Diffusion-Based Generation of Gait Trajectories

2026-09-13

AI Summary

本文针对个性化步态轨迹生成难题，采用条件扩散模型方法，实现基于步态参数的下肢关节角度轨迹生成，为辅助机器人提供支持。

Abstract

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.

Problem

• gait trajectories
• wearable robotics
• rehabilitation
• patient-specific parameters
• biomechanical realism

Innovation

• conditional diffusion models
• controllable diffusion transformer
• personalized gait synthesis