模拟人类低最佳控制:一篇评论
Alex Bersani1,2, Giorgio Davico1,2, Marco Viceconti1,2
1Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna,Italy.
Journal of applied biomechanics
|August 16, 2023
概括
本综述探讨了神经肌肉控制建模,详细介绍了在儿童和患有疾病的儿童中识别非最佳策略的方法. 它涵盖了减少主义,随机和合方法,以更好地理解人类运动.
科学领域:
- 生物力学 生物力学
- 神经科学是一个神经科学.
- 发动机控制器的控制器
背景情况:
- 神经肌肉控制建模对于理解运动至关重要.
- 确定非最佳策略对于患有神经肌肉疾病或儿童的人群至关重要.
- 现有的模型在完全捕捉神经肌肉控制方面存在局限性.
研究的目的:
- 审查和比较用于建模神经肌肉控制的不同方法.
- 突出各种建模技术的演变和局限性.
- 讨论模拟神经和肌肉骨系统相互作用的方法.
主要方法:
- 简化主义方法的审查 (静态/动态优化,基于电肌图的方法).
- 探索随机方法和不受控制的多重体理论.
- 检查神经和肌肉骨系统合的显式建模.
主要成果:
- 减少主义方法提供了简化的观点,但也有局限性.
- 随机方法允许适应性和能源效率的探索.
- 结合的模型旨在克服减少主义方法的局限性.
结论:
- 建模神经肌肉控制需要不同的方法.
- 了解非最佳策略可以为特定人群提供干预信息.
- 未来的模型应该更有效地整合神经和肌肉骨系统的相互作用.
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