# Contactless Oxygen Monitoring with Gated Transformer

arXiv: https://arxiv.org/abs/2212.03357 · HTML: https://mlsanity.com/paper/2212.03357/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 56 references that are in the map.
- First posted on arXiv 6 Dec 2022.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut](https://mlsanity.com/paper/2202.11539/) (2022)
- [Targeted Supervised Contrastive Learning for Long-Tailed Recognition](https://mlsanity.com/paper/2111.13998/) (2021)
- [TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut](https://mlsanity.com/paper/2209.00383/) (2022)
- [Learning Longterm Representations for Person Re-Identification Using Radio Signals](https://mlsanity.com/paper/2004.01091/) (2020)
- [Marginalized Average Attentional Network for Weakly-Supervised Learning](https://mlsanity.com/paper/1905.08586/) (2019)
- [Temporal Dynamic Graph LSTM for Action-driven Video Object Detection](https://mlsanity.com/paper/1708.00666/) (2017)
- [Unsupervised Learning for Human Sensing Using Radio Signals](https://mlsanity.com/paper/2207.02370/) (2022)
- [Addressing Feature Suppression in Unsupervised Visual Representations](https://mlsanity.com/paper/2012.09962/) (2020)
- [In-Home Daily-Life Captioning Using Radio Signals](https://mlsanity.com/paper/2008.10966/) (2020)
- [Neural Extractive Summarization with Side Information](https://mlsanity.com/paper/1704.04530/) (2017)
- [Remote Blood Oxygen Estimation From Videos Using Neural Networks](https://mlsanity.com/paper/2107.05087/) (2021)
- [Auxiliary Learning for Deep Multi-task Learning](https://mlsanity.com/paper/1909.02214/) (2019)
- [CLOPS: Continual Learning of Physiological Signals](https://mlsanity.com/paper/2004.09578/) (2020)

## Built on by (most cited first, 1 of 1)

- [Deakin RF-Sensing: Experiments on Correlated Knowledge Distillation for Monitoring Human Postures with Radios](https://mlsanity.com/paper/2305.14829/) (2023)

## Abstract (arXiv)

With the increasing popularity of telehealth, it becomes critical to ensure that basic physiological signals can be monitored accurately at home, with minimal patient overhead. In this paper, we propose a contactless approach for monitoring patients' blood oxygen at home, simply by analyzing the radio signals in the room, without any wearable devices. We extract the patients' respiration from the radio signals that bounce off their bodies and devise a novel neural network that infers a patient's oxygen estimates from their breathing signal. Our model, called Gated BERT-UNet, is designed to adapt to the patient's medical indices (e.g., gender, sleep stages). It has multiple predictive heads and selects the most suitable head via a gate controlled by the person's physiological indices. Extensive empirical results show that our model achieves high accuracy on both medical and radio datasets.