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

arXiv: https://arxiv.org/abs/2201.11828 · HTML: https://mlsanity.com/paper/2201.11828/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 54 references that are in the map.
- First posted on arXiv 27 Jan 2022.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018)
- [GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose](https://mlsanity.com/paper/1803.02276/) (2018)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [MemNet: A Persistent Memory Network for Image Restoration](https://mlsanity.com/paper/1708.02209/) (2017)
- [Seeing Under the Cover: A Physics Guided Learning Approach for In-Bed Pose Estimation](https://mlsanity.com/paper/1907.02161/) (2019)
- [3D Human Pose Estimation on a Configurable Bed from a Pressure Image](https://mlsanity.com/paper/1804.07873/) (2018)
- [In-Bed Pose Estimation: Deep Learning with Shallow Dataset](https://mlsanity.com/paper/1711.01005/) (2017)
- [Simultaneously-Collected Multimodal Lying Pose Dataset: Towards In-Bed Human Pose Monitoring under Adverse Vision Conditions](https://mlsanity.com/paper/2008.08735/) (2020)

## Built on by (most cited first, 3 of 3)

- [BodyMAP -- Jointly Predicting Body Mesh and 3D Applied Pressure Map for People in Bed](https://mlsanity.com/paper/2404.03183/) (2024)
- [Human Pose Estimation from Ambiguous Pressure Recordings with Spatio-temporal Masked Transformers](https://mlsanity.com/paper/2303.05691/) (2023)
- [Improving the Plausibility of Pressure Distributions Synthesized from Depth Image through Generative Modeling](https://mlsanity.com/paper/2512.13757/) (2025)

## Abstract (arXiv)

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 PCSefs0.1 criteria, superior to state-of-the-art methods in the related image regression/translation tasks.