Generating Personalized Lower-Limb Kinematics Across Walking Speeds Using Subject-Conditioned Diffusion

• URL: http://arxiv.org/abs/2607.07533v1
• Date: Wed, 08 Jul 2026 15:29:18 GMT
• Title: Generating Personalized Lower-Limb Kinematics Across Walking Speeds Using Subject-Conditioned Diffusion
• Abstract summary: Personalizing exoskeleton assistance requires user-specific gait data across many locomotor tasks.<n>This work introduces a subject-conditioned residual diffusion framework that generates personalized lower-limb kinematics at unseen walking speeds.
• Score: 1.0159681653887238
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Personalizing exoskeleton assistance requires user-specific gait data across many locomotor tasks, yet collecting this data demands repeated motion capture sessions that are costly, time-intensive, and especially burdensome for clinical populations. This challenge is most acute across walking speeds, where gait changes substantially and deviates further in clinical gait. This work introduces a subject-conditioned residual diffusion framework that generates personalized lower-limb kinematics at unseen walking speeds from a subject's gait sequence at a single seen speed. Given sagittal-plane hip, knee, and ankle trajectories at a seen speed and a desired unseen speed, the model generates a residual that transforms the seen trajectory into the unseen one, using a transformer denoiser conditioned on the subject's gait and the two speeds through feature-wise linear modulation. Trained only on able-bodied data, the model achieved a mean absolute error (MAE) of 3.4° on held-out able-bodied subjects. Without any stroke-specific fine-tuning, it achieved a 6.0° MAE on out-of-training-distribution stroke subjects, retaining subject identity for clinical gait. The framework reduced the MAE by over 70% relative to supervised feed-forward baselines, and a single seen speed matched the accuracy of four speeds within 0.4°. These results demonstrate that subject-conditioned residual diffusion can synthesize personalized gait across speeds from minimal data, reducing the collection burden for downstream exoskeleton personalization.

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