Real-time Detection of Two Subjects' Respiratory Rates Using Wavelet-Transform-Based 5.8 GHz CW Doppler Radar

Meiyu Li; Luke Xu; Sida Liang; Jianbo Tang

10.1109/BSN58485.2023.10331307

2023

IEEE 19th International Conference on Body Sensor Networks (IEEE BSN)

2376-8886

979-8-3503-1198-3

2023 IEEE 19th International Conference on Body Sensor Networks (BSN)

1-4

9-11 Oct. 2023

Boston, MA, USA

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Using the continuous-wave (CW) Doppler radar to identify and extract the respiratory rates of two subjects is challenging, especially when the subjects have time-varying respiratory rates. In this work, a novel wavelet-transform-based algorithm is proposed to obtain the respiratory rates for the scenario when two subjects are presenting in the CW Doppler radar detection field. A two-point time-frequency spectrum was obtained from the continuous wavelet transform spectrogram, then a peak power density was calculated based on the two-point time-frequency spectrum, with which the respiratory rates can be eventually extracted. The algorithm was evaluated with numerical simulation and tested with human subjects using a 5.8-GHz continuous-wave Doppler radar platform.

Vital sign detection multiple subjects detection Non-contact continuous-wave Doppler radar time-frequency spectrum Wavelet transform

第一

英语

EI ; CPCI-S

Computer Science ; Engineering ; Materials Science

Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Materials Science, Biomaterials

WOS:001116732100065

20240215348077

Doppler radar ; Signal detection ; Simulation platform ; Tracking radar

Information Theory and Signal Processing:716.1 ; Radar Systems and Equipment:716.2 ; Computer Applications:723.5 ; Mathematical Transformations:921.3

IEEE

Dept. of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China

Meiyu Li,Luke Xu,Sida Liang,et al. Real-time Detection of Two Subjects' Respiratory Rates Using Wavelet-Transform-Based 5.8 GHz CW Doppler Radar[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2023:1-4.