个性化的空间招聘模式,以运动单元的类型和数量

Douania Ines, J. Laforêt, Boudaoud Sofiane
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引用次数: 0

摘要

在研究具体的科学问题时,有效的计算模型应该包含可靠结果的现实特征。本研究探讨了一个有用且广泛使用的运动单元(MU)池招募模型设计如何影响一个特定现实案例的模拟结果:衰老的肱二头肌(BB)肌肉。本研究:1)研究了使用通用MU招募模型(Fuglevand模型)如何扭曲老年BB肌快速运动单元(MU)的招募率;2)提出了一种基于不同MU类型离散属性范围的新型缩放模型。研究结果表明,混淆最小单位大小和类型的通用最小单位招募模型对老年人这一特殊情况会产生不现实的招募行为。此外,这种模型不适用于在动物和人类获得中观察到的混合、反向和类型离散的招募顺序。新提出的模型在更高的力范围内招募了比有序规模招募更多的快速mu。这方面提高肌肉效率,更现实的预测表面肌电图和力产生的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Personalized spatial recruitment model to motor unit type and number
Efficient computational models should incorporate realistic features for reliable results when investigating specific scientific questions. The present study investigates how a useful and widely used Motor Unit (MU) pool recruitment model design impacts simulation results for a particular realistic case: aged Biceps Brachii (BB) muscle. This study: 1) examines how the use of generic MU recruitment model (Fuglevand model) can distort the recruitment rate of faster Motor Units (MUs) for aged BB muscle, and 2) proposes a new type-scaled model based on discrete property ranges for different MUs types. The results show that generic MU recruitment models confusing size and type of MUs can deliver a non-realistic recruitment behavior for particular cases: elderly people. Additionally, such models are not appropriate for mixed, reversed, and type-discrete recruitment orders observed in animal and human acquisitions. The new proposed model recruits higher number of fast MUs at higher force ranges than the orderly size recruitment. This aspect increases muscle efficiency with more realistic prediction of surface Electromyogram and force generated using models.
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