# 3D Human Pose Estimation on a Configurable Bed from a Pressure Image

arXiv: https://arxiv.org/abs/1804.07873 · HTML: https://mlsanity.com/paper/1804.07873/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2018.

## Built on by (most cited first, 13 of 13)

- [DiffuPose: Monocular 3D Human Pose Estimation via Denoising Diffusion Probabilistic Model](https://mlsanity.com/paper/2212.02796/) (2022)
- [BodyPressure -- Inferring Body Pose and Contact Pressure from a Depth Image](https://mlsanity.com/paper/2105.09936/) (2021)
- [Under the Cover Infant Pose Estimation using Multimodal Data](https://mlsanity.com/paper/2210.00662/) (2022)
- [LInKs "Lifting Independent Keypoints" -- Partial Pose Lifting for Occlusion Handling with Improved Accuracy in 2D-3D Human Pose Estimation](https://mlsanity.com/paper/2309.07243/) (2023)
- [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)
- [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)
- [PressMimic: Pressure-Guided Motion Capture and Control for Humanoid Robot Imitation](https://mlsanity.com/paper/2606.26741/) (2026)
- [MassNet: A Deep Learning Approach for Body Weight Extraction from A Single Pressure Image](https://mlsanity.com/paper/2303.10136/) (2023)
- [In-Bed Pose Estimation: A Review](https://mlsanity.com/paper/2402.00700/) (2024)
- [A Self-supervised Pressure Map human keypoint Detection Approch: Optimizing Generalization and Computational Efficiency Across Datasets](https://mlsanity.com/paper/2402.14241/) (2024)
- [Multi-modal 3D Pose and Shape Estimation with Computed Tomography](https://mlsanity.com/paper/2503.19405/) (2025)

## Abstract (arXiv)

Robots have the potential to assist people in bed, such as in healthcare settings, yet bedding materials like sheets and blankets can make observation of the human body difficult for robots. A pressure-sensing mat on a bed can provide pressure images that are relatively insensitive to bedding materials. However, prior work on estimating human pose from pressure images has been restricted to 2D pose estimates and flat beds. In this work, we present two convolutional neural networks to estimate the 3D joint positions of a person in a configurable bed from a single pressure image. The first network directly outputs 3D joint positions, while the second outputs a kinematic model that includes estimated joint angles and limb lengths. We evaluated our networks on data from 17 human participants with two bed configurations: supine and seated. Our networks achieved a mean joint position error of 77 mm when tested with data from people outside the training set, outperforming several baselines. We also present a simple mechanical model that provides insight into ambiguity associated with limbs raised off of the pressure mat, and demonstrate that Monte Carlo dropout can be used to estimate pose confidence in these situations. Finally, we provide a demonstration in which a mobile manipulator uses our network's estimated kinematic model to reach a location on a person's body in spite of the person being seated in a bed and covered by a blanket.