Cyclostationary-Based Vital Signs Detection Using Microwave Radar at 2.5 GHz.

Fatima Sekak,Kamel Haddadi,Kawtar Zerhouni,Christophe Loyez,Madjid Haddad,Fouzia Elbahhar

doi:10.3390/s20123396

Abstract

Non-contact detection and estimation of vital signs such as respiratory and cardiac frequencies is a powerful tool for surveillance applications. In particular, the continuous wave bio-radar has been widely investigated to determine the physiological parameters in a non-contact manner. Since the RF-reflected signal from the human body is corrupted by noise and random body movements, traditional Fourier analysis fails to detect the heart and breathing frequencies. In this effort, cyclostationary analysis has been used to improve the radar performance for non-invasive measurement of respiratory rate and heart rate. However, the preliminary works focus only on one frequency and do not include the impact of attenuation and random movement of the body in the analysis. Hence in this paper, we evaluate the impact of distance and noise on the cyclic features of the reflected signal. Furthermore, we explore the assessment of second order cyclostationary signal processing performance by developing the cyclic mean, the conjugate cyclic autocorrelation and the cyclic cumulant. In addition, the analysis is carried out using a reduced number of samples to reduce the response time. Implementation of the cyclostationary technique using a bi-static radar configuration at 2.5 GHz is shown as an example to demonstrate the proposed approach.

Highlights

Respiration rate (RR) and heart rate (HR) are considered the most important physiological parameters indicating the body’s functioning state
The use of the cyclostationarity theory has been proposed. This theory is based on the estimation and the detection of vital signs from the signal contaminated by noise and random body motion to extract HR and RR
We propose to develop the theoretical vital sign detection analysis based on the second order cyclostationary approach, using cyclic mean, cyclic conjugate autocorrelation functions and cyclic cumulant

Summary

Introduction

Respiration rate (RR) and heart rate (HR) are considered the most important physiological parameters indicating the body’s functioning state. In the case of a low signal-to-noise ratio (SNR), it is difficult to obtain a precise heart rate in the presence of strong harmonics of RR [36] To overcome these problems, the use of the cyclostationarity theory has been proposed. The use of the cyclostationarity theory has been proposed This theory is based on the estimation and the detection of vital signs from the signal contaminated by noise and random body motion to extract HR and RR. We propose to develop the theoretical vital sign detection analysis based on the second order cyclostationary approach, using cyclic mean, cyclic conjugate autocorrelation functions and cyclic cumulant.

Signal Model

Cyclostationarity of Vital Signs

Influence of Distance on Cyclostationary Detection

Experimental Validation

Conclusions