Virtual models of human gait, or digital twins, offer a promising solution for studying mobility without the need for labor-intensive data collection. However, challenges such as the sim-to-real gap and limited adaptability to diverse walking conditions persist. To address these, we developed and validated a framework to create a skeletal humanoid agent capable of adapting to varying walking speeds while maintaining biomechanically realistic motions. The framework combines a synthetic data generator, which produces biomechanically plausible gait kinematics from open-source biomechanics data, and a training system that uses adversarial imitation learning to train the agent's walking policy. We conducted comprehensive analyses comparing the agent's kinematics, synthetic data, and the original biomechanics dataset. The agent achieved a root mean square error of 5.24 +- 0.09 degrees at varying speeds compared to ground-truth kinematics data, demonstrating its adaptability. This work represents a significant step toward developing a digital twin of human locomotion, with potential applications in biomechanics research, exoskeleton design, and rehabilitation.

翻译：人体步态的虚拟模型（或称数字孪生）为研究运动能力提供了一种前景广阔的解决方案，无需进行劳动密集型的数据采集。然而，诸如仿真与现实间的差距以及对多样化行走条件的有限适应性等挑战依然存在。为解决这些问题，我们开发并验证了一个框架，用于创建能够适应不同行走速度同时保持生物力学真实运动的骨骼型人形智能体。该框架结合了合成数据生成器（从开源生物力学数据中生成生物力学上合理的步态运动学数据）与训练系统（利用对抗式模仿学习来训练智能体的行走策略）。我们进行了综合分析，比较了智能体的运动学数据、合成数据以及原始生物力学数据集。与真实运动学数据相比，该智能体在不同速度下的均方根误差为5.24 ± 0.09度，证明了其适应性。这项工作代表了在开发人体运动数字孪生方面迈出的重要一步，在生物力学研究、外骨骼设计和康复领域具有潜在应用价值。