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  1. Unmanned Aerial Vehicles (UAVs) have demonstrated efficacy as a platform for remote life sensing in post-disaster search and rescue applications. Radar-assisted UAV respiration motion sensing technology also shows promise yet a significant technological challenge remains associated with interfering motion artefacts from the moving UAV platform. The feasibility of integrating an adaptive filter approach for the compensation of platform motion artefacts is investigated here for the extraction of respiratory motion signatures. A 24-GHz dual radar system was attached to a mechanical mover to emulating motion artefacts while measuring the motion of a robotic breathing phantom designed to reproduce breathing motion patterns.more »Recursive least square (RLS) and a least mean square (LMS) adaptive filter algorithms were employed to test efficacy for extracting respiratory rate from the motion corrupted breathing signal. Experimental results demonstrated that the RLS performed best with an accuracy of 98.24% for extracting the frequency of the robotic breathing phantom mover. The proposed system has several potential applications including military, humanitarian, and post-disaster search and rescue operations.« less
  2. Modern microwave radar technologies and systems are taking important roles in healthcare, security, and human–machine interface by remote sensing of human life activities. This paper first reviews the developments in the past decade on the sensing front-end, transponder tag, and leveraging of other wireless infrastructure such as Wi-Fi. Based on the state-of-the-art engineering technologies, several emerging applications will then be studied, including continuous authentication, behavior recognition, human-aware localization, occupancy sensing, blood pressure monitoring, and sleep medicine. As radio frequency spectrum becomes a scarce resource, the allocation and spectrum sharing of life activity sensing bandwidth with other wireless infrastructures will bemore »discussed. Several future research directions will be laid out to solve challenges for ubiquitous deployment of these sensing technologies at the human–microwave frontier.« less
  3. In-home sleep monitoring system using Microwave Doppler radar is gaining attention as it is unobtrusive and noncontact form of measurement. Most of the reported results in literature focused on utilizing radar-reflected signal amplitude to recognize Obstructive sleep apnea (OSA) events which requires iterative analysis and cannot recommend about sleep positions also (supine, prone and side). In this paper, we propose a new, robust and automated ERCS-based (Effective Radar Cross section) method for classifying OSA events (normal, apnea and hypopnea) by integrating radar system in a clinical setup. In our prior attempt, ERCS has been proven versatile method to recognize differentmore »sleep postures. We also employed two different machine learning classifiers (K-nearest neighbor (KNN) and Support Vector machine (SVM) to recognize OSA events from radar captured ERCS and breathing rate measurement from five different patients' clinical study. SVM with quadratic kernel outperformed with other classifiers with an accuracy of 96.7 % for recognizing different OSA events. The proposed system has several potential applications in healthcare, continuous monitoring and security/surveillance applications.« less
  4. While Doppler radar measurement of respiration has shown promise for various healthcare applications, simultaneous sensing of respiration for multiple subjects in the radar field of view remains a significant challenge as reflections from the subjects are received as an interference pattern. Prior research has demonstrated the basic feasibility of using phase comparison with a 24-GHz Monopulse radar for isolation of one subject when another subject was in view, by estimating each subject's angular location with 88% accuracy. The integration of the high-resolution Multiple Signal Classification (MUSIC) algorithm with a phase-comparison technique is proposed to achieve robust accuracy for practical multi-subjectmore »respiration monitoring. Experimental results for this work demonstrate that the MUSIC pseudo-spectrum can separate two subjects 1.5 meters apart from each other at a distance of 3 meters from the sensor, using the same antenna array elements, spacing, and experimental scenarios previously reported for phase comparison Monopulse alone. Experimental results demonstrate that the MUSIC algorithm outperforms the phase-comparison technique with an azimuth angular position estimation accuracy over 95%. Higher accuracy indicates the system has improved robustness concerning noise and interference.« less
  5. Unmanned Aerial Vehicles (UAVs) with onboard Doppler radar sensors can be used for health reconnaissance including the remote detection of respiratory patterns associated with COVID-19. While respiratory diagnostics have been demonstrated with radar, the motion of the airborne introduces motion interference. An adaptive filter method is applied here which uses a second radar facing a non-moving surface (ceiling) for a nose cancellation reference signal. Variations in respiratory rate and displacement have been demonstrated which is consistent with the need for detecting tachypnea associated with COVID-19.
  6. Effective radar cross-section (ERCS) for microwave Doppler radar, is defined by the reflected power from sections of the human body that undergo physiological motion. This paper investigates ERCS for human cardiopulmonary motion of sedentary subjects at three different positions (front, back and side with respect to radar). While human breathing and heartbeat can be measured from all four sides of the body, the characteristics of measured signals will vary with body orientation. Thus, continuous wave radar with quadrature architecture at 2. 4GHz was used to test a sedentary subject for three minutes from three different orientations: front, back and sidemore »with respect to radar. The results obtained from the tests showed that physiological motion could be obtained and that distinct patterns emerge due to the differences in the ERCS for each orientation. For the seated subject, back ERCS was higher than for front and side positions. Determining ERCS changes with position may enable determining body orientation with respect to the radar. This research opens further opportunities for development of high-resolution occupancy sensing and emergency search and rescue sensing, where the orientation of a human subject may be unknown ahead of time.« less
  7. Concurrent respiration monitoring of multiple subjects remains a challenge in microwave Doppler radar-based non-contact physiological sensing technology. Prior research using Independent component analysis with the JADE algorithm has been limited to the separation of respiratory signatures for normal breathing patterns. This paper investigates the feasibility of using the ICA-JADE algorithm with a 24-GHz phase comparison monopulse radar transceiver for separating respiratory signatures from combined mixtures of varied breathing patterns. Normal, fast, and slow breathing pattern variations likely to occur due to physiological activity, and emotional stress were used as a basis for assessing separation robustness. Experimental results showed efficacy formore »recognition of three different breathing patterns, and isolation of respiratory signatures with an accuracy of100% for normal breathing, 92% for slow breathing, and 83.78% for fast breathing using ICA-JADE. Breathing pattern variations were observed to affect the signal-to-noise ratio through multiple mechanisms, decreasing with an increase in the number of breathing cycles and associated motion artifacts. Additionally, for removing motion artifacts of fast breathing pattern empirical mode decomposition (EMD) is employed, and for slow breathing pattern, increasing the breathing cycles helps to achieve an accuracy of 89.2% and 94.5% respectively.« less
  8. Non-contact vital signs monitoring using microwave Doppler radar has shown great promise in healthcare applications. Recently, this unobtrusive form of physiological sensing has also been gaining attention for its potential for continuous identity authentication, which can reduce the vulnerability of traditional one-pass validation authentication systems. Physiological Doppler radar is an attractive approach for continuous identity authentication as it requires neither contact nor line-of-sight and does not give rise to privacy concerns associated with video imaging. This paper presents a review of recent advances in radar-based identity authentication systems. It includes an evaluation of the applicability of different research efforts inmore »authentication using respiratory patterns and heart-based dynamics. It also identifies aspects of future research required to address remaining challenges in applying unobtrusive respiration-based or heart-based identity authentication to practical systems. With the advancement of machine learning and artificial intelligence, radar-based continuous authentication can grow to serve a wide range of valuable functions in society.« less
  9. Radar is an attractive approach for identity authentication because it requires no contact and is unobtrusive. Most reported results have focused only on sedentary breathing patterns, without considering how respiratory patterns may change due to physiological activities or emotional stress. In this research the feasibility of extracting identifying features from radar respiratory traces was tested, for sedentary subject conditions as well as just after performing physiological activities (walking upstairs). Respiratory breathing dynamics related features (breathing rate, spectral entropy, breathing depth, inhale/exhale area ratio, mean and standard deviation of the peaks) were extracted from radar captured respiration patterns, and variations inmore »feature parameters after physiological activities were assessed. Experimental results demonstrated that, after short exertions dynamically segmented respiratory pattern exhale area and breathing depth increased by more than 1.4 times for all participants, which made evident the uniqueness of residual heart volume after expiration for recognizing each subject even after short exertions. Our proposed approach is also integrated with a Support Vector Machine (SVM) with a radial basis function kernel to demonstrate an identification success rate of almost 98.55% for sedentary-only conditions and almost 92% for a combined mixture of conditions (sedentary and after short exertion). While the efficacy was reduced, the method still shows significant potential. The proposed identity authentication approach has several potential applications including security/surveillance, IOT applications, virtual reality and health monitoring as well.« less