GaitDiffusion: Diffusion-based Gait Generation Conditioned on Parkinson Severity

Generating patient-specific gait patterns is essential for digital twinning and clinical assessment, data augmentation due to the scarcity of patient-specific data, and assistive robotics where gait patterns must adapt to individual pathology and therapeutic goals while preserving biomechanical realism. However, learning to generate full-body gait at clinical scale remains challenging. Here we present GaitDiffusion, a two-stage latent diffusion framework that generates full-body gait sequences conditioned on a clinical severity level scale, i.e., the Unified Parkinson's Disease (PD) Rating Scale (UPDRS). PD is a suitable testbed for pathology-conditioned generation as gait patterns degrade with clinical severity, a progression that hand-crafted healthy templates cannot represent. GaitDiffusion includes a 1D convolutional VAE with surrogate UPDRS classification and class-prototype guidance that compresses sliding-window motion into a compact latent space with embedded clinical structure, and a Diffusion Transformer (DiT) with adaptive layer normalization and classifier-free guidance, which denoises class-conditioned latents. A range-of-motion (ROM) supervision loss enforces kinematic amplitude directly on the decoded output. Trained on the multi-site CARE-PD dataset, GaitDiffusion reproduces the clinical progression of bradykinesia: mean sagittal-leg ROM decreases monotonically with severity, matching real gait with a ROM error of $\pm 5.8\%$ on average, while achieving a mean dynamic time warping of 0.585 across all four classes and an overall latent-space Fréchet inception distance of 6.28. A classifier trained on real gait features assigns the generated gait its intended UPDRS severity in 84.4% of cases, supporting use of the generated trajectories as severity-appropriate reference patterns for exoskeleton control and as synthetic training data for downstream PD gait analysis. Compared with the strongest prior pathology-conditioned generator, GaitDiffusion reduces the Average Variance Error and the Absolute Stooped Posture Mean Difference by 97.9% and 8.3%, respectively, while maintaining a competitive Absolute Arm Swing Mean Difference. Moreover, its continuous latent space enables differentiable biomechanical supervision. Overall, GaitDiffusion shows stable full-body pathology-conditioned gait generation using an ambulatory medical scale.