicate that locomotion tasks exhibit clusters of momentum variations. This finding suggests that an optimization approach that targets these variations may be able to predict which rehabilitation strategies would be most effective for a specific patient. This novel approach will be valuable for people with disabilities resulting from either neurological disorders (i.e., cerebral palsy, stroke) or mobility impairments (i.e., dependence on a cane or crutches) by helping them achieve more normal gait patterns. The transformative nature of this proposed research is the paradigm shift away from making treatment decisions based on subjective experience and instead basing them on objective predictions made by patient-specific computational models that obey the laws of physics and account for patient-specific limitations.Broader Impact Resulting from the Proposed ActivityIf successful, the proposed computational methodology may provide an objective means for identifying where to focus rehabilitation efforts that are likely to produce the largest functional improvement for a particular patient. Not only will it be beneficial for people with pathological gait, but the proposed research can also be applied to other areas, including deep space health (i.e., by helping astronauts achieve adequate loading in a weightless environment to reduce bone and muscle loss) and general mobility (i.e., by helping patients improve their mobility for non-locomotion tasks). In addition, this research will include an underrepresented graduate student who will be mentored through the completion of this project. The research results will be evaluated by comparing the optimization predictions with experimental measurements for normal and emulated pathological gait patterns. The results will be disseminated through publications and conference presentations.

PI：Fregly, Benjamin J. 和 Hass, Christopher J. 提案编号：1052754 该提案旨在开发一个计算框架，以促进为患有步态疾病的个体识别有效的个性化康复策略。四个主要目标是： 1）证明正常和病理步态簇的全身动量变化彼此不同，并且这些簇可以被视为不同步态模式的“动量特征”； 2) 开发一种优化方法，使用与指定动量特征相匹配的特定于受试者的计算模型来预测不同的特定于受试者的步态模式； 3) 通过将正常步态动量特征应用于患者的计算模型，预测患有步态病理学的个体的正常步态应该是什么样子； 4) 通过对患者的计算模型施加患者特定的限制（例如，协调性、力量、力量或关节运动范围）并评估不同的限制如何影响患者接近正常步态动量特征的能力，预测特定患者的康复工作重点在哪里。 计算模型对于开发和测试假设非常有价值，否则这些假设将无法通过实验进行探索。它们还可以提供解释实验观察结果的理论框架。就病理步态而言，尽管许多研究报告中枢神经系统（CNS）在行走过程中调节角动量，但目前不存在简单的控制定律来解释中枢神经系统如何使行走高效甚至成为可能。尽管最近的研究结果表明各种运动任务都会发生线性动量守恒，但很少有研究关注人类运动过程中线性动量如何守恒。 使用基本动量考虑因素来预测病理步态个体可实现的、改进的步态模式的计算模型可能是帮助临床医生做出客观、高效治疗决策的宝贵工具。拟议活动的智力价值拟议研究的智力价值将是开发一种简单的方法，用于根据患者的具体情况生成不同的步态模式。最近的结果表明，运动任务表现出动量变化的集群。这一发现表明，针对这些变化的优化方法可能能够预测哪种康复策略对特定患者最有效。这种新颖的方法对于因神经系统疾病（即脑瘫、中风）或行动障碍（即依赖手杖或拐杖）而导致的残疾人来说非常有价值，可以帮助他们实现更正常的步态模式。这项拟议研究的变革性本质是范式转变，不再根据主观经验做出治疗决策，而是基于患者特定计算模型做出的客观预测，这些计算模型遵守物理定律并考虑患者特定的限制。拟议活动产生更广泛的影响如果成功，拟议的计算方法可能会提供一种客观的方法来确定将康复工作重点放在哪里，从而可能为特定患者带来最大的功能改善。它不仅对患有病理步态的人有益，而且拟议的研究还可以应用于其他领域，包括深空健康（即通过帮助宇航员在失重环境中实现足够的负载以减少骨骼和肌肉损失）和一般活动能力（即通过帮助患者提高非运动任务的活动能力）。此外，这项研究还将包括一名代表性不足的研究生，他将在完成该项目的过程中得到指导。研究结果将通过将优化预测与正常和模拟病理步态模式的实验测量进行比较来评估。结果将通过出版物和会议演讲传播。