Signal Processing Contributions to Contactless Monitoring of Vital Signs Using Radars

Vital signs are a group of biological indicators that show the status of the body’s life-sustaining functions. They provide an objective measurement of the essential physiological functions of a living organism, and their assessment is the critical first step for any clinical evaluation. Monitoring vital sign information provides valuable insight into the patient's condition, including how they are responding to medical treatment and, more importantly, whether the patient is deteriorating. However, conventional contact-based devices are inappropriate for long-term continuous monitoring. Besides mobility restrictions and stress, they can cause discomfort, and epidermal damage, and even lead to pressure necrosis. On the other hand, the contactless monitoring of vital signs using radar devices has several advantages. Radar signals can penetrate through different materials and are not affected by skin pigmentation or external light conditions. Additionally, these devices preserve privacy, can be low-cost, and transmit no more power than a mobile phone. Despite recent advances, accurate contactless vital sign monitoring is still challenging in practical scenarios. The challenge stems from the fact that when we breathe, or when the heart beats, the tiny induced motion of the chest wall surface can be smaller than one millimeter. This means that the vital sign information can be easily lost in the background noise, or even masked by additional body movements from the monitored subject. This thesis aims to propose innovative signal processing solutions to enable the contactless monitoring of vital signs in practical scenarios. Its main contributions are threefold: a new algorithm for recovering the chest wall movements from radar signals; a novel random body movement and interference mitigation technique; and a simple, yet robust and accurate, adaptive estimation framework. These contributions were tested under different operational conditions and scenarios, spanning ideal simulation settings, real data collected while imitating common working conditions in an office environment, and a complete validation with premature babies in a critical care environment. The proposed algorithms were able to precisely recover the chest wall motion, effectively reducing the interfering effects of random body movements, and allowing clear identification of different breathing patterns. This capability is the first step toward frequency estimation and early non-invasive diagnosis of cardiorespiratory problems. In addition, most of the time, the adaptive estimation framework provided breathing and heart rate estimates within the predefined error intervals, being capable of tracking the reference values in different scenarios. Our findings shed light on the strengths and limitations of this technology and lay the foundation for future studies toward a complete contactless solution for vital signs monitoring

On the Analysis of PM/FM Noise Radar Waveforms Considering Modulating Signals with Varied Stochastic Properties

Noise Radar technology is the general term used to describe radar systems that employ realizations of a given stochastic process as transmit waveforms. Originally, carriers modulated in amplitude by a Gaussian random signal, derived from a hardware noise source, were taken into consideration, justifying the adopted nomenclature. With the advances made in hardware as well as the rise of the software defined noise radar concept, waveform design emerges as an important research area related to such systems. The possibility of generating signals with varied stochastic properties increased the potential in achieving systems with enhanced performances. The characterization of random phase and frequency modulated waveforms (more suitable for several applications) has then gained considerable notoriety within the radar community as well. Several optimization algorithms have been proposed in order to conveniently shape both the autocorrelation function of the random samples that comprise the transmit signal, as well as their power spectrum density. Nevertheless, little attention has been driven to properly characterize the stochastic properties of those signals through closed form expressions, jeopardizing the effectiveness of the aforementioned algorithms as well as their reproducibility. Within this context, this paper investigates the performance of several random phase and frequency modulated waveforms, varying the stochastic properties of their modulating signals

FPGA Design and Implementation of a Real-time FM/PM Pseudo Random Waveform Generation for Noise Radars

Noise Radar technology is the general term used to describe radar systems that employ realizations of a given stochastic process as transmit waveforms. With the advances made in hardware as well as the rise of the software defined noise radar concept, waveform design emerges as an important research area related to such systems. Several optimization algorithms have been proposed to generate pseudo-random waveforms with specific desired features, specially with respect to sidelobes. Nevertheless, not only modifying random waveforms may compromise their LPI performance, but also the implementation of such algorithms in real time applications may not be feasible. Within this context, this paper analyzes varied design architectures for FM/PM pseudo-noise waveform generation, considering a real-time application. The proposed architectures are verified in a co-simulation environment using the Xilinx System Generator tool and implemented on reconfigurable hardware, i.e., a Xilinx Field Programmable Gate Array (FPGA) is taken into consideration. Timing, resource consumption, and the trade-offs related to hardware area and performance are then investigated

Joint Waveform/Receiver Design for Vital-Sign Detection in Signal-Dependent Interference

This paper presents the joint design of discrete slow-time radar waveform and receive filter, with the aim of enhancing the Signal to Interference and Noise Ratio (SINR) in phase coded radar systems for vital-sign monitoring. Towards this, we consider maximizing the SINR at the input of the vital-sign estimation block, when transmitting hardware efficient Mary Phase Shift Keying (MPSK) sequences. This multi-variable and non-convex optimization problem is efficiently solved based on a Minimum Variance Distortionless Response (MVDR) filter, with the Coordinate Descent (CD) approach for the sequence optimization, and the obtained results have shown attractive interference suppression capabilities, even for the simple binary case

Sidelobe Performance Analysis of Noise Waveforms Considering the Doppler Mismatch

Waveform design and optimization algorithms generally assume a zero-Doppler ideal case to reach an optimum or satisfactory solution in terms of the matched filter output. Therefore, its performance is usually characterized only in terms of the resultant waveforms autocorrelation function, neglecting the practical situation in which the received signal is modulated by the target’s Doppler shift. Within this context, this work investigates the Doppler mismatch effects in the Integrated Sidelobe Level (ISL) performance of previously designed/optimized noise waveforms. The analysis has shown that, despite much better results for steady targets, the increasing Doppler mismatch reduces the ISL performance of optimized waveforms, until similar levels achieved when no optimization is performed. To address that, a subpulse Doppler processing approach is also considered, and the results have shown that, besides increasing the Doppler tolerance, it has also increased the optimized waveform robustness to the Doppler mismatch, reducing the resultant ISL loss and thus extending its applicability

Statistical Performance Analysis of Radar-Based Vital-Sign Processing Techniques

Radar-based vital-sign monitoring provides several advantages over standard methodologies. Despite the huge amount of recent work, the preference for particular technique(s) is in debt, due to lack of a formal comparison between them. In addition, collection of real data is a time-consuming process and therefore most of the proposed solutions are only evaluated under very limited scenarios. In this paper we present a simulation framework and a selection of results which allow easy performance comparison between radar-based vital-sign processing techniques. The proposed simulation tool scans over multiple breathing and heartbeat frequencies, and the combined effects along the entire signal processing chain can be analyzed, for different combinations of scenarios and techniques. The results have shown specific limitations for each method, thus indicating a need for proper selection based on operating conditions. In addition, while breathing estimation performance is only limited by noise, heartbeat estimation is limited by the presence of breathing harmonics and, despite promising results at specific breathing/heartbeat frequencies, the presented methods fail to fully mitigate this type of interference in all scenarios

Contactless radar-based breathing monitoring of premature infants in the neonatal intensive care unit

Vital sign monitoring systems are essential in the care of hospitalized neonates. Due to the immaturity of their organs and immune system, premature infants require continuous monitoring of their vital parameters and sensors need to be directly attached to their fragile skin. Besides mobility restrictions and stress, these sensors often cause skin irritation and may lead to pressure necrosis. In this work, we show that a contactless radar-based approach is viable for breathing monitoring in the Neonatal intensive care unit (NICU). For the first time, different scenarios common to the NICU daily routine are investigated, and the challenges of monitoring in a real clinical setup are addressed through different contributions in the signal processing framework. Rather than just discarding measurements under strong interference, we present a novel random body movement mitigation technique based on the time-frequency decomposition of the recovered signal. In addition, we propose a simple and accurate frequency estimator which explores the harmonic structure of the breathing signal. As a result, the proposed radar-based solution is able to provide reliable breathing frequency estimation, which is close to the reference cabled device values most of the time. Our findings shed light on the strengths and limitations of this technology and lay the foundation for future studies toward a completely contactless solution for vital signs monitoring