@inproceedings{ff27b1eaf1b74adb93ce561fb0edc2da,
title = "Sparse Processing for Driver Respiration Monitoring using In-Vehicle mmWave Radar",
abstract = "We investigate non-contact driver's breath-rate monitoring using a compact millimeter-wave (mmWave) in-vehicle radar. The small form-factor mmWave sensor allows deploying multiple antennas in space-constrained automotive applications while also improving the spatial resolution of the radar. We formulate the breathing rate extraction as a sparse and low-rank recovery problem. Our sparse spectral estimation approach then accurately extracts driver respiration rate from highly cluttered and noisy radar measurements. We validate our proposed approach through real-data collected from a 77 GHz TI AWR6843 radar sensor deployed in a driving simulator.",
keywords = "Automotive sensing, low-rank denoising, mmWave radar, sparse reconstruction, vital sign monitoring",
author = "Yu Rong and Mishra, {Kumar Vijay} and Bliss, {Daniel W.}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE/MTT-S International Microwave Symposium, IMS 2022 ; Conference date: 19-06-2022 Through 24-06-2022",
year = "2022",
doi = "10.1109/IMS37962.2022.9865552",
language = "English (US)",
series = "IEEE MTT-S International Microwave Symposium Digest",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "440--443",
booktitle = "2022 IEEE/MTT-S International Microwave Symposium, IMS 2022",
}