Search

Human eye activity has been widely studied in many fields such as psychology, neuroscience, medicine, and human-computer interaction engineering. In previous studies, monitoring of human eye activity mainly depends on electrooculogram (EOG) that requires a contact sensor. This article proposes a novel eye movement monitoring method called continuous wave doppler oculogram (cDOG). Unlike the conventional EOG-based eye movement monitoring methods, cDOG based on continuous wave doppler radar sensor (cDRS) can remotely measure human eye activity without placing electrodes on the head. To verify the feasibility of using cDOG for eye movement monitoring, we first theoretically analyzed the association between the radar signal and the corresponding eye movements measured with EOG. Afterward, we conducted an experiment to compare EOG and cDOG measurements under the conditions of eyes closure and opening. In addition, different eye movement states were considered, including right-left saccade, up-down saccade, eye-blink, and fixation. Several representative time domain and frequency domain features obtained from cDOG and from EOG were compared in these states, allowing us to demonstrate the feasibility of using cDOG for monitoring eye movements. The experimental results show that there is a correlation between cDOG and EOG in the time and frequency domain features, the average time error of single eye movement is less than 280.5 ms, and the accuracy of cDOG in eye movement detection is higher than 92.35%, when the distance between the cDRS and the face is 10 cm and eyes is facing the radar directly.

Low-cost and portable electromagnetic (EM) stroke diagnostic systems are of great interest due to the increasing demand for early on-site detection or long-term bedside monitoring of stroke patients. Biosensor antennas serve as crucial hardware components for EM diagnostic systems. This article presents a detect capability enhanced biosensor antenna with a planar and compact configuration for portable EM stroke detection systems, overcoming the problem of limited detection capability in existing designs for this application. The proposed antenna is developed based on multiple dipoles, exhibiting multi-mode resonances and complementary interaction. In the frequency domain, the simulated and measured results with the presence of head phantoms show that this compact planar antenna achieves improved performance in both impedance bandwidth and near-field radiation inside the head tissues, which all contribute to enhancing its stroke detection capability in radar-based EM diagnosis. An array of 12 elements is numerically and experimentally tested in a lab-setting EM stroke diagnostic system to validate the detection capability of the proposed antenna. The reconstructed 2-D images inside the head demonstrate successful detection of different stroke-affected areas, even as small as 3 mm in radius, significantly smaller than those of reported relevant works under the same validation setting, confirming the enhanced detection capability of the proposed antenna.

The use of radar technology for contactless monitoring of cardiorespiratory activity has been a significant research topic for the last two decades. However, despite the abundant literature focusing on the use of different radar architectures for healthcare applications, an in-depth analysis is missing about the most appropriate configuration. This article presents a comparison between continuous-wave (CW) and linear-frequency-modulated continuous-wave (LFMCW) radars for application in vital sign monitoring scenarios. These waveforms are generated with the same architecture at two different frequencies: 24 and 134 GHz. Results evidence that both configurations are capable of measuring general metrics, such as the breathing and heart rates. However, LFMCW offers better results in the identification of cardiac events and the extraction of certain derived biomarkers, such as the heart rate variability sequences (HRV). Conclusions show that this performance does not depend on the selected working frequency.

This article presents and experimentally evaluates a frequency error elimination technique suitable for unsynchronized bistatic Multiple-Input Multiple-Output (MIMO) radar for human-body detection. First, a mathematical expression of human-body localization using bistatic MIMO radar is presented. Then the direct path is used to eliminate the phase error created by the frequency difference between the transmitter and receiver. A new Doppler-shifted component of the MIMO channel without phase error is derived, and the locations of the multiple targets are calculated by the 2-dimensional MUltiple SIgnal Classification (MUSIC) method. Next, the results of simulations that examine frequency error versus power ratios are discussed to illustrate the effectiveness of the proposed method. An experiment is carried out in an indoor multipath-rich environment. To emulate the unsynchronized condition, the transmitter and receiver use independent Signal Generators (SGs). One to six targets are tested. The experiments demonstrate that our unsynchronized radar system can identify the locations of multiple targets with high accuracy.

A respiratory disorder that attacks COVID-19 patients requires intensive supervision of medical practitioners during the isolation period. A non-contact monitoring device will be a suitable solution for reducing the spread risk of the virus while monitoring the COVID-19 patient. This study uses Frequency-Modulated Continuous Wave (FMCW) radar and Machine Learning (ML) to obtain respiratory information and analyze respiratory signals, respectively. Multiple subjects in a room can be detected simultaneously by calculating the Angle of Arrival (AoA) of the received signal and utilizing the Multiple Input Multiple Output (MIMO) of FMCW radar. Fast Fourier Transform (FFT) and some signal processing are implemented to obtain a breathing waveform. ML helps the system to analyze the respiratory signals automatically. This paper also compares the performance of several ML algorithms such as Multinomial Logistic Regression (MLR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), CatBoosting (CB) Classifier, Multilayer Perceptron (MLP), and three proposed stacked ensemble models, namely Stacked Ensemble Classifier (SEC), Boosting Tree-based Stacked Classifier (BTSC), and Neural Stacked Ensemble Model (NSEM) to obtain the best ML model. The results show that the NSEM algorithm achieves the best performance with 97.1% accuracy. In the real-time implementation, the system could simultaneously detect several objects with different breathing characteristics and classify the respiratory signals into five different classes.

During the global epidemic, non-contact methods for monitoring the vital signs of several people have become particularly important. Advanced signal processing techniques have recently been demonstrated to separate and track the vital signs of multiple people. In this paper, we further develop the multi-person vital signs identification (VSign-ID) system to make non-contact detection available in public places. VSign-ID not only extracts multi-person vital signs but also states from whom these vital signs are collected. We utilize multiple doppler radars to expand the effective range of the measurement area and propose a space and time matching mechanism for vital signs identification. We use a thermal camera to detect the number of people and their movements. VSign-ID efficiently coordinates these two types of sensors (i.e., the doppler radars and the thermal camera) to track and identify the respiration rates and heartbeat rates of multiple people. A series of experiments and simulations are conducted to measure the efficiency of VSign-ID. In the case of five people sitting closely, the estimation errors for respiration and heartbeat rates are -4.85 dB and -2.36 dB lower than the standard resolution of the system, respectively, despite using only two independent radars.

Empowered by the rapid advancements of semiconductor techniques, emerging areas such as industry 4.0, precise healthcare, pervasive communications, intelligent robots, and smart buildings are to be realized, which put substantial demands on low-power and high-performance cognitive edge sensors. Capabilities of precise sensing and seamless interactions with human subjects are pivotal to boosting versatile Internet of Everything (IoE) applications. However, it is challenging to attain various kinds of intuitive sensing based on one edge sensor. A novel silicon-based phased-array coherent radar sensing platform is proposed to attain versatile application-driven capabilities by focusing the wideband radar beams accurately at the target's direction to attain precise sensing. The coherent radar platform can support a maximum 60° field-of-view sensing range with smaller than 2° optimum steering step resolution and -70-dBm sensitivity. The silicon-based mixed-signal coherent radar chip platform is fabricated by a 65-nm CMOS process and owns the convenience for massive deployments at the edge. A series of experiments were conducted to validate the integrated radar platform's adaptable capabilities and salient performances on human subject detection, vital signs monitoring, and motion recognition. Notably, the adaptable multifunction integrated radar platform opens up the enticing possibility for next-generation monolithic edge devices supporting seamless health sensing and cognitive interactive functions with human subjects.

Vital sign detection using linear frequency-modulated continuous-wave (LFMCW) radar may be subject to the proximity stationary clutters. This paper presents a novel technique to synthesize the slow-time I/Q signals, which are equivalent to those in a single tone quadrature CW radar, from a single-channel LFMCW radar. It correlates the two types of radars in such a way that the proximity stationary clutters are translated to direct current (DC) offsets in the synthesized I/Q signals across slow-time. The circle-fitting based DC offsets calibration (DCcal) technique, which was developed for CW radar, can now be applied to eliminate the impact of the proximity stationary clutters in LFMCW radars for accurate vital sign detection. Moreover, the modified differentiate and cross-multiply (MDACM) algorithm can also be leveraged to eliminate the phase ambiguity issue. Thorough theoretical analysis and working principles are presented. Simulations are performed to validate the proposed technique. Moreover, exhaustive experiments are carried out with a millimeter-wave 79 GHz FMCW radar in the office environment. Mechanical vibration and vital signs are extracted with micrometer-level accuracy in the existence of proximity stationary clutters.

For precise health status monitoring and accurate disease diagnostics in the current COVID-19 pandemic, it is essential to detect various kinds of target signals robustly under high noise and strong interferences. Moreover, the health monitoring system is preferred to be realized in a small form factor for convenient mass deployments. A CMOS-integrated coherent sensing platform is proposed to achieve the goal, which synergetically leverages quadrature coherent photoacoustic (PA) detection and coherent radar sensing for achieving universal healthcare. By utilizing configurable mixed-signal quadrature coherent PA detection, high sensitivity and enhanced specificity can be achieved. In-phase (I) and quadrature (Q) templates are specifically designed to accurately sense and precisely reconstruct the target PA signals in a coherent mode. By mixed-signal implementation leveraging an FPGA to generate template waveforms adaptively, accurate tracking and precise reconstruction on the target PA signal can be attained based on the early-late tracking principle. The multiplication between the received PA signal and the templates is implemented efficiently in analog-domain by the Gilbert cell on-chip. In vivo blood temperature monitoring was realized based on the integrated PA sensing platform fabricated in a 65-nm CMOS process. With an integrated radar sensor deployed in the indoor scenario, noncontact monitoring on respiration and heartbeat rates can be attained based on electromagnetic (EM) sensing. By complementary usage of PA-EM sensing mechanisms, comprehensive health status monitoring and precise remote disease diagnostics can be achieved for the currentglobal COVID-19 pandemic and the future pervasive healthcare in the Internet of Everything (IoE) era.

A novel phase-canceling demodulation scheme to improve the linearity of a self-injection-locked (SIL) ultrasonic radar is proposed with the goal of solving the null detection problem and accurately sensing large displacements of a moving target. A proportional-integral (PI) controller regulates the phase of the injection signal and cancels the Doppler phase shift by tuning a delay in the received echo signal, and this tunable delay serves as the radar output, which is linearly proportional to the displacement of the target. Without assuming weak injection, the frequency and phase equations for an SIL oscillator are derived, supporting the construction of a plant model and the design of a PI controller. Also, a new ultrasonic radar equation is presented for estimating the radar detection range. The SIL radar with phase regulation is operated in its anti-phase injection mode for better performance. The proposed design is implemented on an FPGA to make a 40 kHz continuous-wave ultrasonic radar. The maximum detectable peak-to-peak motion is up to 120 mm (approximately 14 wavelengths of displacement), with a total harmonic distortion as low as 2.3% for the detection of 1 Hz harmonic motion. The radar is used to detect the human chest movement for non-contact monitoring of the respiratory rate and heart rate. Due to the high linearity and sensitivity, the radar is capable of faithfully detecting the relatively large involuntary body movements and lung movements while still preserving the weak heartbeat rhythm buried in them, with the average error of measured heart rates less than 1 BPM.

Wearable monitors for measuring vital signs such as blood pressure will greatly impact the medical field. This work presents a millimeter-wave, radar-based system for performing accurate measurements of arterial pulse waveforms without contacting the region that is pulsing. Electromagnetic and radar-system simulation models are utilized to demonstrate the viability and safety of this approach. This is followed by hardware/software implementation and a study on 12 human subjects. Measured radial arterial waveforms exhibit signal strengths that are well above the noise floor of the system and a morphology that would be expected in an arterial pulse. Finally, comparison of the radar-based signals with a reference tonometer indicates a strong correlation between waveforms, as well as similar spectral signatures. The results observed suggest a millimeter-wave based approach for arterial pulse detection is very promising for future applications in pulse wave analysis and pulse transit time measurement for blood pressure tracking.

Coherent ultra-wideband (UWB) radar-on-chip technology shows great promise for developing portable and low-cost medical imaging and monitoring devices. Particularly monitoring the mechanical functioning of the cardiovascular system is of interest, due to the ability of radar systems to track sub-mm motion inside the body at a high speed. For imaging applications, UWB radar systems are required, but there are still significant challenges with in-body sensing using low-power microwave equipment and wideband signals. Recently, it was shown for the first time, on a single subject, that the arterial pulse wave can be measured at various locations in the body, using a coherent UWB radar-on-chip technology. This paper provides more substantial evidence, in the form of new measurements and improved methods, to demonstrate that cardiovascular dynamics can be measured using radar-on-chip. Results across four participants were found to be robust and repeatable. Cardiovascular signals were recorded using radar-on-chip systems and electrocardiography (ECG). Through ECG-aligned averaging, the arterial pulse wave could be measured at a number of locations in the body. Pulse arrival time could be determined with high precision, and blood pressure pulse wave propagation through different arteries was demonstrated. In addition, cardiac dynamics were measured from the chest. This paper serves as a first step in developing a portable and low-cost device for long-term monitoring of the cardiovascular system and provides the fundamentals necessary for developing UWB radar-on-chip imaging systems.

A continuous wave Doppler radar, operating as a phase-locked-loop in phase demodulator configuration, is proposed and in vivo demonstrated for noncontact vital signs monitoring. The radar architecture exhibits a unique precision in tracking the phase modulation caused by human cardiopulmonary activity from which heartbeat and respiration can simultaneously be extracted. The single mixer architecture is immune to the null point and does not require small-angle approximation conditions, which distinguishes it from pre-existing other approaches. This enables the proposed radar to behave highly linear, with very precise detection of phase modulations induced by any kind of movement, independently from amplitude and speed. After simulations and technical tests to validate functionality and safety of the proposed architecture, a practical setup was demonstrated on human volunteers. Wavelet independent component analysis was applied to successfully retrieve respiratory and heart rate information from the radar baseband signal.

Radar devices can be used in nonintrusive situations to monitor vital sign, through clothes or behind walls. By detecting and extracting body motion linked to physiological activity, accurate simultaneous estimations of both heart rate (HR) and respiration rate (RR) is possible. However, most research to date has focused on front monitoring of superficial motion of the chest. In this paper, body penetration of electromagnetic (EM) wave is investigated to perform back monitoring of human subjects. Using body-coupled antennas and an ultra-wideband (UWB) pulsed radar, in-body monitoring of lungs and heart motion was achieved. An optimised location of measurement in the back of a subject is presented, to enhance signal-to-noise ratio and limit attenuation of reflected radar signals. Phase-based detection techniques are then investigated for back measurements of vital sign, in conjunction with frequency estimation methods that reduce the impact of parasite signals. Finally, an algorithm combining these techniques is presented to allow robust and real-time estimation of both HR and RR. Static and dynamic tests were conducted, and demonstrated the possibility of using this sensor in future health monitoring systems, especially in the form of a smart car seat for driver monitoring.

A Doppler radar operating as a Phase-Locked-Loop (PLL) in frequency demodulator configuration is presented and discussed. The proposed radar presents a unique architecture, using a single channel mixer, and allows to detect contactless vital signs parameters while solving the null point issue and without requiring the small angle approximation condition. Spectral analysis, simulations, and experimental results are presented and detailed to demonstrate the feasibility and the operational principle of the proposed radar architecture.

Quality of sleep is an important indicator of health and well being. Recent developments in the field of in-home sleep monitoring have the potential to enhance a person's sleeping experience and contribute to an overall sense of well being. Existing in-home sleep monitoring devices either fail to provide adequate sleep information or are obtrusive to use. To overcome these obstacles, a noncontact and cost-effective sleep monitoring system, named SleepSense, is proposed for continuous recognition of the sleep status, including on-bed movement, bed exit, and breathing section. SleepSense consists of three parts: a Doppler radar-based sensor, a robust automated radar demodulation module, and a sleep status recognition framework. Herein, several time-domain and frequency-domain features are extracted for the sleep recognition framework. A prototype of SleepSense is presented and evaluated using two sets of experiments. In the short-term controlled experiment, the SleepSense achieves an overall 95.1% accuracy rate in identifying various sleep status. In the 75-minute sleep study, SleepSense demonstrates wide usability in real life. The error rate for breathing rate extraction in this study is only 6.65%. These experimental results indicate that SleepSense is an effective and promising solution for in-home sleep monitoring.

This paper presents an ultra-wideband (UWB) impulse-radio radar signal processing platform used to analyze human respiratory features. Conventional radar systems used in human detection only analyze human respiration rates or the response of a target. However, additional respiratory signal information is available that has not been explored using radar detection. The authors previously proposed a modified raised cosine waveform (MRCW) respiration model and an iterative correlation search algorithm that could acquire additional respiratory features such as the inspiration and expiration speeds, respiration intensity, and respiration holding ratio. To realize real-time respiratory feature extraction by using the proposed UWB signal processing platform, this paper proposes a new four-segment linear waveform (FSLW) respiration model. This model offers a superior fit to the measured respiration signal compared with the MRCW model and decreases the computational complexity of feature extraction. In addition, an early-terminated iterative correlation search algorithm is presented, substantially decreasing the computational complexity and yielding negligible performance degradation. These extracted features can be considered the compressed signals used to decrease the amount of data storage required for use in long-term medical monitoring systems and can also be used in clinical diagnosis. The proposed respiratory feature extraction algorithm was designed and implemented using the proposed UWB radar signal processing platform including a radar front-end chip and an FPGA chip. The proposed radar system can detect human respiration rates at 0.1 to 1 Hz and facilitates the real-time analysis of the respiratory features of each respiration period.