Low Overhead DMG Sensing for Vital Signs Detection
Steve Blandino, Jihoon Bang, Jian Wang, Samuel Berweger, Jack Chuang, Jelena Senic, Tanguy Ropitault, Camillo Gentile
Abstract
Sensing biometric markers such as respiration rate (RR) and heart rate (HR) in non-medical contexts using the high resolution of Millimeter-Wave (mmWave) Wi-Fi networks has recently gathered considerable attention. A significant challenge in deploying a Wi-Fi system capable of performing sensing tasks is to minimize the overhead on the communication tasks associated with acquiring sensing information, both in terms radio resources and memory usage. In this paper, we explore the potential of IEEE 802.11bf passive sensing as a means to mitigate overhead, while effectively estimating both RR and HR. We showcase the potential to develop a low overhead Wi-Fi system that precisely captures vital signs, even in demanding situations, such as rapidly increasing RR interfering with HR, by integrating microdoppler processing with super-resolution eigenvector noise subspace analysis. The results shows that the proposed methodology enables RR and HR estimation without any radio-resource overhead and requiring very limited memory usage.
BibTeX
@inproceedings{icassp2024_lowoverheaddmgse,
title = {Low Overhead DMG Sensing for Vital Signs Detection},
author = {Steve Blandino and Jihoon Bang and Jian Wang and Samuel Berweger and Jack Chuang and Jelena Senic and Tanguy Ropitault and Camillo Gentile and Nada Golmie},
booktitle = {ICASSP 2024},
year = {2024}
}