发表机构
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)（埃尔朗根-纽伦堡弗里德里希·亚历山大大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将学习状态先验融入物理驱动的步态预测模拟，提升真实感，使运动学与动力学更匹配实验数据，并支持假设检验与泛化重建。
AI 中文摘要
人类运动的预测性模拟是研究人类运动及其潜在运动控制中“假设”场景的有前景工具，但其真实感往往受限。为解决这一差距，我们将一个在大规模人类步态运动学和外部力数据集上训练的学习状态先验整合到预测性模拟中。由此产生的步态模拟在不同步行和跑步速度下产生的运动学和动力学比当前基于物理的模拟更匹配实验数据，达到了与复现学习数据的数据驱动模型相当的精度。此外，我们的方法通过展示不同的最优性假设、肌肉无力和鞋类选择如何影响预测步态，实现了稳健的假设检验。我们还表明，该先验在其训练数据之外具有良好泛化能力，成功地从稀疏标记点集重建了曲线跑步和急停变向动作的全身体运动学。最终，这些结果表明状态先验应广泛整合到预测性模拟中。
英文摘要
Predictive simulation of human movement is a promising tool for studying ``what-if'' scenarios in human movement and its underlying motor control, yet its realism is often limited. To address this gap, we incorporate a learned state prior that is trained on a large-scale dataset of human gait kinematics and external forces into predictive simulations. Resulting gait simulations yield kinematics and kinetics across diverse walking and running speeds that better match experimental data than current physics-based simulations, achieving accuracy comparable to data-driven models that reproduce learned data. Furthermore, our method enables robust hypothesis testing by demonstrating how varying optimality assumptions, muscle weakness, and footwear choices influence predicted gait. We also show that this prior generalizes well beyond its training data, successfully reconstructing full-body kinematics for curved running and cutting maneuvers from sparse marker sets. Ultimately, these results suggest that state priors should be broadly integrated into predictive simulations.
