在肩膀绑架期间肌肉控制的随机建模
Alex Bersani1, Saulo Martelli2, Maxence Lavaill2
1Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Bologna, Italy.
Journal of biomechanics
|October 4, 2025
概括
Myobolica工具箱使用电肌图 (EMG) 数据准确模拟了肩膀绑架,改善了关节力预测. 这种随机方法增强了用于肩关节运动分析的肌肉骨建模.
科学领域:
- 生物力学 生物力学
- 肌肉骨系统的建模
- 计算生理学计算生理学
背景情况:
- 肩关节运动的变化性至关重要,但对于传统的计算方法来说具有挑战性.
- 随机方法为复杂的生物机械系统提供了一系列可信的解决方案.
- 在下肢模拟中,Myobolica工具箱显示出前景.
研究的目的:
- 评估Myobolica对肩关节模拟的概括性.
- 评估一个电肌图 (EMG) 信息版本的Myobolica用于肩部绑架.
- 将随机模拟准确度与没有EMG数据进行比较.
主要方法:
- 采用了先前开发的肩部肌肉骨模型.
- 利用了公开可用的动力学,EMG和腺关节 (GH) 联合力数据.
- 带有和没有EMG数据的计算1x10^5随机模拟.
主要成果:
- 经EMG告知的Myobolica显著提高了GH关节力预测准确度 (R^2从0.06到0.6).
- EMG数据减少了解决方案带宽,表明了更精确的预测 (2.3到0.65BW).
- 没有EMG数据的模拟仍然产生了有希望的结果.
结论:
- Myobolica,特别是使用EMG数据,提高了肩部绑架模拟的准确性.
- 该工具箱证明了对肩关节生物力学的概括性.
- 需要进一步优化,以降低计算成本和解决模型数据一致性差距.
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