Contactless Oxygen Monitoring with Radio Waves and Gated Transformer

Hao He, Yuan Yuan, Ying-Cong Chen, Peng Cao, Dina Katabi
Proceedings of the 8th Machine Learning for Healthcare Conference, PMLR 219:248-265, 2023.

Abstract

With the increasing popularity of telehealth, it is crucial to ensure accurate monitoring of basic physiological signals at home with minimal patient overhead. In this paper, we propose a contactless approach for monitoring blood oxygen levels simply by analyzing radio signals in a patient’s room, without the need for wearable devices. Our method extracts a patient’s respiration from radio signals that bounce off their body, and we use a novel neural network, called Gated BERT-UNet, to estimate blood oxygen saturation from the breathing signal. We designed our model to adapt to a patient’s medical indices, such as gender and sleep stages, to provide personalized inference. Specifically, it uses multiple predictive heads, controlled by a gate, to make predictions for different sub-populations. Our extensive empirical results demonstrate that our model achieves high accuracy on both medical and radio-frequency datasets. It outperforms past work on contactless oxygen monitoring, reducing the mean absolute error in oxygen saturation from 2.0% to 1.3%.

Cite this Paper

BibTeX
@InProceedings{pmlr-v219-he23a, title = {Contactless Oxygen Monitoring with Radio Waves and Gated Transformer}, author = {He, Hao and Yuan, Yuan and Chen, Ying-Cong and Cao, Peng and Katabi, Dina}, booktitle = {Proceedings of the 8th Machine Learning for Healthcare Conference}, pages = {248--265}, year = {2023}, editor = {Deshpande, Kaivalya and Fiterau, Madalina and Joshi, Shalmali and Lipton, Zachary and Ranganath, Rajesh and Urteaga, Iñigo and Yeung, Serene}, volume = {219}, series = {Proceedings of Machine Learning Research}, month = {11--12 Aug}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v219/he23a/he23a.pdf}, url = {https://proceedings.mlr.press/v219/he23a.html}, abstract = {With the increasing popularity of telehealth, it is crucial to ensure accurate monitoring of basic physiological signals at home with minimal patient overhead. In this paper, we propose a contactless approach for monitoring blood oxygen levels simply by analyzing radio signals in a patient’s room, without the need for wearable devices. Our method extracts a patient’s respiration from radio signals that bounce off their body, and we use a novel neural network, called Gated BERT-UNet, to estimate blood oxygen saturation from the breathing signal. We designed our model to adapt to a patient’s medical indices, such as gender and sleep stages, to provide personalized inference. Specifically, it uses multiple predictive heads, controlled by a gate, to make predictions for different sub-populations. Our extensive empirical results demonstrate that our model achieves high accuracy on both medical and radio-frequency datasets. It outperforms past work on contactless oxygen monitoring, reducing the mean absolute error in oxygen saturation from 2.0% to 1.3%.} }
Endnote
%0 Conference Paper %T Contactless Oxygen Monitoring with Radio Waves and Gated Transformer %A Hao He %A Yuan Yuan %A Ying-Cong Chen %A Peng Cao %A Dina Katabi %B Proceedings of the 8th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2023 %E Kaivalya Deshpande %E Madalina Fiterau %E Shalmali Joshi %E Zachary Lipton %E Rajesh Ranganath %E Iñigo Urteaga %E Serene Yeung %F pmlr-v219-he23a %I PMLR %P 248--265 %U https://proceedings.mlr.press/v219/he23a.html %V 219 %X With the increasing popularity of telehealth, it is crucial to ensure accurate monitoring of basic physiological signals at home with minimal patient overhead. In this paper, we propose a contactless approach for monitoring blood oxygen levels simply by analyzing radio signals in a patient’s room, without the need for wearable devices. Our method extracts a patient’s respiration from radio signals that bounce off their body, and we use a novel neural network, called Gated BERT-UNet, to estimate blood oxygen saturation from the breathing signal. We designed our model to adapt to a patient’s medical indices, such as gender and sleep stages, to provide personalized inference. Specifically, it uses multiple predictive heads, controlled by a gate, to make predictions for different sub-populations. Our extensive empirical results demonstrate that our model achieves high accuracy on both medical and radio-frequency datasets. It outperforms past work on contactless oxygen monitoring, reducing the mean absolute error in oxygen saturation from 2.0% to 1.3%.
APA
He, H., Yuan, Y., Chen, Y., Cao, P. & Katabi, D.. (2023). Contactless Oxygen Monitoring with Radio Waves and Gated Transformer. Proceedings of the 8th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 219:248-265 Available from https://proceedings.mlr.press/v219/he23a.html.

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