以EMG驱动的肌肉骨模型进行参数优化的前进动态框架.
Hao Xie1,2,3,4, Yingpeng Wang5, Tingting Liu3,4
1School of Biomedical Engineering, GuangZhou Medical University, No. 1, Xinzao Road, Xinzao Town, Panyu District, Guangzhou, China.
Journal of neuroengineering and rehabilitation
|February 7, 2026
概括
本研究介绍了一种新的电肌图 (EMG) 驱动的肌肉骨模型,使用OpenSim中的遗传算法来准确估计膝关节扭矩和肌肉力量,以进行个性化生物机械分析.
科学领域:
- 生物力学 生物力学
- 肌肉骨模型的建模
- 计算生理学计算生理学
背景情况:
- 在对象特定的肌肉骨模型中,准确估计肌肉力和关节扭矩是受到肌肉参数确定方面的挑战所阻碍的.
- 这项研究解决了电肌图学 (EMG) 驱动模型的方法问题,该模型与遗传算法和OpenSim API集成.
研究的目的:
- 估计肌肉力和膝关节扭矩,使用特定对象的EMG驱动肌肉骨模型.
- 通过将预测的扭矩与动力计测量结果进行比较来验证模型的准确性.
主要方法:
- 采用希尔肌肉模型,使用过的EMG数据作为输入来计算膝盖扭矩.
- 该模型包含了诸如最佳纤维长度和肌松长度等参数,通过基因模拟的炼算法调整,以最大限度地减少扭矩预测错误.
- 从四头肌肌的表面EMG数据在8名参与者在不同关节角度的同度膝盖任务中被记录下来.
主要成果:
- 拟议的EMG驱动模型实现了高精度,整体平均根平均平方 (RMS) 误差为3.7Nm,R平方值为0.97.
- 当关键肌肉参数同时调整时,模拟的扭矩曲线与测量扭矩曲线非常相匹配.
结论:
- 特定于个体的,精心校准的肌肉骨模型显著提高了肌肉力和膝关节扭矩的预测准确度.
- 开发的方法表明,可以以高精度和最小的误差为膝盖生成个性化的肌单位 (MTU) 参数.
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