{
  "id": 7218,
  "url": "https://arxiv.org/abs/2603.14409v1",
  "title": "PGcGAN: Pathological Gait-Conditioned GAN for Human Gait Synthesis",
  "summary": "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 acr",
  "authors": "Mritula Chandrasekaran, Sanket Kachole, Jarek Francik, Dimitrios Makris",
  "category": "research",
  "topics": "bias-fairness,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-15T14:50:23.000Z",
  "fetched_at": "2026-07-14T16:33:03.573Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7218",
  "original_url": "https://arxiv.org/abs/2603.14409v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}