用生成模型合成特定步态障碍数据，解决临床数据少的问题。
PGcGAN: Pathological Gait-Conditioned GAN for Human Gait Synthesis
- 用病种标签控制生成，可按六类步态障碍合成新数据。
- 合成数据与真实数据在特征分布和运动模式上高度一致。
- 适合用于步态障碍研究的数据增强，提升识别模型性能。
病理步态分析受限于临床数据少且不均衡，难以建模多样化的步态障碍。为此，我们提出病理步态条件生成对抗网络（PGcGAN），直接从3D关节轨迹数据中合成特定病理类型的步态序列。该框架在生成器和判别器中引入独热编码的病种标签，实现对六类步态障碍的可控生成。生成器采用条件自编码架构，联合对抗损失与重建损失训练，以保留步态的结构与时间特征。在病理步态数据集上的实验表明，合成序列与真实序列在主成分分析（PCA）和t-SNE分析中分布高度重合，视觉运动学检查也显示相似性，且下游分类任务表现优异。将合成数据加入真实数据后，GRU、LSTM和CNN模型的病理步态识别准确率均得到提升，证明该方法能有效支持病理步态分析中的数据增强。
原文摘要 · Abstract (English)
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.
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