Improving the Plausibility of Pressure Distributions Synthesized from Depth Image through Generative Modeling

TL;DR

This paper introduces a generative modeling framework that significantly improves the physical plausibility and fidelity of pressure distribution estimates from depth images, aiding real-time patient monitoring.

Contribution

It develops a novel conditional generative approach with ILS and WOL, and introduces LBBDM for faster, plausible pressure map synthesis from depth images.

Findings

Enhanced pressure map plausibility and detail

LBBDM offers faster inference with competitive accuracy

Proposed methods outperform baseline models in realism and efficiency

Abstract

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.