发表机构
Cajal Neuroscience Center, Spanish National Research Council (CSIC); Rey Juan Carlos University (URJC)（西班牙国家研究委员会卡哈尔神经科学中心; 胡安·卡洛斯国王大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究探索条件扩散模型生成下肢步态轨迹，以步长等参数为条件，并在4590个步态周期上验证了其生成逼真且可控轨迹的潜力，助力个性化辅助机器人。
AI 中文摘要
生成以患者特定参数为条件的肌肉骨骼步态轨迹，仍然是可穿戴机器人和康复领域的关键挑战。下肢外骨骼等辅助系统需要参考轨迹，这些轨迹需适应个体形态和治疗目标，同时保持生物力学真实性。传统方法依赖手工制作的步态模板或优化程序，这些方法在受试者和步行条件之间的扩展性较差。在本工作中，我们探索了条件扩散模型，用于生成以步长等步态参数为条件的下肢关节角度轨迹。我们将基线Transformer扩散模型与一种结合自适应归一化和无分类器引导的可控扩散Transformer变体进行了比较。在包含4,590个步态周期的数据集上的实验表明，扩散模型能够生成逼真的周期性步态轨迹，同时实现对步态特征的一定可控性变化，突显了其在辅助机器人中用于个性化步态合成的潜力。
英文摘要
Generation of musculoskeletal gait trajectories conditioned on patient-specific parameters remains a key challenge for wearable robotics and rehabilitation. Assistive systems such as lower-limb exoskeletons require reference trajectories that adapt to individual morphology and therapeutic goals while preserving biomechanical realism. Traditional approaches rely on hand-crafted gait templates or optimization procedures that scale poorly across subjects and walking conditions. In this work, we explore conditional diffusion models for generating lower-limb joint-angle trajectories conditioned on gait parameters such as step length. We compare a baseline transformer diffusion model with a controllable diffusion transformer variant incorporating adaptive normalization and classifier-free guidance. Experiments on a dataset of 4,590 gait cycles show that diffusion models can generate realistic periodic gait trajectories while enabling some controllability variation in gait characteristics, highlighting their potential for personalized gait synthesis in assistive robotics.
CommentsInternational Conference on NeuroRehabilitation (ICNR2026), September 29-October 2, 2026, Seoul, South Korea
