Pressure Eye: In-bed Contact Pressure Estimation via Contact-less Imaging

TL;DR

This paper introduces PEye, a vision-based system that estimates high-resolution contact pressure maps between a human and bed surfaces, enabling early pressure ulcer detection without expensive pressure mats.

Contribution

The paper presents a novel dual-encoder network with pixel-wise resampling for high-resolution pressure map estimation from vision signals, outperforming existing methods.

Findings

Achieved over 91% accuracy in pressure estimation from RGB and LWIR images.

Demonstrated superior performance compared to state-of-the-art image regression methods.

Enabled non-contact pressure mapping for bed-bound patients.

Abstract

Computer vision has achieved great success in interpreting semantic meanings from images, yet estimating underlying (non-visual) physical properties of an object is often limited to their bulk values rather than reconstructing a dense map. In this work, we present our pressure eye (PEye) approach to estimate contact pressure between a human body and the surface she is lying on with high resolution from vision signals directly. PEye approach could ultimately enable the prediction and early detection of pressure ulcers in bed-bound patients, that currently depends on the use of expensive pressure mats. Our PEye network is configured in a dual encoding shared decoding form to fuse visual cues and some relevant physical parameters in order to reconstruct high resolution pressure maps (PMs). We also present a pixel-wise resampling approach based on Naive Bayes assumption to further enhance the PM regression performance. A percentage of correct sensing (PCS) tailored for sensing estimation accuracy evaluation is also proposed which provides another perspective for performance evaluation under varying error tolerances. We tested our approach via a series of extensive experiments using multimodal sensing technologies to collect data from 102 subjects while lying on a bed. The individual's high resolution contact pressure data could be estimated from their RGB or long wavelength infrared (LWIR) images with 91.8% and 91.2% estimation accuracies in $PCS_{efs0.1}$ criteria, superior to state-of-the-art methods in the related image regression/translation tasks.