DL-Aided NOMP: A Deep Learning-Based Vital Sign Estimating Scheme Using FMCW Radar

Hsin Yuan Chang, Chia Hung Lin, Yu Chien Lin, Wei Ho Chung, Ta-Sung Lee

研究成果: Conference contribution同行評審

7 引文 斯高帕斯(Scopus)

摘要

Recently, non-contact vital sign estimating devices, which are used for health monitoring, have gradually gained interest among researchers. However, most of these devices have the disadvantages of high power consumption and high cost, which limit their practicality. Therefore, a less-expensive radar-based system is suggested for long-term health monitoring. Existing radar-based vital sign estimating schemes introduce unacceptable estimating errors. In order to improve the precision and stability, we employ Newtonized Orthogonal Matching Pursuit (NOMP) algorithm. NOMP provides better estimating results compared to existing schemes in vital sign estimation tasks. However, the performance of NOMP deteriorates severely under conditions of low signal-to-noise ratio, which causes poor power efficiency. In this study, we propose deep learning (DL)-aided NOMP schemes to tackle the aforementioned issue. Our simulation results and over the air measurements suggest that DL-aided NOMP schemes are superior to existing schemes.

原文English
主出版物標題2020 IEEE 91st Vehicular Technology Conference, VTC Spring 2020 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁數7
ISBN(電子)9781728152073
DOIs
出版狀態Published - 25 5月 2020
事件91st IEEE Vehicular Technology Conference, VTC Spring 2020 - Antwerp, Belgium
持續時間: 25 5月 202028 5月 2020

出版系列

名字IEEE Vehicular Technology Conference
2020-May
ISSN(列印)1550-2252

Conference

Conference91st IEEE Vehicular Technology Conference, VTC Spring 2020
國家/地區Belgium
城市Antwerp
期間25/05/2028/05/20

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