Different antenna designs for non-contact vital signs measurement: a review

Cardiopulmonary activity measured through contactless means is a hot topic within the research community. The Doppler radar is an approach often used to acquire vital signs in real time and to further estimate their rates, in a remote way and without requiring direct contact with subjects. Many solutions have been proposed in the literature, using different transceivers and operation modes. Nonetheless, all different strategies have a common goal: enhance the system efficiency, reduce the manufacturing cost, and minimize the overall size of the system. Antennas are a key component for these systems since they can influence the radar robustness directly. Therefore, antennas must be designed with care, facing several trade-offs to meet all the system requirements. In this sense, it is necessary to define the proper guidelines that need to be followed in the antenna design. In this manuscript, an extensive review on different antenna designs for non-contact vital signals measurements is presented. It is intended to point out and quantify which parameters are crucial for the optimal radar operation, for non-contact vital signs' acquisition.info:eu-repo/semantics/publishedVersio

UWB radar for non-contact heart rate variability monitoring and mental state classification.

Heart rate variability (HRV), as measured by ultra-wideband (UWB) radar, enables contactless monitoring of physiological functioning in the human body. In the current study, we verified the reliability of HRV extraction from radar data, under limited transmitter power. In addition, we conducted a feasibility study of mental state classification from HRV data, measured using radar. Specifically, arctangent demodulation with calibration and low rank approximation have been used for radar signal pre-processing. An adaptive continuous wavelet filter and moving average filter were utilized for HRV extraction. For the mental state classification task, performance of support vector machine, k-nearest neighbors and random forest classifiers have been compared. The developed system has been validated on human participants, with 10 participants for HRV extraction, and three participants for the proof-of-concept mental state classification study. The results of HRV extraction demonstrate the reliability of time-domain parameter extraction from radar data. However, frequency-domain HRV parameters proved to be unreliable under low SNR. The best average overall mental state classification accuracy achieved was 82.34%, which has important implications for the feasibility of mental health monitoring using UWB radar

Feasibility Study and Design of a Wearable System-on-a-Chip Pulse Radar for Contactless Cardiopulmonary Monitoring

A new system-on-a-chip radar sensor for next-generation wearable wireless interface applied to the human health care and safeguard is presented. The system overview is provided and the feasibility study of the radar sensor is presented. In detail, the overall system consists of a radar sensor for detecting the heart and breath rates and a low-power IEEE 802.15.4 ZigBee radio interface, which provides a wireless data link with remote data acquisition and control units. In particular, the pulse radar exploits 3.1–10.6 GHz ultra-wideband signals which allow a significant reduction of the transceiver complexity and then of its power consumption. The operating principle of the radar for the cardiopulmonary monitoring is highlighted and the results of the system analysis are reported. Moreover, the results obtained from the building-blocks design, the channel measurement, and the ultra-wideband antenna realization are reported

Doppler radar-based non-contact health monitoring for obstructive sleep apnea diagnosis: A comprehensive review

Today’s rapid growth of elderly populations and aging problems coupled with the prevalence of obstructive sleep apnea (OSA) and other health related issues have affected many aspects of society. This has led to high demands for a more robust healthcare monitoring, diagnosing and treatments facilities. In particular to Sleep Medicine, sleep has a key role to play in both physical and mental health. The quality and duration of sleep have a direct and significant impact on people’s learning, memory, metabolism, weight, safety, mood, cardio-vascular health, diseases, and immune system function. The gold-standard for OSA diagnosis is the overnight sleep monitoring system using polysomnography (PSG). However, despite the quality and reliability of the PSG system, it is not well suited for long-term continuous usage due to limited mobility as well as causing possible irritation, distress, and discomfort to patients during the monitoring process. These limitations have led to stronger demands for non-contact sleep monitoring systems. The aim of this paper is to provide a comprehensive review of the current state of non-contact Doppler radar sleep monitoring technology and provide an outline of current challenges and make recommendations on future research directions to practically realize and commercialize the technology for everyday usage

Unobtrusive cot side sleep stage classification in preterm infants using ultra-wideband radar

Background: Sleep is an important driver of development in infants born preterm. However, continuous unobtrusive sleep monitoring of infants in the neonatal intensive care unit (NICU) is challenging.Objective: To assess the feasibility of ultra-wideband (UWB) radar for sleep stage classification in preterm infants admitted to the NICU.Methods: Active and quiet sleep were visually assessed using video recordings in 10 preterm infants (recorded between 29 and 34 weeks of postmenstrual age) admitted to the NICU. UWB radar recorded all infant's motions during the video recordings. From the baseband data measured with the UWB radar, a total of 48 features were calculated. All features were related to body and breathing movements. Six machine learning classifiers were compared regarding their ability to reliably classify active and quiet sleep using these raw signals.Results: The adaptive boosting (AdaBoost) classifier achieved the highest balanced accuracy (81%) over a 10-fold cross-validation, with an area under the curve of receiver operating characteristics (AUC-ROC) of 0.82.Conclusions: The UWB radar data, using the AdaBoost classifier, is a promising method for non-obtrusive sleep stage assessment in very preterm infants admitted to the NICU

A CNN based Multifaceted Signal Processing Framework for Heart Rate Proctoring Using Millimeter Wave Radar Ballistocardiography

The recent pandemic has refocused the medical world's attention on the diagnostic techniques associated with cardiovascular disease. Heart rate provides a real-time snapshot of cardiovascular health. A more precise heart rate reading provides a better understanding of cardiac muscle activity. Although many existing diagnostic techniques are approaching the limits of perfection, there remains potential for further development. In this paper, we propose MIBINET, a convolutional neural network for real-time proctoring of heart rate via inter-beat-interval (IBI) from millimeter wave (mm-wave) radar ballistocardiography signals. This network can be used in hospitals, homes, and passenger vehicles due to its lightweight and contactless properties. It employs classical signal processing prior to fitting the data into the network. Although MIBINET is primarily designed to work on mm-wave signals, it is found equally effective on signals of various modalities such as PCG, ECG, and PPG. Extensive experimental results and a thorough comparison with the current state-of-the-art on mm-wave signals demonstrate the viability and versatility of the proposed methodology. Keywords: Cardiovascular disease, contactless measurement, heart rate, IBI, mm-wave radar, neural networkComment: 13 pages, 10 figures, Submitted to Elsevier's Array Journa

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

Non-Contact Human Motion Sensing Using Radar Techniques

Human motion analysis has recently gained a lot of interest in the research community due to its widespread applications. A full understanding of normal motion from human limb joint trajectory tracking could be essential to develop and establish a scientific basis for correcting any abnormalities. Technology to analyze human motion has significantly advanced in the last few years. However, there is a need to develop a non-invasive, cost effective gait analysis system that can be functional indoors or outdoors 24/7 without hindering the normal daily activities for the subjects being monitored or invading their privacy. Out of the various methods for human gait analysis, radar technique is a non-invasive method, and can be carried out remotely. For one subject monitoring, single tone radars can be utilized for motion capturing of a single target, while ultra-wideband radars can be used for multi-subject tracking. But there are still some challenges that need to be overcome for utilizing radars for motion analysis, such as sophisticated signal processing requirements, sensitivity to noise, and hardware imperfections. The goal of this research is to overcome these challenges and realize a non-contact gait analysis system capable of extracting different organ trajectories (like the torso, hands and legs) from a complex human motion such as walking. The implemented system can be hugely beneficial for applications such as treating patients with joint problems, athlete performance analysis, motion classification, and so on

Portable UWB RADAR Sensing System for Transforming Subtle Chest Movement into Actionable Micro-Doppler Signatures to Extract Respiratory Rate Exploiting ResNet Algorithm

Contactless or non-invasive technology for the monitoring of anomalies in an inconspicuous and distant environment has immense significance in health-related applications, in particular COVID-19 symptoms detection, diagnosis, and monitoring. Contactless methods are crucial specifically during the COVID-19 epidemic as they require the least amount of involvement from infected individuals as well as healthcare personnel. According to recent medical research studies regarding coronavirus, individuals infected with novel COVID-19-Delta variant undergo elevated respiratory rates due to extensive infection in the lungs. This appalling situation demands constant real-time monitoring of respiratory patterns, which can help in avoiding any pernicious circumstances. In this paper, an Ultra-Wideband RADAR sensor “XeThru X4M200” is exploited to capture vital respiratory patterns. In the low and high frequency band, X4M200 operates within the 6.0-8.5 GHz and 7.25-10.20 GHz band, respectively. The experimentation is conducted on six distinct individuals to replicate a realistic scenario of irregular respiratory rates. The data is obtained in the form of spectrograms by carrying out normal (eupnea) and abnormal (tachypnea) respiratory. The collected spectrogram data is trained, validated, and tested using a cutting-edge deep learning technique called Residual Neural Network or ResNet. The trained ResNet model’s performance is assessed using the confusion matrix, precision, recall, F1-score, and classification accuracy. The unordinary skip connection process of the deep ResNet algorithm significantly reduces the underfitting and overfitting problem, resulting in a classification accuracy rate of up to 90%