# In-Bed Pose Estimation: A Review

arXiv: https://arxiv.org/abs/2402.00700 · HTML: https://mlsanity.com/paper/2402.00700/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Builds on (most cited first, 12 of 12)

- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [ETRI-Activity3D: A Large-Scale RGB-D Dataset for Robots to Recognize Daily Activities of the Elderly](https://mlsanity.com/paper/2003.01920/) (2020)
- [Multimodal In-bed Pose and Shape Estimation under the Blankets](https://mlsanity.com/paper/2012.06735/) (2020)
- [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)
- [Towards human-level performance on automatic pose estimation of infant spontaneous movements](https://mlsanity.com/paper/2010.05949/) (2020)
- [Anatomy-guided domain adaptation for 3D in-bed human pose estimation](https://mlsanity.com/paper/2211.12193/) (2022)
- [Unsupervised domain adaptation for clinician pose estimation and instance segmentation in the operating room](https://mlsanity.com/paper/2108.11801/) (2021)
- [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)
- [Estimating Pose from Pressure Data for Smart Beds with Deep Image-based Pose Estimators](https://mlsanity.com/paper/2206.06518/) (2022)

## Abstract (arXiv)

Human pose estimation, the process of identifying joint positions in a person's body from images or videos, represents a widely utilized technology across diverse fields, including healthcare. One such healthcare application involves in-bed pose estimation, where the body pose of an individual lying under a blanket is analyzed. This task, for instance, can be used to monitor a person's sleep behavior and detect symptoms early for potential disease diagnosis in homes and hospitals. Several studies have utilized unimodal and multimodal methods to estimate in-bed human poses. The unimodal studies generally employ RGB images, whereas the multimodal studies use modalities including RGB, long-wavelength infrared, pressure map, and depth map. Multimodal studies have the advantage of using modalities in addition to RGB that might capture information useful to cope with occlusions. Moreover, some multimodal studies exclude RGB and, this way, better suit privacy preservation. To expedite advancements in this domain, we conduct a review of existing datasets and approaches. Our objectives are to show the limitations of the previous studies, current challenges, and provide insights for future works on the in-bed human pose estimation field.