Restoration of movement using muscle synergies to control natural limb dynamics

利用肌肉协同作用恢复运动来控制自然肢体动力学

基本信息

  • 批准号:
    7938931
  • 负责人:
  • 金额:
    $ 19.06万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2009
  • 资助国家:
    美国
  • 起止时间:
    2009-09-30 至 2012-08-31
  • 项目状态:
    已结题

项目摘要

DESCRIPTION (provided by applicant): The control of every movement, whether it is a basic reflex or a sophisticated skill, is highly complex, involving the control of muscles distributed throughout the limb and body. This complexity is present whether movements are produced naturally by the nervous system or artificially by rehabilitation engineers for the restoration of movement following motor impairments. Understanding the neural strategies used to overcome these complexities can therefore potentially provide new advances in our ability to restore movement following injury. We take this approach in the proposed research, attempting to translate insights derived from basic experimental and theoretical research in order to improve strategies used to restore motor function. The experiments performed in this research are based on two recently proposed principles for the simplification of biological motor control. The first principle suggests that the nervous system uses muscle synergies to reduce the number of variables that need to be specified in the production of movements. In this hypothesis, each such 'synergy' controls the activation of a small group of muscles, with complex movements produced by flexibly combining multiple synergies. The second principle suggests that the nervous system exploits the intrinsic dynamics of the limb in order to increase the efficiency of motor control. In this hypothesis, the properties of the muscles and skeleton allow certain motor commands to be particularly effective in producing movements. In this proposal, we combine these two principles in order to develop a novel strategy for the restoration of motor function following injury. In particular, we will develop and evaluate a controller based on muscles synergies which are designed to exploit the intrinsic dynamics of the limb. We have shown in simulation work that this hypothesis is capable of producing a wide range of movement efficiently and effectively. The proposed experiments will extend this simulation work and evaluate this strategy directly by using it to reanimate a paralyzed limb. Specifically, this research will 1) use experimental measurements of the musculoskeletal dynamics to identify a low dimensional representation of the rat hindlimb, 2) identify a set of muscle synergies which controls the intrinsic dynamics of the rat hindlimb, 3) then finally use these synergies to produce movements in a paralyzed limb. This research will therefore directly test whether this strategy of using muscle synergies to exploit intrinsic limb dynamics is capable of restoring motor function following injury. This work will take recent novel theoretical research and translate it to an experimental situation with direct clinical relevance. The results of this research therefore have the potential to significantly advance clinical applications using control strategies to restore movement in patients with motor impairments. PUBLIC HEALTH RELEVANCE: The research in this proposal will evaluate a novel strategy for restoring motor function following paralysis. This strategy will greatly simplify the control of limb movements using functional electrical stimulation, increasing the efficiency and efficacy of rehabilitation strategies. The experiments to be performed in this research therefore have the potential to significantly advance clinical applications for the restoration of function in patients with motor impairments.
描述(由申请人提供):每一个动作的控制,无论是基本的反射还是复杂的技能,都是高度复杂的,涉及到分布在肢体和身体各处的肌肉的控制。无论运动是由神经系统自然产生的,还是由康复工程师人工产生的,都存在这种复杂性,以恢复运动损伤后的运动。因此,了解用于克服这些复杂性的神经策略可能会为我们在受伤后恢复运动的能力提供新的进展。我们在拟议的研究中采用这种方法,试图翻译来自基础实验和理论研究的见解,以改善用于恢复运动功能的策略。 在这项研究中进行的实验是基于两个最近提出的生物运动控制的简化原则。第一个原则表明,神经系统使用肌肉协同作用来减少运动产生中需要指定的变量的数量。在这个假设中,每一个这样的“协同作用”控制着一小群肌肉的激活,通过灵活地组合多种协同作用产生复杂的运动。第二个原理表明,神经系统利用肢体的内在动力学来提高运动控制的效率。在这个假设中,肌肉和骨骼的特性允许某些运动指令在产生运动时特别有效。 在这个建议中,我们联合收割机这两个原则,以开发一种新的策略,恢复运动功能的伤害。特别是,我们将开发和评估一个控制器的基础上肌肉协同作用,旨在利用肢体的内在动力学。我们已经在模拟工作中表明,这种假设能够有效地产生广泛的运动。所提出的实验将扩展这种模拟工作,并通过使用它来使瘫痪的肢体复活来直接评估这种策略。 具体而言,本研究将1)使用肌肉骨骼动力学的实验测量来识别大鼠后肢的低维表示,2)识别一组控制大鼠后肢内在动力学的肌肉协同作用,3)然后最终使用这些协同作用来产生瘫痪肢体的运动。因此,这项研究将直接测试这种利用肌肉协同作用来利用肢体内在动力学的策略是否能够在受伤后恢复运动功能。 这项工作将采取最新的理论研究,并将其转化为具有直接临床相关性的实验情况。因此,这项研究的结果有可能显着推进临床应用,使用控制策略来恢复运动障碍患者的运动。 公共卫生相关性:本提案中的研究将评估一种恢复瘫痪后运动功能的新策略。该策略将大大简化使用功能性电刺激的肢体运动控制,提高康复策略的效率和功效。因此,在这项研究中进行的实验有可能显着推进运动障碍患者功能恢复的临床应用。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Matthew Tresch其他文献

Matthew Tresch的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Matthew Tresch', 18)}}的其他基金

Optimizing the restoration and rehabilitation of function using cortically-controlled FES following SCI
SCI 后使用皮质控制的 FES 优化功能恢复和康复
  • 批准号:
    10397418
  • 财政年份:
    2019
  • 资助金额:
    $ 19.06万
  • 项目类别:
Optimizing the restoration and rehabilitation of function using cortically-controlled FES following SCI
SCI 后使用皮质控制的 FES 优化功能恢复和康复
  • 批准号:
    10613441
  • 财政年份:
    2019
  • 资助金额:
    $ 19.06万
  • 项目类别:
Optimizing the restoration and rehabilitation of function using cortically-controlled FES following SCI
SCI 后使用皮质控制的 FES 优化功能恢复和康复
  • 批准号:
    10160969
  • 财政年份:
    2019
  • 资助金额:
    $ 19.06万
  • 项目类别:
The Neural Control of Internal Joint State
内部关节状态的神经控制
  • 批准号:
    9273197
  • 财政年份:
    2016
  • 资助金额:
    $ 19.06万
  • 项目类别:
The Neural Control of Internal Joint State
内部关节状态的神经控制
  • 批准号:
    8817096
  • 财政年份:
    2014
  • 资助金额:
    $ 19.06万
  • 项目类别:
The Neural Control of Internal Joint State
内部关节状态的神经控制
  • 批准号:
    9115263
  • 财政年份:
    2014
  • 资助金额:
    $ 19.06万
  • 项目类别:
The Neural Control of Internal Joint State
内部关节状态的神经控制
  • 批准号:
    8916841
  • 财政年份:
    2014
  • 资助金额:
    $ 19.06万
  • 项目类别:
In situ measurement of sarcomere operating range in passive and active muscle
被动和主动肌肉肌节工作范围的原位测量
  • 批准号:
    8384368
  • 财政年份:
    2012
  • 资助金额:
    $ 19.06万
  • 项目类别:
In situ measurement of sarcomere operating range in passive and active muscle
被动和主动肌肉肌节工作范围的原位测量
  • 批准号:
    8502250
  • 财政年份:
    2012
  • 资助金额:
    $ 19.06万
  • 项目类别:
Physiological and biomechanical analysis of muscle synergies in rat locomotion
大鼠运动中肌肉协同作用的生理和生物力学分析
  • 批准号:
    7319764
  • 财政年份:
    2007
  • 资助金额:
    $ 19.06万
  • 项目类别:

相似海外基金

Development, Evaluation and Refinement of Metalloenediyne Complex Cyclization Kinetics for Biological Applications
用于生物应用的金属烯二炔络合物环化动力学的开发、评估和完善
  • 批准号:
    2247314
  • 财政年份:
    2023
  • 资助金额:
    $ 19.06万
  • 项目类别:
    Standard Grant
CAREER: Phoretic Transport of Membrane-Bound Biological Colloids in Complex Environments
职业:复杂环境中膜结合生物胶体的电泳传输
  • 批准号:
    2237177
  • 财政年份:
    2023
  • 资助金额:
    $ 19.06万
  • 项目类别:
    Continuing Grant
eMB: Bridging the Gap Between Agent Based Models of Complex Biological Phenomena and Real-World Data Using Surrogate Models
eMB:使用代理模型弥合基于代理的复杂生物现象模型与真实世界数据之间的差距
  • 批准号:
    2324818
  • 财政年份:
    2023
  • 资助金额:
    $ 19.06万
  • 项目类别:
    Standard Grant
CRII: OAC: A Computational Framework for Studying Transport Phenomena in Complex Networks: From Biological Towards Sustainable and Resilient Engineering Networks
CRII:OAC:研究复杂网络中传输现象的计算框架:从生物网络到可持续和弹性工程网络
  • 批准号:
    2349122
  • 财政年份:
    2023
  • 资助金额:
    $ 19.06万
  • 项目类别:
    Standard Grant
Boson Sampling and Quantum Imaging for Complex Biological Systems
复杂生物系统的玻色子采样和量子成像
  • 批准号:
    EP/Y029097/1
  • 财政年份:
    2023
  • 资助金额:
    $ 19.06万
  • 项目类别:
    Research Grant
CRII: III: Harnessing Deep-Learning to Simplify Biological Inference from Complex Imaging Data
CRII:III:利用深度学习简化复杂成像数据的生物推断
  • 批准号:
    2246064
  • 财政年份:
    2023
  • 资助金额:
    $ 19.06万
  • 项目类别:
    Standard Grant
Machine learning for graph-structured data: Understanding complex biological systems
图结构数据的机器学习:理解复杂的生物系统
  • 批准号:
    RGPIN-2020-05341
  • 财政年份:
    2022
  • 资助金额:
    $ 19.06万
  • 项目类别:
    Discovery Grants Program - Individual
Interactions in complex biological systems by nuclear magnetic resonance
通过核磁共振研究复杂生物系统中的相互作用
  • 批准号:
    RGPIN-2018-06200
  • 财政年份:
    2022
  • 资助金额:
    $ 19.06万
  • 项目类别:
    Discovery Grants Program - Individual
Complex Dynamics in Biological Systems: A Bifurcation Theory Approach
生物系统中的复杂动力学:分岔理论方法
  • 批准号:
    RGPIN-2020-06414
  • 财政年份:
    2022
  • 资助金额:
    $ 19.06万
  • 项目类别:
    Discovery Grants Program - Individual
Developing computational, statistical and machine learning methods to uncover biological mechanisms of complex phenotypes
开发计算、统计和机器学习方法来揭示复杂表型的生物学机制
  • 批准号:
    RGPIN-2021-04062
  • 财政年份:
    2022
  • 资助金额:
    $ 19.06万
  • 项目类别:
    Discovery Grants Program - Individual
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了