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Assessment and Evaluation of Hill-type Muscle Models for Predicting In Vivo Force

Assessment and Evaluation of Hill-type Muscle Models for Predicting In Vivo Force
用于预测体内力的 Hill 型肌肉模型的评估和评价
批准号:
9096085
负责人:
Andrew A Biewener
金额:
$32.2万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2008
资助国家:
美国
项目状态:
已结题
起止时间:
2008-09-26 至 2018-06-30

项目摘要

项目成果

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中文摘要
翻译
描述(由申请人提供):Hill型肌肉模型广泛适用于运动功能评估,对设计改进的康复策略至关重要,可作为肌肉驱动模拟的关键组件,旨在识别因年龄或神经肌肉损伤而限制活动性的因素。尽管普遍使用的希尔型模型,很少有研究在体内,随时间变化的条件下,检查其准确性和有效性。拟议项目的总体目标是测试和改进使用先进的Hill型模型评估人体肌肉功能的方法,以及肌电活动和肌肉结构的非侵入性测量。领先的创新目标是实现我们最近开发的两元件Hill型模型,具有独立的快速和缓慢收缩元件,在肌肉驱动的人体骑自行车模拟中,并预测腓肠肌在一系列速度和测力计条件下的力,这些条件与肌腱应变的3D超声测量实验确定的力相比有利。我们以前的工作表明,一种新的两元件模型,由来自EMG记录的快和慢运动单元的招募模式驱动,比传统的具有平均纤维特性的单元件模型产生更好的原位和体内肌肉力量预测。然而,由于我们的山羊动物模型的尺寸很大,我们无法通过实验评估肌肉的F-V特性,这可能降低了模型的预测能力。拟议的研究通过使用一种新的小动物模型(大鼠后肢远端肌肉)在不同的刺激条件下对F-V和周期性功率输出进行创新的原位分析来解决这一限制。这些分析,连同肌肉形状变化的体内3D X射线成像,将进一步推进两元件模型。目前的工作解决了两个具体目标,关键性地扩大了Hill型模型对与康复相关的人类运动功能临床评估的影响:目标#1评估一元与二元模型的准确性,该模型可以使用新颖的基于3D超声的肌腱应变测量来估计在骑自行车测力计上踩踏的人类受试者的受试者特定模拟内的时变肌肉力,束羽状花序和肌肉厚度。目的#2检查运动单位募集和刺激频率如何影响原位肌肉机械输出,目的是更好地预测原位和体内肌肉力量和功率。两元件模型的改进将基于原位收缩动力学、新颖的体内肌肉肌腱力和肌束应变测量以及创新的3D X射线视频荧光透视。目标2的见解将被反复纳入目标1中测试的人体自行车模拟。
英文摘要
DESCRIPTION (provided by applicant): Hill-type muscle models are broadly applicable to the assessment of motor function and critical to the design of improved rehabilitative strategies, serving as key components of muscle- driven simulations aimed at identifying factors that limit mobility due to age or neuromuscular impairment. Despite the ubiquitous use of Hill-type models, few studies have examined their accuracy and validity under in vivo, time-varying conditions. An overarching goal of the proposed project is to test and refine methods for assessing human muscle function using advanced Hill-type models, together with non-invasive measures of electromyographic activity and muscle structure. The lead innovative aim is to implement our recently developed two-element Hill-type model, with independent fast and slow contractile elements, within muscle-driven simulations of human cycling and to predict gastrocnemius forces across a range of speed and ergometry conditions that compare favorably to the forces determined experimentally from 3D ultrasound-based measures of tendon strain. Our previous work showed that a novel two-element model, driven by recruitment patterns of fast and slow motor units derived from EMG recordings, generates significantly better predictions of in situ and in vivo muscle force than traditional one-element models with average fiber properties. However, due to the large size of our goat animal model, we were unable to experimentally assess the muscles' F-V characteristics, which may have diminished the model's predictive capability. The proposed research addresses this limitation by using a new small animal model (rat distal hindlimb muscles) to conduct innovative in situ analyses of F-V and cyclical power output under varying stimulation conditions. These analyses, together with in vivo 3D X-ray imaging of muscle shape changes, will further advance the two-element model. The current work addresses two specific aims, critically broadening the impact of Hill-type models on clinical assessment of human motor function related to rehabilitation: Aim #1 evaluates the accuracy with which one- vs two-element models can estimate time-varying muscle forces within subject-specific simulations of human subjects pedaling on a cycle ergometer, using novel 3D ultrasound-based measures of tendon strain, fascicle pennation and muscle thickness. Aim #2 examines how motor unit recruitment and stimulation frequency affect in situ muscle mechanical output, with the goal of better predicting muscle force and power in situ and in vivo. Refinement of the two-element model will be based on in situ contractile dynamics, novel in vivo muscle-tendon force and fascicle strain measures, and innovative 3D X-ray video fluoroscopy. Insights from Aim 2 will be iteratively incorporated into the simulations of human cycling tested in Aim 1.
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会议论文
Muscle Mass: a Critical but Missing Component in Muscle Modeling and Simulation
  • 批准号:
    10586547
  • 项目类别:
  • 资助金额:
    $48.99万
  • 财政年份:
    2023
  • 负责人:
    Andrew A Biewener
  • 依托单位:
Assessment and Evaluation of Hill-type Muscle Models for Predicting In Vivo Force
  • 批准号:
    8695754
  • 项目类别:
  • 资助金额:
    $33.58万
  • 财政年份:
    2008
  • 负责人:
    Andrew A Biewener
  • 依托单位:
Assessment and evaluation of Hill-type muscle models for predicting in vivo force
  • 批准号:
    7927041
  • 项目类别:
  • 资助金额:
    $50.45万
  • 财政年份:
    2008
  • 负责人:
    Andrew A Biewener
  • 依托单位:
Assessment and evaluation of Hill-type muscle models for predicting in vivo force
  • 批准号:
    7692986
  • 项目类别:
  • 资助金额:
    $37.54万
  • 财政年份:
    2008
  • 负责人:
    Andrew A Biewener
  • 依托单位:
海外基金