PGcGAN: Pathological Gait-Conditioned GAN for Human Gait Synthesis
This work addresses data scarcity in pathological gait analysis for medical applications, but it is incremental as it applies a conditional GAN approach to a specific domain.
The paper tackled the problem of limited clinical datasets for pathological gait analysis by proposing PGcGAN, a generative adversarial network that synthesizes pathology-specific gait sequences from 3D pose data, resulting in improved pathological gait recognition across models when augmenting real data with synthetic sequences.
Pathological gait analysis is constrained by limited and variable clinical datasets, which restrict the modeling of diverse gait impairments. To address this challenge, we propose a Pathological Gait-conditioned Generative Adversarial Network (PGcGAN) that synthesises pathology-specific gait sequences directly from observed 3D pose keypoint trajectories data. The framework incorporates one-hot encoded pathology labels within both the generator and discriminator, enabling controlled synthesis across six gait categories. The generator adopts a conditional autoencoder architecture trained with adversarial and reconstruction objectives to preserve structural and temporal gait characteristics. Experiments on the Pathological Gait Dataset demonstrate strong alignment between real and synthetic sequences through PCA and t-SNE analyses, visual kinematic inspection, and downstream classification tasks. Augmenting real data with synthetic sequences improved pathological gait recognition across GRU, LSTM, and CNN models, indicating that pathology-conditioned gait synthesis can effectively support data augmentation in pathological gait analysis.