用强化学习模拟假肢跑步，优化截肢者运动表现。
Simulation of Adaptive Running with Flexible Sports Prosthesis using Reinforcement Learning of Hybrid-link System
- 构建混合链系统，结合分段恒定应变模型模拟假肢柔性。
- 在多种虚拟刚度下仿真跑步动作，代谢成本与已有研究一致。
- 适合假肢设计优化与个性化运动分析的研究者使用。
本研究提出一种基于强化学习的框架，用于模拟单侧胫骨截肢者在使用具有叶片弹簧型柔性假肢时的自适应跑步运动。假肢设计通常依赖试错，而综合考虑人体运动与假肢形变相互作用的全身动力学分析，可为个体化设计提供关键洞见。所提出的混合链系统通过引入分段恒定应变（PCS）模型，实现了对假肢柔性的精确建模。在此基础上，该方法利用强化学习生成截肢者的全身动态运动，并融合运动捕捉数据的模仿学习与精确的假肢动力学计算。在多个虚拟假肢刚度条件下进行跑步运动仿真，获取相应的运输代谢成本（COT），并进行分析。结果表明，假肢刚度变化影响跑步动力学与表现，且所得COT值与先前研究一致。研究验证了该方法在虚拟条件下进行仿真与分析的潜力。
原文摘要 · Abstract (English)
This study proposes a reinforcement learning-based framework for adaptive running motion simulation in a unilateral transtibial amputee using a hybrid-link system that incorporates the flexibility of a leaf-spring-type sports prosthesis. The design and selection of sports prostheses typically rely on trial and error. A comprehensive whole-body dynamics analysis that accounts for interactions between human motion and prosthetic deformation can provide valuable insights for user-specific design and selection. The proposed hybrid-link system enables such analysis by integrating a Piece-wise Constant Strain (PCS) model to represent prosthetic flexibility. Based on this system, the simulation methodology generates whole-body dynamic motions of a unilateral transtibial amputee using a reinforcement learning approach. This framework integrates imitation learning based on motion capture data with accurate computation of prosthetic dynamics. Running motions are simulated under multiple virtual prosthetic stiffness conditions, and the corresponding metabolic cost of transport (COT) obtained from these simulations is analyzed. The results suggest that variations in prosthetic stiffness influence running dynamics and performance, and that COT is consistent with values reported in prior study. Our findings demonstrate the potential of the proposed approach for simulation and analysis under virtual conditions that differ from real-world conditions.
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