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  1. Identity authentication based on Doppler radar respiration sensing is gaining attention as it requires neither contact nor line of sight and does not give rise to privacy concerns associated with video imaging. Prior research demonstrating the recognition of individuals has been limited to isolated single subject scenarios. When two equidistant subjects are present, identification is more challenging due to the interference of respiration motion patterns in the reflected radar signal. In this research, respiratory signature separation techniques are functionally combined with machine learning (ML) classifiers for reliable subject identity authentication. An improved version of the dynamic segmentation algorithm (peak search and triangulation) was proposed, which can extract distinguishable airflow profile-related features (exhale area, inhale area, inhale/exhale speed, and breathing depth) for medium-scale experiments of 20 different participants to examine the feasibility of extraction of an individual’s respiratory features from a combined mixture of motions for subjects. Independent component analysis with the joint approximation of diagonalization of eigenmatrices (ICA-JADE) algorithm was employed to isolate individual respiratory signatures from combined mixtures of breathing patterns. The extracted hyperfeature sets were then evaluated by integrating two different popular ML classifiers, k-nearest neighbor (KNN) and support vector machine (SVM), for subject authentication. Accuracies of 97.5%more »for two-subject experiments and 98.33% for single-subject experiments were achieved, which supersedes the performance of prior reported methods. The proposed identity authentication approach has several potential applications, including security/surveillance, the Internet-of- Things (IoT) applications, virtual reality, and health monitoring.« less
    Free, publicly-accessible full text available August 22, 2023
  2. A number of algorithms have been developed to extract heart rate from physiological motion data using Doppler radar system. Yet, it is very challenging to eliminate noise associated with surroundings, especially with a single-channel Doppler radar system. However, single-channel Doppler radars provide the advantage of operating at lower power. Additionally, heart rate extraction using single-channel Doppler radar has remained somewhat unexplored. This has motivated the development of effective signal processing algorithms for signals received from single-channel Doppler radars. Three algorithms have been studied for estimating heart rate. The first algorithm is based on applying FFT on an FIR filtered signal. In the second algorithm, autocorrelation was performed on the filtered data. Thirdly, a peak finding algorithm was used in conjunction with a moving average preceded by a clipper to determine the heart rate. The results obtained were compared with heart rate readings from a pulse oximeter. With a mean difference of 2.6 bpm, the heart rate from Doppler radar matched that from the pulse oximeter most frequently when the peak finding algorithm was used. The results obtained using autocorrelation and peak finding algorithm (with standard deviations of 2.6 bpm and 4.0 bpm) suggest that a single channel Doppler radar systemmore »can be a viable alternative to contact heart rate monitors in patients for whom contact measurements are not feasible.« less
    Free, publicly-accessible full text available July 11, 2023
  3. Free, publicly-accessible full text available July 1, 2023
  4. Abstract

    Human-Building Interaction (HBI) is a convergent field that represents the growing complexities of the dynamic interplay between human experience and intelligence within built environments. This paper provides core definitions, research dimensions, and an overall vision for the future of HBI as developed through consensus among 25 interdisciplinary experts in a series of facilitated workshops. Three primary areas contribute to and require attention in HBI research: humans (human experiences, performance, and well-being), buildings (building design and operations), and technologies (sensing, inference, and awareness). Three critical interdisciplinary research domains intersect these areas: control systems and decision making, trust and collaboration, and modeling and simulation. Finally, at the core, it is vital for HBI research to center on and support equity, privacy, and sustainability. Compelling research questions are posed for each primary area, research domain, and core principle. State-of-the-art methods used in HBI studies are discussed, and examples of original research are offered to illustrate opportunities for the advancement of HBI research.

  5. Measurement of the body's displacement at multiple positions allows heart pulse wave propagation to be observed; this is an important step toward noncontact blood pressure measurement. This study investigates the feasibility of performing blood pressure measurements using skin displacement waveforms measured at two positions on a human body. To evaluate the accuracy of the proposed approach, this study uses a pair of laser displacement sensors to enable precise pulse transit time measurement. By comparing the displacement waveforms from the two sensors, the relationship between pulse transit time and blood pressure was evaluated. It is demonstrated experimentally that the blood pressure can be estimated with accuracy of 5.1 mmHg, which is equivalent to the error of an ordinary cuff-type blood pressure monitor.
    Free, publicly-accessible full text available June 19, 2023
  6. The increasingly sophisticated at-home screening systems for obstructive sleep apnea (OSA), integrated with both contactless and contact-based sensing modalities, bring convenience and reliability to remote chronic disease management. However, the device pairing processes between system components are vulnerable to wireless exploitation from a noncompliant user wishing to manipulate the test results. This work presents SIENNA, an insider-resistant context-based pairing protocol. SIENNA leverages JADE-ICA to uniquely identify a user’s respiration pattern within a multi-person environment and fuzzy commitment for automatic device pairing, while using friendly jamming technique to prevent an insider with knowledge of respiration patterns from acquiring the pairing key. Our analysis and test results show that SIENNA can achieve reliable (> 90% success rate) device pairing under a noisy environment and is robust against the attacker with full knowledge of the context information.
  7. 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 side 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.
  8. 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 different 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.
  9. One deadly aspect of COVID-19 is that those infected can often be contagious before exhibiting overt symptoms. While methods such as temperature checks and sinus swabs have aided with early detection, the former does not always provide a reliable indicator of COVID-19, and the latter is invasive and requires significant human and material resources to administer. This paper presents a non-invasive COVID-19 early screening system implementable with commercial off-the-shelf wireless communications devices. The system leverages the Doppler radar principle to monitor respiratory-related chest motion and identifies breathing rates that indicate COVID-19 infection. A prototype was developed from software-defined radios (SDRs) designed for 5G NR wireless communications and system performance was evaluated using a robotic mover simulating human breathing, and using actual breathing, resulting in a consistent respiratory rate accuracy better than one breath per minute, exceeding that used in common medical practice.Clinical Relevance-This establishes the potential efficacy of wireless communications based radar for recognizing respiratory disorders such as COVID-19.