Analysis of signal processing algorithms for detection of human vital signs using uwb radar

Abstract

Detection of human vital signs using ultrawideband radar systems has been a popular research topic amongst researchers since the 2000s. The detection and monitoring of human vital signs in free space or through obstacles such as walls or debris using radar systems are defined as bio-radiolocation. Detecting human vital signs through a robust clutter such as concrete walls using radar technology is possible. This technology is known as Sense Through Wall Radar (STWR) or Through Wall Radar (TWR). The applications of human vital signs monitoring utilizing radar are wide and grouped in two major fields: civil and military applications. Breathing and heart rates are the principal vital signs of the human body. Breathing and heartbeat frequencies are varied between 0.2-0.6Hz and 0.8 -2.5Hz, respectively. While breathing, the human chest area expands and contacts periodically. Human breathing/heartbeat movements draw a sinusoidal mathematical model, and this model is a signature of human vitality and the presence of human beings. These tiny movements up to centimeters in radar distance are observable thanks to ultrawideband technology. Ultrawideband(UWB) radars are utilized between 3.4 GHZ and 10.6 GHZ, with a band ratio of 1.29-7. This standardization is made by the Federal Communications Commission (FCC). UWB radars have improved target range accuracy and high range resolution, enabling target classification, are robust to interferences such as rain and mist, and have reduced radar dead zones. In addition to the properties mentioned earlier of UWB radars, they are non-ionizing, non-intrusive, and contactless. The non-contact and non-ionized property of UWB radar systems makes them great candidates for monitoring human vital signs in biomedical fields. Sleep monitoring (Sudden Infant Death Syndrome, Obstructive Sleep Apnea Syndrome), analysis of the physical performance of the athletes, veterinarian monitoring, and animal study are examples of these systems' civil and biomedical applications. The contactless measurement technique also enables monitoring of border zones and enemy territories, such as buildings, which is attractive for defense applications. Detecting human victims under debris after natural disasters such as earthquakes using UWB radar systems is helpful for search and rescue teams in the field. In light of the increasing aging population worldwide, UWB radar systems can monitor the activities of older adults, such as walking, falling, breathing, etc. These systems are widely known as Ambient Assisted Living(AAL). Thus, UWB radar systems are not only used for detection and monitoring human vital signs but also for classifying human gestures and movements. The operational specifications of UWB radar systems determine their performance. Thus, engineers seek solutions focusing on time, energy, bandwidth, and space. Higher bandwidths have better range resolution. The longer ranges are obtained with maximized energy. The positioning of antenna systems requires area. Regarding such tiny movements of human breath and heartbeat, ranging accuracy and space budget are crucial. The penetration through heavy clutters with longer ranges requires maximized energy. Moreover, the UWB radar performance is vulnerable to external noises and propagation effects due to earth curvature, atmosphere, and interference. Thus, analyzing the selection of UWB radar operational parameters for detected human vital signs is one of the research topics in the field. The UWB radar accuracy is measured via reference sensors to validate radar operation. Contact-based and non-contact systems are used. Electrocardiography, photoplethysmorgy, and chest belt sensors require contact with human tissues. Lidar and cameras are contactless techniques like UWB radars to analyze human vital signs. However, they suffer from environmental lightning. Electromagnetic tools are also used to model human vital signs. However, they are computationally heavy and complex. Thus, the presence of a simple, realistic, and cost-free vital sign reference system is a gap in the literature. Detecting human vital signs using UWB radars under the debris is an intricate problem for the researchers. The radar specifications, such as antenna geometry, radar type, bandwidth, and system power, are significant. Plus, after data collection in debris, the variational signal processing methods are utilized to enhance weak target returns. The first step is to suppress the clutter and debris in this case. The surrounding environment also affects the UWB radar data and makes additional contributions. Thus, the clutter type is modeled as time-varying and independent from time. Mean Removal, Frame Differencing, Loop-back Filtering, Linear Trend Subtraction, Principal Component Analysis(PCA), and Singular Value Decomposition (SVD) are the well-studied clutter reduction techniques in the literature. Time-frequency visualization of radar breath signals is handled via the most fundamental technique, the Fast Fourier Transform, or advanced techniques such as Hilbert Huang Transform and Short Time Fourier Transform. Hilbert Huang Transform and Short Time Fourier Transform are worthwhile while analyzing more complete scenarios such as additional movements. The feature analysis of these movements is significant for machine learning researchers who classify the movement types. In this study, we studied signal processing algorithms on radar human vital signs. The earthquake in Hatay and Kahramanmaraş in 2023, past pandemics, and a growing number of older adult populations motivated the thesis. Data collection regarding the gaps in literature was the central part of the study. We collected seven data types focused on cluttered zones, body movements, human/radar orientation, and simulation data. While collecting the data, we noticed a lack of publicly available data and the rapid process needed in medical and rescue radars. Thus, we shared our data on Mendeley Data and published its paper. We also shared fundamental signal processing algorithm analysis in this dataset to detect human breath signals under the table. In the remaining part of the studies, we focused on signal processing algorithms and analysis results. We analyzed the dataset using clutter reduction techniques and time-spectral analysis techniques. We presented detailed data performance analysis outcomes. Our studies on the effects of body movements on radar breath data were published in two international conferences. We developed a novel UWB radar breath simulator based on the electromagnetic properties of human tissues. Finally, we presented a brief conclusion.