反向距离加权,快速生成大型模拟数据集
Kalyn M Kearney1, Joel B Harley2, Jennifer A Nichols1
1J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.
Journal of biomechanics
|August 20, 2023
概括
反向距离加权 (IDW) 提供了一种快速而准确的方法,用于从计算上昂贵的计算肌肉控制 (CMC) 模拟中插入肌肉激活. 这种方法显著减少了机器学习在生物力学中的数据生成时间.
科学领域:
- 生物力学 生物力学
- 机器学习 机器学习
- 计算建模 计算建模
背景情况:
- 为机器学习生成大型生物力学数据集是一项挑战.
- 基于物理的模拟,如计算肌肉控制 (CMC),是计算密集的.
- 插值方法为数据生成提供了更快的替代方案.
研究的目的:
- 评估反向距离权重 (IDW) 作为一种低成本和准确的方法,用于从CMC模拟中插入肌肉激活.
- 与CMC相比,评估IDW的准确性和计算效率.
主要方法:
- IDW被应用于来自OpenSim侧模拟的肌肉激活数据.
- 模拟数据被组织成各种稀疏度的网格.
- 用CMC和IDW计算肌肉激活,用于随机的质量-高度对.
- 评估了插值错误及其对力的影响.
主要成果:
- 插入肌肉激活的根平均平方误差随着网格密度的增加而减少,不超过4%.
- IDW插值错误对模拟的侧边力 (≤0.1 N) 产生了最小的影响.
- IDW计算时间 (0.95核心秒) 比CMC (4.22核心分钟) 快得多.
结论:
- IDW是一种实用且高效的方法,用于从稀疏的CMC数据集中估计肌肉激活.
- 这种方法可以加速为机器学习应用程序生成生物力学数据.
- 未来的研究可以将IDW扩展到各种生物力学任务和人群.
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