Monitoring contact pressure in hospital beds is essential for preventing pressure ulcers and enabling real-time patient assessment. Current methods can predict pressure maps but often lack physical plausibility, limiting clinical reliability. This work proposes a framework that enhances plausibility via Informed Latent Space (ILS) and Weight Optimization Loss (WOL) with conditional generative modeling to produce high-fidelity, physically consistent pressure estimates. This study also applies diffusion based conditional Brownian Bridge Diffusion Model (BBDM) and proposes training strategy for its latent counterpart Latent Brownian Bridge Diffusion Model (LBBDM) tailored for pressure synthesis in lying postures. Experiment results shows proposed method improves physical plausibility and performance over baselines: BBDM with ILS delivers highly detailed maps at higher computational cost and large inference time, whereas LBBDM provides faster inference with competitive performance. Overall, the approach supports non-invasive, vision-based, real-time patient monitoring in clinical environments.

翻译：监测医院病床上的接触压力对于预防压疮和实现实时患者评估至关重要。现有方法虽能预测压力分布图，但常缺乏物理合理性，限制了临床可靠性。本研究提出一个框架，该框架通过知情潜在空间（ILS）和权重优化损失（WOL），结合条件生成建模，以生成高保真、物理一致的压力估计。本研究还应用了基于扩散的条件布朗桥扩散模型（BBDM），并为其潜在空间对应模型——潜在布朗桥扩散模型（LBBDM）——提出了一种针对卧姿压力合成量身定制的训练策略。实验结果表明，所提方法在物理合理性和性能上均优于基线模型：结合ILS的BBDM能以更高的计算成本和较长的推理时间生成高度精细的分布图，而LBBDM则能以更快的推理速度提供具有竞争力的性能。总体而言，该方法为临床环境中实现非侵入、基于视觉的实时患者监测提供了支持。