# Thermal Imaging and Radar for Remote Sleep Monitoring of Breathing and Apnea

arXiv: https://arxiv.org/abs/2407.11936 · HTML: https://mlsanity.com/paper/2407.11936/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 90 references that are in the map.
- First posted on arXiv 16 Jul 2024.

## Builds on (most cited first, 10 of 10)

- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks](https://mlsanity.com/paper/1805.07888/) (2018)
- [EfficientPhys: Enabling Simple, Fast and Accurate Camera-Based Vitals Measurement](https://mlsanity.com/paper/2110.04447/) (2021)
- [MoRe-Fi: Motion-robust and Fine-grained Respiration Monitoring via Deep-Learning UWB Radar](https://mlsanity.com/paper/2111.08195/) (2021)
- [Motion Matters: Neural Motion Transfer for Better Camera Physiological Measurement](https://mlsanity.com/paper/2303.12059/) (2023)
- [Diverse R-PPG: Camera-Based Heart Rate Estimation for Diverse Subject Skin-Tones and Scenes](https://mlsanity.com/paper/2010.12769/) (2020)
- [Remote Blood Oxygen Estimation From Videos Using Neural Networks](https://mlsanity.com/paper/2107.05087/) (2021)
- [A Multi-Channel Ratio-of-Ratios Method for Noncontact Hand Video Based SpO2 Monitoring Using Smartphone Cameras](https://mlsanity.com/paper/2107.08528/) (2021)
- [Deep Learning-Enabled Sleep Staging From Vital Signs and Activity Measured Using a Near-Infrared Video Camera](https://mlsanity.com/paper/2306.03711/) (2023)
- [Equitable non-contact infrared thermography after solar loading using deep learning](https://mlsanity.com/paper/2304.08832/) (2023)

## Abstract (arXiv)

Polysomnography (PSG), the current gold standard method for monitoring and detecting sleep disorders, is cumbersome and costly. At-home testing solutions, known as home sleep apnea testing (HSAT), exist. However, they are contact-based, a feature which limits the ability of some patient populations to tolerate testing and discourages widespread deployment. Previous work on non-contact sleep monitoring for sleep apnea detection either estimates respiratory effort using radar or nasal airflow using a thermal camera, but has not compared the two or used them together. We conducted a study on 10 participants, ages 34 - 78, with suspected sleep disorders using a hardware setup with a synchronized radar and thermal camera. We show the first comparison of radar and thermal imaging for sleep monitoring, and find that our thermal imaging method outperforms radar significantly. Our thermal imaging method detects apneas with an accuracy of 0.99, a precision of 0.68, a recall of 0.74, an F1 score of 0.71, and an intra-class correlation of 0.70; our radar method detects apneas with an accuracy of 0.83, a precision of 0.13, a recall of 0.86, an F1 score of 0.22, and an intra-class correlation of 0.13. We also present a novel proposal for classifying obstructive and central sleep apnea by leveraging a multimodal setup. This method could be used accurately detect and classify apneas during sleep with non-contact sensors, thereby improving diagnostic capacities in patient populations unable to tolerate current technology.