In this paper, we propose an efficient and robust non-contact heart rate estimation algorithm. The key idea is to recover the fundamental heartbeat frequency from its higher-order spectral features. Our contributions are threefold: 1) carefully review and examine possible approaches in vital signs detection with ultra-wide band (UWB) impulse radar; 2) numerically and experimentally show an important result that, in the spectral domain, the fundamental heartbeat signal is respiration-interference-limited, whereas its higher-order harmonics are noise-limited; and 3) implement an adaptive heart rate monitoring algorithm based on the proposed theory, which is feasible with continuous monitoring. To justify the proposed theory, we perform a spectral analysis of the harmonics of vital signs signal. We validate the proposed algorithm using a controlled vital sign simulator. We experimentally demonstrate the effectiveness of the harmonics-based heart rate estimation algorithm and compare it against existing methods. For completeness, we also provide a limitation analysis of the proposed algorithm.

Microwave radar technology is very attractive for ubiquitous short-range health monitoring due to its non-contact, see-through, privacy-preserving and safe features compared to the competing remote technologies such as optics. The possibility of radar-based approaches for breathing and cardiac sensing was demonstrated a few decades ago. However, investigation regarding the robustness of radar-based vital-sign monitoring (VSM) is not available in the current radar literature. In this paper, we aim to close this gap by presenting an extensive experimental study of vital-sign radar approach. We consider diversity in test subjects, fitness levels, poses/postures, and, more importantly, random body movement (RBM) in the study. We discuss some new insights that lead to robust radar heart-rate (HR) measurements. A novel active motion cancellation signal-processing technique is introduced, exploiting dual ultra-wideband (UWB) radar system for motion-tolerant HR measurements. Additionally, we propose a spectral pruning routine to enhance HR estimation performance. We validate the proposed method theoretically and experimentally. Totally, we record and analyze about 3500 seconds of radar measurements from multiple human subjects.

Practical, in-band, full-duplex (IBFD) systems typically require more than 100 dB of self-interference cancellation (SIC). Digital processing alone is insufficient for achieving this target, which drives us towards supplementary analog mitigation techniques. We propose an analog-domain, self-interference cancellation circuit to enable pass-band, analog SIC in an IBFD system. Analog SIC is limited by several hardware constraints and design choices, including finite tap-delay resolution, non-negative tap constraints, and bit precision quantization. We characterize the performance impact of each of these limitations as a function of signal bandwidth, carrier frequency, bit precision, and other system design parameters. We further characterize the achievable system performance under all of these limitations combined. We simulate several realistic examples to illustrate the relationship between the achievable self-mitigation performance and various system design choices. We implement a simple constrained optimization algorithm informed by these results to optimize the tap-delay weights of the analog circuit under these system constraints. We simulate the achievable mitigation performance and demonstrate as much as 45 dB of analog-domain, self-interference mitigation of a wide-band signal with realistic system configurations.

This study presents findings in the terahertz (THz) frequency spectrum for non-contact cardiac sensing applications. Cardiac pulse information is simultaneously extracted using THz waves based on the established principles in electronics and optics. The first fundamental principle is micro-Doppler motion effect. This motion based method, primarily using coherent phase information from the radar receiver, has been widely exploited in microwave frequency bands and has recently found popularity in millimeter waves (mmWave) for breathe rate and heart rate detection. The second fundamental principle is reflectance based optical measurement using infrared or visible light. The variation in the light reflection is proportional to the volumetric change of the heart, often referred as photoplethysmography (PPG). Herein, we introduce the concept of terahertz-wave-plethysmography (TPG), which detects blood volume changes in the upper dermis tissue layer by measuring the reflectance of THz waves, similar to the existing remote PPG (rPPG) principle. The TPG principle is justified by scientific deduction, electromagnetic wave simulations and carefully designed experimental demonstrations. Additionally, pulse measurements from various peripheral body parts of interest (BOI), palm, inner elbow, temple, fingertip and forehead, are demonstrated using a wideband THz sensing system developed by the Terahertz Electronics Lab at Arizona State University, Tempe. Among the BOIs under test, it is found that the measurements from forehead BOI gives the best accuracy with mean heart rate (HR) estimation error 1.51 beats per minute (BPM) and standard deviation 1.08 BPM. The results validate the feasibility of TPG for direct pulse monitoring. A comparative study on pulse sensitivity is conducted between TPG and rPPG. The results indicate that the TPG contains more pulsatile information from the forehead BOI than that in the rPPG signals in regular office lighting condition and thus generate better heart rate estimation statistic in the form of empirical cumulative distribution function of HR estimation error. Last but not least, TPG penetrability test for covered skin is demonstrated using two types of garment materials commonly used in daily life.

Novel use of radar acoustics for noncontact cardiac parameter estimation is presented as a potentially invaluable addition to wireless sensor networks in Healthcare IoT. Here, radar acoustics refers to the radar captured high-frequency mechanical motion, beyond 20 Hz, on the skin surface induced by heart sound (HS) acoustic waves. Conventional radar-based heart rate (HR) detection methods rely on detecting the heartbeat motion, around 1 Hz, while the unaddressed respiration motion coupling issue prevents consistent heartbeat estimation accuracy in the presence of stronger respiration interference, below 1 Hz. Another issue common to prior methods is the nonadaptive spectral filter design for separating heartbeat signal. As a consequence, these methods cannot be generalized to measurement spaces with high heartbeat variability as demonstrated in this study. To effectively address these limitations, a new HR detection method using radar is proposed to derive a high-fidelity pulse template from the smoothed envelogram of HS measurements. What is more, the feasibility study of radar HS-based HR variability analysis is performed, in which inter-beat-internal is derived from the first and second HS signatures. To that end, automatic identification of HS signatures and signal denoising techniques are developed accordingly. Multiple validation experiments are conducted on human subjects. Our results imply that the new methodology effectively improves remote cardiac sensing performance using the radar technology and presents an exciting opportunity to expand Healthcare IoT, improving patient experiences and outcomes.