Human Respiration Localization Method Using UWB Linear Antenna Array

Human respiration is the basic vital sign in remote monitoring. There has been remarkable progress in this area, but some challenges still remain to obtain the angle-of-arrival (AOA) and distinguish the individual signals. This paper presents a 2D noncontact human respiration localization method using Ultra-Wideband (UWB) 1D linear antenna array. The imaging reconstruction based on beamforming is used to estimate the AOA of the human chest. The distance-slow time 2D matrix at the estimated AOA is processed to obtain the distance and respiration frequency of the vital sign. The proposed method can be used to isolate signals from individual targets when more than one human object is located in the surveillance space. The feasibility of the proposed method is demonstrated via the simulation and experiment results

UWB Pulse Radar for Human Imaging and Doppler Detection Applications

We were motivated to develop new technologies capable of identifying human life through walls. Our goal is to pinpoint multiple people at a time, which could pay dividends during military operations, disaster rescue efforts, or assisted-living. Such system requires the combination of two features in one platform: seeing-through wall localization and vital signs Doppler detection. Ultra-wideband (UWB) radar technology has been used due to its distinct advantages, such as ultra-low power, fine imaging resolution, good penetrating through wall characteristics, and high performance in noisy environment. Not only being widely used in imaging systems and ground penetrating detection, UWB radar also targets Doppler sensing, precise positioning and tracking, communications and measurement, and etc. A robust UWB pulse radar prototype has been developed and is presented here. The UWB pulse radar prototype integrates seeing-through imaging and Doppler detection features in one platform. Many challenges existing in implementing such a radar have been addressed extensively in this dissertation. Two Vivaldi antenna arrays have been designed and fabricated to cover 1.5-4.5 GHz and 1.5-10 GHz, respectively. A carrier-based pulse radar transceiver has been implemented to achieve a high dynamic range of 65dB. A 100 GSPS data acquisition module is prototyped using the off-the-shelf field-programmable gate array (FPGA) and analog-to-digital converter (ADC) based on a low cost solution: equivalent time sampling scheme. Ptolemy and transient simulation tools are used to accurately emulate the linear and nonlinear components in the comprehensive simulation platform, incorporated with electromagnetic theory to account for through wall effect and radar scattering. Imaging and Doppler detection examples have been given to demonstrate that such a “Biometrics-at-a-glance” would have a great impact on the security, rescuing, and biomedical applications in the future

Wide Band Embedded Slot Antennas for Biomedical, Harsh Environment, and Rescue Applications

For many designers, embedded antenna design is a very challenging task when designing embedded systems. Designing Antennas to given set of specifications is typically tailored to efficiently radiate the energy to free space with a certain radiation pattern and operating frequency range, but its design becomes even harder when embedded in multi-layer environment, being conformal to a surface, or matched to a wide range of loads (environments). In an effort to clarify the design process, we took a closer look at the key considerations for designing an embedded antenna. The design could be geared towards wireless/mobile platforms, wearable antennas, or body area network. Our group at UT has been involved in developing portable and embedded systems for multi-band operation for cell phones or laptops. The design of these antennas addressed single band/narrowband to multiband/wideband operation and provided over 7 bands within the cellular bands (850 MHz to 2 GHz). Typically the challenge is: many applications require ultra wide band operation, or operate at low frequency. Low frequency operation is very challenging if size is a constraint, and there is a need for demonstrating positive antenna gain

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

Antennas And Wave Propagation In Wireless Body Area Networks: Design And Evaluation Techniques

Recently, fabrication of miniature electronic devices that can be used for wireless connectivity becomes of great interest in many applications. This has resulted in many small and compact wireless devices that are either implantable or wearable. As these devices are small, the space for the antenna is limited. An antenna is the part of the wireless device that receives and transmits a wireless signal. Implantable and wearable antennas are very susceptible to harmful performance degradation caused by the human body and very difficult to integrate, if not designed properly. A designer need to minimize unwanted radiation absorption by the human body to avoid potential health issues. Moreover, a wearable antenna will be inevitably exposed to user movements and has to deal with influences such as crumpling and bending. These deformations can cause degraded performance or a shifted frequency response, which might render the antenna less effective. The existing wearable and implantable antennas’ topologies and designs under discussion still suffer from many challenges such as unstable antenna behavior, low bandwidth, considerable power generation, less biocompatibility, and comparatively bigger size. The work presented in this thesis focused on two main aspects. Part one of the work presents the design, realization, and performance evaluation of two wearable antennas based on flexible and textile materials. In order to achieve high body-antenna isolation, hence, minimal coupling between human body and antenna and to achieve performance enhancement artificial magnetic conductor is integrated with the antenna. The proposed wearable antennas feature a small footprint and low profile characteristics and achieved a wider -10 dB input impedance bandwidth compared to wearable antennas reported in literature. In addition, using new materials in wearable antenna design such as flexible magneto-dielectric and dielectric/magnetic layered substrates is investigated. Effectiveness of using such materials revealed to achieve further improvements in antenna radiation characteristics and bandwidth and to stabilize antenna performance under bending and on body conditions compared to artificial magnetic conductor based antenna. The design of a wideband biocompatible implantable antenna is presented. The antenna features small size (i.e., the antenna size in planar form is 2.52 mm3), wide -10 dB input impedance bandwidth of 7.31 GHz, and low coupling to human tissues. In part two, an overview of investigations done for two wireless body area network applications is presented. The applications are: (a) respiratory rate measurement using ultra-wide band radar system and (b) an accurate phase-based localization method of radio frequency identification tag. The ultimate goal is to study how the antenna design can affect the overall system performance and define its limitations and capabilities. In the first studied application, results indicate that the proposed sensing system is less affected and shows less error when an antenna with directive radiation pattern, low cross-polarization, and stable phase center is used. In the second studied application, results indicate that effects of mutual coupling between the array elements on the phase values are negligible. Thus, the phase of the reflected waves from the tag is mainly determined by the distance between the tag and each antenna element, and is not affected by the induced currents on the other elements

Multiradar Data Fusion for Respiratory Measurement of Multiple People

This study proposes a data fusion method for multiradar systems to enable measurement of the respiration of multiple people located at arbitrary positions. Using the proposed method, the individual respiration rates of multiple people can be measured, even when echoes from some of these people cannot be received by one of the radar systems because of shadowing. In addition, the proposed method does not require information about the positions and orientations of the radar systems used because the method can estimate the layout of these radar systems by identifying multiple human targets that can be measured from different angles using multiple radar systems. When a single target person can be measured using multiple radar systems simultaneously, the proposed method selects an accurate signal from among the multiple signals based on the spectral characteristics. To verify the effectiveness of the proposed method, we performed experiments based on two scenarios with different layouts that involved seven participants and two radar systems. Through these experiments, the proposed method was demonstrated to be capable of measuring the respiration of all seven people by overcoming the shadowing issue. In the two scenarios, the average errors of the proposed method in estimating the respiration rates were 0.33 and 1.24 respirations per minute (rpm), respectively, thus demonstrating accurate and simultaneous respiratory measurements of multiple people using the multiradar system

Multiradar Data Fusion for Respiratory Measurement of Multiple People

This study proposes a data fusion method for multiradar systems to enable measurement of the respiration of multiple people located at arbitrary positions. Using the proposed method, the individual respiration rates of multiple people can be measured, even when echoes from some of these people cannot be received by one of the radar systems because of shadowing. In addition, the proposed method does not require information about the positions and orientations of the radar systems used because the method can estimate the layout of these radar systems by identifying multiple human targets that can be measured from different angles using multiple radar systems. When a single target person can be measured using multiple radar systems simultaneously, the proposed method selects an accurate signal from among the multiple signals based on the spectral characteristics. To verify the effectiveness of the proposed method, we performed experiments based on two scenarios with different layouts that involved seven participants and two radar systems. Through these experiments, the proposed method was demonstrated to be capable of measuring the respiration of all seven people by overcoming the shadowing issue. In the two scenarios, the average errors of the proposed method in estimating the respiration rates were 0.33 and 1.24 respirations per minute (rpm), respectively, thus demonstrating accurate and simultaneous respiratory measurements of multiple people using the multiradar system

Remote Human Vital Sign Monitoring Using Multiple-Input Multiple-Output Radar at Millimeter-Wave Frequencies

Non-contact respiration rate (RR) and heart rate (HR) monitoring using millimeter-wave (mmWave) radars has gained lots of attention for medical, civilian, and military applications. These mmWave radars are small, light, and portable which can be deployed to various places. To increase the accuracy of RR and HR detection, distributed multi-input multi-output (MIMO) radar can be used to acquire non-redundant information of vital sign signals from different perspectives because each MIMO channel has different fields of view with respect to the subject under test (SUT). This dissertation investigates the use of a Frequency Modulated Continuous Wave (FMCW) radar operating at 77-81 GHz for this application. Vital sign signal is first reconstructed with Arctangent Demodulation (AD) method using phase change’s information collected by the radar due to chest wall displacement from respiration and heartbeat activities. Since the heartbeat signals can be corrupted and concealed by the third/fourth harmonics of the respiratory signals as well as random body motion (RBM) from the SUT, we have developed an automatic Heartbeat Template (HBT) extraction method based on Constellation Diagrams of the received signals. The extraction method will automatically spot and extract signals’ portions that carry good amount of heartbeat signals which are not corrupted by the RBM. The extracted HBT is then used as an adapted wavelet for Continuous Wavelet Transform (CWT) to reduce interferences from respiratory harmonics and RBM, as well as magnify the heartbeat signals. As the nature of RBM is unpredictable, the extracted HBT may not completely cancel the interferences from RBM. Therefore, to provide better HR detection’s accuracy, we have also developed a spectral-based HR selection method to gather frequency spectra of heartbeat signals from different MIMO channels. Based on this gathered spectral information, we can determine an accurate HR even if the heartbeat signals are significantly concealed by the RBM. To further improve the detection’s accuracy of RR and HR, two deep learning (DL) frameworks are also investigated. First, a Convolutional Neural Network (CNN) has been proposed to optimally select clean MIMO channels and eliminate MIMO channels with low SNR of heartbeat signals. After that, a Multi-layer Perceptron (MLP) neural network (NN) is utilized to reconstruct the heartbeat signals that will be used to assess and select the final HR with high confidence

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