PGcGAN: Pathological Gait-Conditioned GAN for Human Gait Synthesis

2026-03-15

AI Summary

This study addresses the limitations of existing pathological gait analysis, which suffers from small-scale and low-diversity clinical datasets that hinder effective modeling of diverse gait impairments. To overcome this, the authors propose a Pathological Gait conditional Generative Adversarial Network (PGcGAN), which explicitly embeds one-hot encoded pathological labels into the GAN framework for the first time. By integrating a conditional autoencoder architecture, PGcGAN enables controllable synthesis of six distinct gait types through both the generator and discriminator. The model jointly optimizes adversarial and reconstruction objectives, producing highly realistic 3D pose keypoint sequences that preserve structural and temporal characteristics. Experimental results demonstrate that augmenting training data with these synthetic sequences significantly improves the performance of various temporal models—including GRU, LSTM, and CNN—on pathological gait recognition tasks, thereby validating the effectiveness of the proposed approach for data augmentation.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

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.

Problem

• Pathological gait
• Data scarcity
• Gait impairment
• Clinical dataset variability
• Human gait synthesis

Innovation

• Pathological Gait Synthesis
• Conditional GAN
• Data Augmentation
• 3D Pose Generation
• Gait Classification