GAITGen: Disentangled Motion-Pathology Impaired Gait Generative Model -- Bringing Motion Generation to the Clinical Domain
Journal:
arXiv
Published Date:
Mar 28, 2025
Abstract
Gait analysis is crucial for the diagnosis and monitoring of movement
disorders like Parkinson's Disease. While computer vision models have shown
potential for objectively evaluating parkinsonian gait, their effectiveness is
limited by scarce clinical datasets and the challenge of collecting large and
well-labelled data, impacting model accuracy and risk of bias. To address these
gaps, we propose GAITGen, a novel framework that generates realistic gait
sequences conditioned on specified pathology severity levels. GAITGen employs a
Conditional Residual Vector Quantized Variational Autoencoder to learn
disentangled representations of motion dynamics and pathology-specific factors,
coupled with Mask and Residual Transformers for conditioned sequence
generation. GAITGen generates realistic, diverse gait sequences across severity
levels, enriching datasets and enabling large-scale model training in
parkinsonian gait analysis. Experiments on our new PD-GaM (real) dataset
demonstrate that GAITGen outperforms adapted state-of-the-art models in both
reconstruction fidelity and generation quality, accurately capturing critical
pathology-specific gait features. A clinical user study confirms the realism
and clinical relevance of our generated sequences. Moreover, incorporating
GAITGen-generated data into downstream tasks improves parkinsonian gait
severity estimation, highlighting its potential for advancing clinical gait
analysis.
