神经网络在动态条件下比Hill型肌肉模型更好地估计肌肉力量
Maria Eleni Athanasiadou1, Monica A Daley2,3, Anne D Koelewijn1
1Chair of Autonomous Systems and Control, Department of Electrical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany.
The Journal of experimental biology
|October 16, 2025
概括
神经网络为捕捉动态肌肉功能的传统Hill型肌肉模型提供了一个有希望的替代方案. 他们在估计肌肉力量方面表现出卓越的准确性,特别是对于快速运动,当提供足够的训练数据时.
科学领域:
- 生物力学 生物力学
- 计算神经科学是一种神经科学.
- 肌肉生理学 肌肉生理学
背景情况:
- 山型肌肉模型很普遍,但在准确表示动态肌肉收缩方面存在局限性.
- 现有的模型在动态运动过程中难以理解肌肉激活和力量之间的复杂关系.
研究的目的:
- 评估神经网络作为估计肌肉力量的希尔型模型的替代方案.
- 评估神经网络在没有预定义的力-长度-速度假设的情况下捕捉动态肌肉功能的能力.
主要方法:
- 神经网络被训练使用sonomicrometry,电肌学,和扣数据从海的肌肉.
- 性能与使用相同训练数据集的优化Hill型模型进行了比较.
- 在不同的肌肉和鸟类上测试了网络概括,并提取了力-长度-速度关系.
主要成果:
- 神经网络的表现始终优于希尔型模型,实现更高的确定系数和更低的误差.
- 与不同肌肉或鸟类相比,同一鸟类内的训练肌肉的模型准确性更高,表明对激活和力度扩大的敏感性.
- 提取的力-长度和力-速度关系被低估了,并且在训练数据分布之外得到了很差的概括.
结论:
- 神经网络显示出作为Hill型模型的优越替代品,用于动态肌肉建模的潜力,特别是在高速运动中.
- 神经网络的准确性高度依赖于训练数据集的质量和规模,需要仔细的实验设计.
- 需要进一步的研究来应对数据收集和扩展方面的挑战,以实现强大的跨肌肉和跨动物应用.
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