# Estimating Pose from Pressure Data for Smart Beds with Deep Image-based Pose Estimators

arXiv: https://arxiv.org/abs/2206.06518 · HTML: https://mlsanity.com/paper/2206.06518/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 55 references that are in the map.
- First posted on arXiv 13 Jun 2022.

## Builds on (most cited first, 21 of 21)

- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [MoCoGAN: Decomposing Motion and Content for Video Generation](https://mlsanity.com/paper/1707.04993/) (2017)
- [Cascaded Pyramid Network for Multi-Person Pose Estimation](https://mlsanity.com/paper/1711.07319/) (2017)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [Relation Networks for Object Detection](https://mlsanity.com/paper/1711.11575/) (2017)
- [DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model](https://mlsanity.com/paper/1605.03170/) (2016)
- [Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation](https://mlsanity.com/paper/1804.01110/) (2018)
- [Multi-Context Attention for Human Pose Estimation](https://mlsanity.com/paper/1702.07432/) (2017)
- [Learning Feature Pyramids for Human Pose Estimation](https://mlsanity.com/paper/1708.01101/) (2017)
- [Multi-Scale Structure-Aware Network for Human Pose Estimation](https://mlsanity.com/paper/1803.09894/) (2018)
- [Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation](https://mlsanity.com/paper/1705.00389/) (2017)
- [Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data](https://mlsanity.com/paper/2004.01166/) (2020)
- [ArtTrack: Articulated Multi-person Tracking in the Wild](https://mlsanity.com/paper/1612.01465/) (2016)
- [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)
- [In-bed Pressure-based Pose Estimation using Image Space Representation Learning](https://mlsanity.com/paper/1908.08919/) (2019)
- [Deep Multitask Learning for Pervasive BMI Estimation and Identity Recognition in Smart Beds](https://mlsanity.com/paper/2006.10453/) (2020)
- [Identity and Posture Recognition in Smart Beds with Deep Multitask Learning](https://mlsanity.com/paper/2104.02159/) (2021)
- [Multi-Scale Supervised Network for Human Pose Estimation](https://mlsanity.com/paper/1808.01623/) (2018)

## Built on by (most cited first, 4 of 4)

- [Human Pose Estimation from Ambiguous Pressure Recordings with Spatio-temporal Masked Transformers](https://mlsanity.com/paper/2303.05691/) (2023)
- [SkelFormer: Markerless 3D Pose and Shape Estimation using Skeletal Transformers](https://mlsanity.com/paper/2404.12625/) (2024)
- [In-Bed Pose Estimation: A Review](https://mlsanity.com/paper/2402.00700/) (2024)
- [Geometry-Conditioned Diffusion for Occlusion-Robust In-Bed Pose Estimation](https://mlsanity.com/paper/2604.23651/) (2026)

## Abstract (arXiv)

In-bed pose estimation has shown value in fields such as hospital patient monitoring, sleep studies, and smart homes. In this paper, we explore different strategies for detecting body pose from highly ambiguous pressure data, with the aid of pre-existing pose estimators. We examine the performance of pre-trained pose estimators by using them either directly or by re-training them on two pressure datasets. We also explore other strategies utilizing a learnable pre-processing domain adaptation step, which transforms the vague pressure maps to a representation closer to the expected input space of common purpose pose estimation modules. Accordingly, we used a fully convolutional network with multiple scales to provide the pose-specific characteristics of the pressure maps to the pre-trained pose estimation module. Our complete analysis of different approaches shows that the combination of learnable pre-processing module along with re-training pre-existing image-based pose estimators on the pressure data is able to overcome issues such as highly vague pressure points to achieve very high pose estimation accuracy.