上肢多输出多输入闭环模型的反参数
Ian Syndergaard1, Daniel B Free1, Dario Farina2
1Mechanical Engineering, Brigham Young University, Provo, Utah, United States of America.
本研究提出了一种方法和参数估计,用于创建闭环,多输入多输出 (MIMO) 上肢神经肌肉骨模型. 这些模型对于理解运动控制和模拟四肢运动至关重要.
科学领域:
- 神经科学是一个神经科学.
- 生物力学 生物力学
- 系统生物学 系统生物学
背景情况:
- 闭环和多输入多输出 (MIMO) 模型对于模拟上肢运动控制至关重要.
- 现有的模型缺乏全面的,大规模的线性神经肌肉骨表示,既是闭环的,也是MIMO的.
- 开发此类模型的一个重要障碍是缺乏已确定的反参数值 (收益和延迟).
研究的目的:
- 引入一种方法来构建MIMO模型的短环 afferent反在上肢.
- 根据现有文献提供反参数的估计平均值和范围.
主要方法:
- 来自26项先前研究的综合反参数数据.
- 应用了系统稳定性和行为原理来改进参数估计.
- 开发了一种线性模型,包含13个表面肌肉和7个关节自由度 (从肩到手腕).
- 包括同名反 (戈尔吉器官) 和同名/异名反 (肌肉).
主要成果:
- 对上肢神经肌肉骨模型的估计反收益和延迟.
- 通过比较已知中枢延迟差异的信号来验证肌肉的反收益.
- 通过将估计的延迟与测量的内腔长度进行比较,验证了延迟时间,显示出强度适合以弗伦 (R=0.88) 和中度适合以弗伦 (R=0.65) 的延迟.
- 证明了反对模型行为的影响,并将其与实验观测进行了比较.
结论:
- 成功开发了一种创建MIMO神经肌肉骨模型的方法,该模型具有估计的反参数.
- 该模型为模拟上肢运动控制提供了一个基础,使用现实的反机制来模拟上肢运动控制.
- 这些发现有助于更好地了解神经反如何影响上肢生物力学和运动.
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