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用不同复杂度的力测量和神经网络推导的指,手和手臂肌肉参数的预测

Erica M Lindbeck¹, Maximillian T Diaz², Jennifer A Nichols²

¹University of Florida, Department of Electrical and Computer Engineering, Gainesville, FL, United States.

Journal of biomechanics

|October 22, 2023

概括

个性化的肌肉骨模型可以使用简单的高度,重量和力数据来创建. 机器学习能够快速生成模型,与通用模型相比,大大减少模拟错误.

科学领域:

生物力学生物力学
肌肉骨系统的建模
机器学习在医疗保健中的应用

背景情况:

个性化肌肉骨模型对于推进医疗保健至关重要.
目前的个性化方法依赖于复杂,耗时的生理测量.
需要使用易于获取的数据快速生成模型.

研究的目的:

评估使用简单数据 (身高,体重,力) 进行肌肉骨模型个性化的可行性.
从有限的数据中评估机器学习在估计肌肉模型参数和骨密度方面的有效性.
为了比较个性化模型与通用模型在力量模拟中的性能.

主要方法:

利用4万名受试者的合成人群来训练神经网络.
采用模拟的侧边力测量来训练模型.
估计了四个Hill型肌肉模型参数和骨密度.
与一组单独的1万名合成受试者进行验证的参数估计.
生成个性化模型,并与基线模型比较模拟的捏压力.

主要成果:

增加力测量的复杂性减少了大多数参数估计的平方根平均误差.
使用基于神经网络的参数估计的肌肉骨模型显示,与通用模型相比,模拟力的绝对误差降低了80%.
简单的力测量表明了个性化指模型的潜力.

结论:

通过机器学习,可以有效地使用轻松收集的数据,如身高,体重和力,来个性化肌肉骨模型.
这种方法比通用模型提供了显著的改进,减少了模拟错误.
可能需要进一步的研究来将这种方法扩展到其他关节和任务,可能需要额外的测量.

关键词:

手手手的手机器学习是机器学习.肌肉骨系统的建模个性化模型个性化模型大指的使用方法

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