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Evidence record 7218 · automatically gathered

PGcGAN: Pathological Gait-Conditioned GAN for Human Gait Synthesis

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

Record details

Published: 15 March 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Healthcare
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (15 March 2026), “PGcGAN: Pathological Gait-Conditioned GAN for Human Gait Synthesis,” evidence record 7218, https://ethics.ai/record/7218 (originally published by arXiv).

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