# MassNet: A Deep Learning Approach for Body Weight Extraction from A Single Pressure Image

arXiv: https://arxiv.org/abs/2303.10136 · HTML: https://mlsanity.com/paper/2303.10136/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 40 references that are in the map.
- First posted on arXiv 17 Mar 2023.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Uncovering the structure of clinical EEG signals with self-supervised learning](https://mlsanity.com/paper/2007.16104/) (2020)
- [Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition](https://mlsanity.com/paper/2109.09559/) (2021)
- [Contrastive Predictive Coding for Human Activity Recognition](https://mlsanity.com/paper/2012.05333/) (2020)
- [Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data](https://mlsanity.com/paper/2004.01166/) (2020)
- [COCOA: Cross Modality Contrastive Learning for Sensor Data](https://mlsanity.com/paper/2208.00467/) (2022)
- [3D Human Pose Estimation on a Configurable Bed from a Pressure Image](https://mlsanity.com/paper/1804.07873/) (2018)
- [Simultaneously-Collected Multimodal Lying Pose Dataset: Towards In-Bed Human Pose Monitoring under Adverse Vision Conditions](https://mlsanity.com/paper/2008.08735/) (2020)
- [Dynamic Modeling of Hand-Object Interactions via Tactile Sensing](https://mlsanity.com/paper/2109.04378/) (2021)
- [Deep Multitask Learning for Pervasive BMI Estimation and Identity Recognition in Smart Beds](https://mlsanity.com/paper/2006.10453/) (2020)

## Abstract (arXiv)

Body weight, as an essential physiological trait, is of considerable significance in many applications like body management, rehabilitation, and drug dosing for patient-specific treatments. Previous works on the body weight estimation task are mainly vision-based, using 2D/3D, depth, or infrared images, facing problems in illumination, occlusions, and especially privacy issues. The pressure mapping mattress is a non-invasive and privacy-preserving tool to obtain the pressure distribution image over the bed surface, which strongly correlates with the body weight of the lying person. To extract the body weight from this image, we propose a deep learning-based model, including a dual-branch network to extract the deep features and pose features respectively. A contrastive learning module is also combined with the deep-feature branch to help mine the mutual factors across different postures of every single subject. The two groups of features are then concatenated for the body weight regression task. To test the model's performance over different hardware and posture settings, we create a pressure image dataset of 10 subjects and 23 postures, using a self-made pressure-sensing bedsheet. This dataset, which is made public together with this paper, together with a public dataset, are used for the validation. The results show that our model outperforms the state-of-the-art algorithms over both 2 datasets. Our research constitutes an important step toward fully automatic weight estimation in both clinical and at-home practice. Our dataset is available for research purposes at: https://github.com/USTCWzy/MassEstimation.