MEDUSA: Scalable Biometric Sensing in the Wild through Distributed MIMO Radars

Radar-based techniques for detecting vital signs have shown promise for continuous contactless vital sign sensing and healthcare applications. However, real-world indoor environments face significant challenges for existing vital sign monitoring systems. These include signal blockage in non-line-of-sight (NLOS) situations, movement of human subjects, and alterations in location and orientation. Additionally, these existing systems failed to address the challenge of tracking multiple targets simultaneously. To overcome these challenges, we present MEDUSA, a novel coherent ultra-wideband (UWB) based distributed multiple-input multiple-output (MIMO) radar system, especially it allows users to customize and disperse the $16 \times 16$ into sub-arrays. MEDUSA takes advantage of the diversity benefits of distributed yet wirelessly synchronized MIMO arrays to enable robust vital sign monitoring in real-world and daily living environments where human targets are moving and surrounded by obstacles. We've developed a scalable, self-supervised contrastive learning model which integrates seamlessly with our hardware platform. Each attention weight within the model corresponds to a specific antenna pair of Tx and Rx. The model proficiently recovers accurate vital sign waveforms by decomposing and correlating the mixed received signals, including comprising human motion, mobility, noise, and vital signs. Through extensive evaluations involving 21 participants and over 200 hours of collected data (3.75 TB in total, with 1.89 TB for static subjects and 1.86 TB for moving subjects), MEDUSA's performance has been validated, showing an average gain of 20% compared to existing systems employing COTS radar sensors. This demonstrates MEDUSA's spatial diversity gain for real-world vital sign monitoring, encompassing target and environmental dynamics in familiar and unfamiliar indoor environments.Comment: Preprint. Under Revie

Experimental Demonstration of Accurate Noncontact Measurement of Arterial Pulse Wave Displacements Using 79-GHz Array Radar

In this study, we present a quantitative evaluation of the accuracy of simultaneous array-radar-based measurements of the displacements caused at two parts of the human body by arterial pulse wave propagation. To establish the feasibility of accurate radar-based noncontact measurement of this pulse wave propagation, we perform experiments with four participants using a 79-GHz millimeter-wave ultra-wideband multiple-input multiple-output array radar system and a pair of laser displacement sensors. We evaluate the accuracy of the pulse wave propagation measurements by comparing the displacement waveforms that are measured using the radar system with the corresponding waveforms that are measured using the laser sensors. In addition, to evaluate the estimates of the pulse wave propagation channels, we compare the impulse response functions that are calculated from the displacement waveforms obtained from both the radar data and the laser data. The displacement waveforms and the impulse responses both demonstrated the good agreement between the results of the radar and laser measurements. The normalized correlation coefficient between the impulse responses obtained from the radar and laser data on average was as high as 0.97 for the four participants. The results presented here strongly support the feasibility of accurate radar-based noncontact measurement of arterial pulse wave propagation

Noncontact Vital Signs Detection

Human health condition can be accessed by measurement of vital signs, i.e., respiratory rate (RR), heart rate (HR), blood oxygen level, temperature and blood pressure. Due to drawbacks of contact sensors in measurement, non-contact sensors such as imaging photoplethysmogram (IPPG) and Doppler radar system have been proposed for cardiorespiratory rates detection by researchers.The UWB pulse Doppler radars provide high resolution range-time-frequency information. It is bestowed with advantages of low transmitted power, through-wall capabilities, and high resolution in localization. However, the poor signal to noise ratio (SNR) makes it challenging for UWB radar systems to accurately detect the heartbeat of a subject. To solve the problem, phased-methods have been proposed to extract the phase variations in the reflected pulses modulated by human tiny thorax motions. Advance signal processing method, i.e., state space method, can not only be used to enhance SNR of human vital signs detection, but also enable the micro-Doppler trajectories extraction of walking subject from UWB radar data.Stepped Frequency Continuous Wave (SFCW) radar is an alternative technique useful to remotely monitor human subject activities. Compared with UWB pulse radar, it relieves the stress on requirement of high sampling rate analog-to-digital converter (ADC) and possesses higher signal-to-noise-ratio (SNR) in vital signs detection. However, conventional SFCW radar suffers from long data acquisition time to step over many frequencies. To solve this problem, multi-channel SFCW radar has been proposed to step through different frequency bandwidths simultaneously. Compressed sensing (CS) can further reduce the data acquisition time by randomly stepping through 20% of the original frequency steps.In this work, SFCW system is implemented with low cost, off-the-shelf surface mount components to make the radar sensors portable. Experimental results collected from both pulse and SFCW radar systems have been validated with commercial contact sensors and satisfactory results are shown

Generating a Super-resolution Radar Angular Spectrum Using Physiological Component Analysis

In this study, we propose a method for generating an angular spectrum using array radar and physiological component analysis. We develop physiological component analysis to separate radar echoes from multiple body positions, where echoes are phase-modulated by propagating pulse waves. Assuming that the pulse wave displacements at multiple body positions are constant multiples of a time-shifted waveform, the method estimates echoes using a simplified mathematical model. We exploit the mainlobe and nulls of the directional patterns of the physiological component analysis to form an angular spectrum. We applied the proposed method to simulated data to demonstrate that it can generate a super-resolution angular spectrum

Signal Separation Using a Mathematical Model of Physiological Signals for the Measurement of Heart Pulse Wave Propagation With Array Radar

The arterial pulse wave, which propagates along the artery, is an important indicator of various cardiovascular diseases. By measuring the displacement at multiple parts of the human body, pulse wave velocity can be estimated from the pulse transit time. This paper proposes a technique for signal separation using an antenna array, so that pulse wave propagation can be measured in a non-contact manner. The body displacements due to the pulse wave at different body parts are highly correlated, and cannot be accurately separated using techniques that assume independent or uncorrelated signals. The proposed method formulates the signal separation as an optimization problem, based on a mathematical model of the arterial pulse wave. The objective function in the optimization comprises four terms that are derived based on a small-displacement approximation, unimodal impulse response approximation, and a causality condition. The optimization process was implemented using a genetic algorithm. The effectiveness of the proposed method is demonstrated through numerical simulations and experiments.Comment: This paper has been published in IEEE Access (Early Access), 12 pages, 17 figure

Guest editorial: Special issue on selected papers from IEEE BioCAS 2018

The papers in this special section were presented at the 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS 2018) that was held in in Cleveland, OH, from October 17–19, 2018

NON-CONTACT TECHNIQUES FOR HUMAN VITAL SIGN DETECTION AND GAIT ANALYSIS

Human vital signs including respiratory rate, heart rate, oxygen saturation, blood pressure, and body temperature are important physiological parameters that are used to track and monitor human health condition. Another important biological parameter of human health is human gait. Human vital sign detection and gait investigations have been attracted many scientists and practitioners in various fields such as sport medicine, geriatric medicine, bio-mechanic and bio-medical engineering and has many biological and medical applications such as diagnosis of health issues and abnormalities, elderly care and health monitoring, athlete performance analysis, and treatment of joint problems. Thoroughly tracking and understanding the normal motion of human limb joints can help to accurately monitor human subjects or patients over time to provide early flags of possible complications in order to aid in a proper diagnosis and development of future comprehensive treatment plans. With the spread of COVID-19 around the world, it has been getting more important than ever to employ technology that enables us to detect human vital signs in a non-contact way and helps protect both patients and healthcare providers from potentially life-threatening viruses, and have the potential to also provide a convenient way to monitor people health condition, remotely. A popular technique to extract biological parameters from a distance is to use cameras. Radar systems are another attractive solution for non-contact human vital signs monitoring and gait investigation that track and monitor these biological parameters without invading people privacy. The goal of this research is to develop non-contact methods that is capable of extracting human vital sign parameters and gait features accurately. To do that, in this work, optical systems including cameras and proper filters have been developed to extract human respiratory rate, heart rate, and oxygen saturation. Feasibility of blood pressure extraction using the developed optical technique has been investigated, too. Moreover, a wideband and low-cost radar system has been implemented to detect single or multiple human subject’s respiration and heart rate in dark or from behind the wall. The performance of the implemented radar system has been enhanced and it has been utilized for non-contact human gait analysis. Along with the hardware, advanced signal processing schemes have been enhanced and applied to the data collected using the aforementioned radar system. The data processing algorithms have been extended for multi-subject scenarios with high accuracy for both human vital sign detection and gait analysis. In addition, different configurations of this and high-performance radar system including mono-static and MIMO have been designed and implemented with great success. Many sets of exhaustive experiments have been conducted using different human subjects and various situations and accurate reference sensors have been used to validate the performance of the developed systems and algorithms

Doppler Radar Techniques for Distinct Respiratory Pattern Recognition and Subject Identification.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Smart Devices and Systems for Wearable Applications

Wearable technologies need a smooth and unobtrusive integration of electronics and smart materials into textiles. The integration of sensors, actuators and computing technologies able to sense, react and adapt to external stimuli, is the expression of a new generation of wearable devices. The vision of wearable computing describes a system made by embedded, low power and wireless electronics coupled with smart and reliable sensors - as an integrated part of textile structure or directly in contact with the human body. Therefore, such system must maintain its sensing capabilities under the demand of normal clothing or textile substrate, which can impose severe mechanical deformation to the underlying garment/substrate. The objective of this thesis is to introduce a novel technological contribution for the next generation of wearable devices adopting a multidisciplinary approach in which knowledge of circuit design with Ultra-Wide Band and Bluetooth Low Energy technology, realization of smart piezoresistive / piezocapacitive and electro-active material, electro-mechanical characterization, design of read-out circuits and system integration find a fundamental and necessary synergy. The context and the results presented in this thesis follow an “applications driven” method in terms of wearable technology. A proof of concept has been designed and developed for each addressed issue. The solutions proposed are aimed to demonstrate the integration of a touch/pressure sensor into a fabric for space debris detection (CApture DEorbiting Target project), the effectiveness of the Ultra-Wide Band technology as an ultra-low power data transmission option compared with well known Bluetooth (IR-UWB data transmission project) and to solve issues concerning human proximity estimation (IR-UWB Face-to-Face Interaction and Proximity Sensor), wearable actuator for medical applications (EAPtics project) and aerospace physiology countermeasure (Gravity Loading Countermeasure Skinsuit project)

Sensors for Vital Signs Monitoring

Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data