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Contactless Oxygen Monitoring with Gated Transformer
Hao He · Yuan Yuan · Yingcong Chen · Peng Cao · Dina Katabi

Fri Dec 02 01:00 PM -- 02:00 PM (PST) @
Event URL: https://openreview.net/forum?id=Y5IcxZfBxSt »

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.

Author Information

Yuan Yuan (MIT)
Yingcong Chen (The Chinese University of Hong Kong)
Peng Cao (Massachusetts Institute of Technology)
Dina Katabi (Massachusetts Institute of Technology)

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