PGcGAN：用于人类步态合成的病理步态条件生成对抗网络
PGcGAN: Pathological Gait-Conditioned GAN for Human Gait Synthesis
AI总结：
本文提出PGcGAN，通过条件生成对抗网络合成特定病理步态序列，利用一热编码标签控制生成六类步态，实验表明合成数据能有效提升病理步态识别性能。
摘要：
病理步态分析受限于有限且多变的临床数据集，限制了对多样化步态障碍建模。为解决这一挑战，我们提出了一种病理步态条件生成对抗网络（PGcGAN），直接从观察到的3D姿态关键点轨迹数据中合成特定病理的步态序列。该框架在生成器和判别器中均包含一热编码的病理标签，使能够在六个步态类别中实现受控合成。生成器采用带有对抗和重建目标的条件自编码器架构，以保留结构和时间步态特征。在病理步态数据集上的实验表明，通过PCA和t-SNE分析、视觉运动学检查和下游分类任务，真实序列与合成序列之间有良好的对齐。通过将真实数据与合成序列增强，提高了GRU、LSTM和CNN模型在病理步态识别中的性能，表明病理条件的步态合成可以有效支持病理步态分析中的数据增强。
英文摘要：
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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