Neuromuscular simulations for predicting functional walking ability

用于预测功能性步行能力的神经肌肉模拟

• 批准号: 2015796
• 负责人: Jessica Allen
• 金额: $ 25.45万
• 依托单位: West Virginia University Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2022-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2015796&HistoricalAwards=false
• 关键词: Neuromuscular; simulations; predicting; functional; walking

Restoration of functional walking ability is a high priority for rehabilitation in pathological populations, e.g., older adults, stroke survivors, individuals with spinal cord injury and Parkinson’s disease. This project will address two critical gaps currently limiting rehabilitation efforts to improve functional walking ability. First, the underlying impairments in neuromuscular control (i.e., how the nervous system recruits muscles to move) that cause pathological walking ability are not well-understood. Second, these impairments can vary from individual to individual such that “one-size-fits-all” rehabilitation approaches produce only modest gains in walking ability. To overcome these gaps, this research will use a combination of experimental motion capture and predictive musculoskeletal computer simulation techniques to identify neuromuscular impairments of walking ability. This project will provide fundamental knowledge about neuromuscular control that is important for functional walking ability. The knowledge gained will help guide rehabilitation interventions to improve pathological walking. This project will also provide educational and research opportunities for socioeconomic and educationally disadvantaged K-12 and undergraduate students in the Appalachian region to learn how biomechanics can improve human health, stimulating their interest and participation in STEM.The objective of this project is to determine the causal relationship between neuromuscular generalization and functional walking ability through predictive simulation techniques. If neuromuscular generalization is identified as important for functional walking ability, which is expected based on preliminary results, the predictive simulation framework can be used to identify impairments in neuromuscular generalization and provide a target for gait rehabilitation. To achieve the project’s objective, the Research Plan is organized under two aims. The FIRST Aim is to characterize the observed relationship between fastest achievable walking speed and neuromuscular generalization across standing reactive balance and walking. The working hypothesis that recruiting standing reactive balance motor modules during walking enables an individual to walk at faster speeds will be tested in healthy young adults, older adults with a history of falls, and stroke survivors. Surface EMGs (electromyographs) will be measured from 12 muscles in the dominant leg (both legs in stroke survivors) while tasks are performed on a split – belt instrumented treadmill. Tasks include (a) standing quietly on a stationary treadmill while being exposed to support-surface translation perturbations through discrete movements of the treadmill belts (standing reactive balance), (b) walking at a self-selected speed on the treadmill for 30 seconds and (c) walking on the treadmill at the fastest speed that can be safely maintained for 30 seconds. Motor modules will be separately identified from the assembled EMG data matrices from each task using non-negative matrix factorization. The SECOND Aim is to demonstrate that increased neuromuscular generalization across standing reactive balance and walking leads to higher maximum walking speed. The working hypothesis that recruiting standing reactive balance motor modules during walking enables an individual to walk at faster speeds will be tested using predictive simulations driven by motor modules that maximize walking speed using the data collected under Aim 1. A generic musculoskeletal model with 23 degrees of freedom and 46 muscles per side (OpenSim Gait 2392 model) will be scaled to subject mass and dimensions. Experimentally observed motor modules will be converted to simulated motor modules through solving the inverse-dynamics based muscle redundancy problem. Walking simulations of single gait cycles that are constrained by motor modules will be generated that track observed motion from the self-selected and maximum walking trials using direct collection in OpenSim. Identifying whether recruiting reactive balance motor modules during walking enables walking at faster speeds will provide strong evidence for neuromuscular generalization as a novel rehabilitation target.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

功能性行走能力的恢复是病理人群（如老年人、中风幸存者、脊髓损伤和帕金森病患者）康复的重中之重。该项目将解决目前限制康复工作以提高功能性行走能力的两个关键空白。首先，神经肌肉控制的潜在损伤（即神经系统如何招募肌肉来运动）导致病理性行走能力的原因尚不清楚。其次，这些损伤因人而异，因此“一刀切”的康复方法只能在行走能力方面产生适度的提高。为了克服这些缺陷，本研究将结合实验运动捕捉和预测肌肉骨骼计算机模拟技术来识别行走能力的神经肌肉损伤。该项目将提供神经肌肉控制的基础知识，这对功能性行走能力很重要。所获得的知识将有助于指导康复干预，以改善病理性行走。该项目还将为阿巴拉契亚地区处于社会经济和教育劣势的K-12和本科生提供教育和研究机会，以了解生物力学如何改善人类健康，激发他们对STEM的兴趣和参与。该项目的目的是通过预测模拟技术确定神经肌肉泛化与功能性行走能力之间的因果关系。如果初步结果表明神经肌肉泛化对功能性行走能力很重要，那么预测模拟框架可用于识别神经肌肉泛化障碍，并为步态康复提供目标。为了实现该项目的目标，研究计划分为两个目标。第一个目的是描述观察到的最快可达到的步行速度与站立反应性平衡和步行的神经肌肉泛化之间的关系。在行走过程中招募站立反应性平衡运动模块可以使个体以更快的速度行走，这一工作假设将在健康的年轻人、有跌倒史的老年人和中风幸存者中进行测试。体表肌电信号（肌电图）将从主腿（中风幸存者的两条腿）的12块肌肉中测量，同时在分离式带仪器跑步机上执行任务。任务包括(a)静静地站在固定的跑步机上，同时暴露在通过跑步机皮带的离散运动产生的支撑面平移扰动下（站立反应平衡），(b)在跑步机上以自己选择的速度行走30秒，(c)在跑步机上以可以安全保持的最快速度行走30秒。电机模块将使用非负矩阵分解从每个任务的组装肌电数据矩阵中单独识别出来。第二个目的是证明在站立反应性平衡和步行中神经肌肉泛化的增加导致更高的最大步行速度。工作假设是，在行走过程中招募站立性反应平衡运动模块可以使个人以更快的速度行走，将使用由运动模块驱动的预测模拟进行测试，这些运动模块使用Aim 1收集的数据最大化行走速度。一个具有23个自由度和每侧46块肌肉的通用肌肉骨骼模型（OpenSim步态2392模型）将按比例缩放到受试者的质量和尺寸。通过解决基于反动力学的肌肉冗余问题，将实验观察到的运动模块转换为模拟的运动模块。通过在OpenSim中直接收集，将生成受运动模块约束的单个步态周期的步行模拟，跟踪自选择和最大步行试验中观察到的运动。确定在步行过程中招募反应性平衡运动模块是否能使步行速度更快，将为神经肌肉泛化作为一种新的康复目标提供强有力的证据。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。