# Seeing Under the Cover: A Physics Guided Learning Approach for In-Bed Pose Estimation

arXiv: https://arxiv.org/abs/1907.02161 · HTML: https://mlsanity.com/paper/1907.02161/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2019.

## Built on by (most cited first, 17 of 17)

- [Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future](https://mlsanity.com/paper/2105.13137/) (2021)
- [BodyPressure -- Inferring Body Pose and Contact Pressure from a Depth Image](https://mlsanity.com/paper/2105.09936/) (2021)
- [Anatomy-guided domain adaptation for 3D in-bed human pose estimation](https://mlsanity.com/paper/2211.12193/) (2022)
- [In-Bed Human Pose Estimation from Unseen and Privacy-Preserving Image Domains](https://mlsanity.com/paper/2111.15124/) (2021)
- [Towards Accurate Cross-Domain In-Bed Human Pose Estimation](https://mlsanity.com/paper/2110.03578/) (2021)
- [Privacy-Preserving In-Bed Pose Monitoring: A Fusion and Reconstruction Study](https://mlsanity.com/paper/2202.10704/) (2022)
- [BodyMAP -- Jointly Predicting Body Mesh and 3D Applied Pressure Map for People in Bed](https://mlsanity.com/paper/2404.03183/) (2024)
- [Estimating Pose from Pressure Data for Smart Beds with Deep Image-based Pose Estimators](https://mlsanity.com/paper/2206.06518/) (2022)
- [Self-supervised 3D Patient Modeling with Multi-modal Attentive Fusion](https://mlsanity.com/paper/2403.03217/) (2024)
- [Heuristic Weakly Supervised 3D Human Pose Estimation](https://mlsanity.com/paper/2105.10996/) (2021)
- [Pressure Eye: In-bed Contact Pressure Estimation via Contact-less Imaging](https://mlsanity.com/paper/2201.11828/) (2022)
- [PI-HMR: Towards Robust In-bed Temporal Human Shape Reconstruction with Contact Pressure Sensing](https://mlsanity.com/paper/2503.00068/) (2025)
- [Video-Based Inpatient Fall Risk Assessment: A Case Study](https://mlsanity.com/paper/2106.07565/) (2021)
- [LWIRPOSE: A novel LWIR Thermal Image Dataset and Benchmark](https://mlsanity.com/paper/2404.10212/) (2024)
- [Automated Patient Positioning with Learned 3D Hand Gestures](https://mlsanity.com/paper/2407.14903/) (2024)
- [Multi-modal 3D Pose and Shape Estimation with Computed Tomography](https://mlsanity.com/paper/2503.19405/) (2025)
- [Patient4D: Temporally Consistent Patient Body Mesh Recovery from Monocular Operating Room Video](https://mlsanity.com/paper/2603.17178/) (2026)

## Abstract (arXiv)

Human in-bed pose estimation has huge practical values in medical and healthcare applications yet still mainly relies on expensive pressure mapping (PM) solutions. In this paper, we introduce our novel physics inspired vision-based approach that addresses the challenging issues associated with the in-bed pose estimation problem including monitoring a fully covered person in complete darkness. We reformulated this problem using our proposed Under the Cover Imaging via Thermal Diffusion (UCITD) method to capture the high resolution pose information of the body even when it is fully covered by using a long wavelength IR technique. We proposed a physical hyperparameter concept through which we achieved high quality groundtruth pose labels in different modalities. A fully annotated in-bed pose dataset called Simultaneously-collected multimodal Lying Pose (SLP) is also formed/released with the same order of magnitude as most existing large-scale human pose datasets to support complex models' training and evaluation. A network trained from scratch on it and tested on two diverse settings, one in a living room and the other in a hospital room showed pose estimation performance of 99.5% and 95.7% in PCK0.2 standard, respectively. Moreover, in a multi-factor comparison with a state-of-the art in-bed pose monitoring solution based on PM, our solution showed significant superiority in all practical aspects by being 60 times cheaper, 300 times smaller, while having higher pose recognition granularity and accuracy.