# In-bed Pressure-based Pose Estimation using Image Space Representation Learning

arXiv: https://arxiv.org/abs/1908.08919 · HTML: https://mlsanity.com/paper/1908.08919/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2019.

## Built on by (most cited first, 7 of 7)

- [Anatomy-guided domain adaptation for 3D in-bed human pose estimation](https://mlsanity.com/paper/2211.12193/) (2022)
- [Under the Cover Infant Pose Estimation using Multimodal Data](https://mlsanity.com/paper/2210.00662/) (2022)
- [Estimating Pose from Pressure Data for Smart Beds with Deep Image-based Pose Estimators](https://mlsanity.com/paper/2206.06518/) (2022)
- [PressureTransferNet: Human Attribute Guided Dynamic Ground Pressure Profile Transfer using 3D simulated Pressure Maps](https://mlsanity.com/paper/2308.00538/) (2023)
- [P2P-Insole: Human Pose Estimation Using Foot Pressure Distribution and Motion Sensors](https://mlsanity.com/paper/2505.00755/) (2025)
- [Multi-modal 3D Pose and Shape Estimation with Computed Tomography](https://mlsanity.com/paper/2503.19405/) (2025)
- [Pressure2Motion: Hierarchical Human Motion Reconstruction from Ground Pressure with Text Guidance](https://mlsanity.com/paper/2511.05038/) (2025)

## Abstract (arXiv)

Recent advances in deep pose estimation models have proven to be effective in a wide range of applications such as health monitoring, sports, animations, and robotics. However, pose estimation models fail to generalize when facing images acquired from in-bed pressure sensing systems. In this paper, we address this challenge by presenting a novel end-to-end framework capable of accurately locating body parts from vague pressure data. Our method exploits the idea of equipping an off-the-shelf pose estimator with a deep trainable neural network, which pre-processes and prepares the pressure data for subsequent pose estimation. Our model transforms the ambiguous pressure maps to images containing shapes and structures similar to the common input domain of the pre-existing pose estimation methods. As a result, we show that our model is able to reconstruct unclear body parts, which in turn enables pose estimators to accurately and robustly estimate the pose. We train and test our method on a manually annotated public pressure map dataset using a combination of loss functions. Results confirm the effectiveness of our method by the high visual quality in the generated images and the high pose estimation rates achieved.