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

• URL: http://arxiv.org/abs/2201.11828v1
• Date: Thu, 27 Jan 2022 22:22:17 GMT
• Title: Pressure Eye: In-bed Contact Pressure Estimation via Contact-less Imaging
• Authors: Shuangjun Liu, Sarah Ostadabbas
• Abstract summary: We present our pressure eye (PEye) approach to estimate contact pressure between a human body and the surface she is lying on. PEye could ultimately enable the prediction and early detection of pressure ulcers in bed-bound patients.
• Score: 18.35652911833834
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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