Diffusion-Augmented Spatiotemporal Graph Convolution for Clinical Gait and Motor Function Assessment

Accurate assessment of gross motor function in children with cerebral palsy (CP) is essential for clinical decision-making, yet current practice is limited by data scarcity, severe class imbalance, and patient heterogeneity. Recent skeleton-based deep learning approaches, such as spatio-temporal graph convolutional networks (STGCN), enable automatic GMFCS prediction from monocular video but are limited in generalizability and fairness. In this work, we propose a unified generative-diagnostic pipeline that integrates a Conditional Skeleton Diffusion Model (CSDM) with a Biomechanically-Aware Spatio-Temporal Graph Convolutional Network (BA-STGCN). The CSDM generates clinically plausible 2D skeleton gait sequences conditioned on Gross Motor Function Classification System (GMFCS) level, Gait Deviation Index (GDI), and anthropometric covariates, guided by an anatomically structured covariance model to preserve biomechanical fidelity and clinical distributions. These synthetic sequences, combined with real patient data, are used to train the BA-STGCN, which incorporates a symmetry-based loss and a multi-task head for joint GMFCS classification and continuous GDI regression. Extensive evaluation on a pediatric clinical gait dataset demonstrates that our approach achieves 85.7% GMFCS classification accuracy with balanced precision and recall, reduces mean absolute error in GDI prediction to 4.6, and markedly improves recognition of severe phenotypes. These findings highlight that conditional skeleton diffusion, coupled with biomechanically informed graph learning, provides a scalable, interpretable, and privacy-preserving pathway for automated clinical gait assessment in CP.