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1. Extracting Individual Respiratory Signatures from Combined Multi-Subject Mixtures with Varied Breathing Pattern Using Independent Component Analysis with the JADE Algorithm

   https://doi.org/10.1109/APMC47863.2020.9331715  

   Islam, Shekh M.; Lubecke, Victor M. (December 2020, 2020 IEEE Asia-Pacific Microwave Conference (APMC))

   null (Ed.)

   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 for 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. 

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2. An Adaptive Filter Technique for Platform Motion Compensation in Unmanned Aerial Vehicle Based Remote Life Sensing Radar

   https://doi.org/10.23919/EuMC48046.2021.9338011  

   Islam, Shekh M.; Lubecke, Lana C.; Grado, Christian; Lubecke, Victor M. (January 2021, 2020 50th European Microwave Conference (EuMC))

   null (Ed.)

   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. 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. 

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3. Respiratory Dynamics of Thoracic and Abdominal Motion in Doppler Radar Measurements

   https://doi.org/10.1109/RWS56914.2024.10438596  

   Sameera, Jannatun Noor; Lee, Alexander; Lubecke, Victor M; Boric-Lubecke, Olga (January 2024, IEEE)

   A Doppler radar measurement of respiration is a well-known technique for assessment of respiratory rates and patterns. Torso respiratory motion is a result of thoracic and abdominal motion during normal breathing. These two contributions produce breathing patterns that are important to understand for assessing respiratory health and sleep disorders. Doppler radar systems often use an antenna beam that illuminates the whole torso, effectively combining the contributions from the two regions. This paper presents theory, simulation, and measurement results that analyze and validate thorax and abdomen motion contributions in Doppler radar respiratory measurement. 

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4. Cardiopulmonary Effective Radar Cross Section (ERCS) for Orientation of Sedentary Subject Using Microwave Doppler Radar

   https://doi.org/10.1109/APMC47863.2020.9331705  

   Snigdha, Farjana; Ishmael, Khaldoon; Neville, Ryan; Boric-Lubecke, Olga (December 2020, 2020 IEEE Asia-Pacific Microwave Conference (APMC))

   null (Ed.)

   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. 

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5. Respiratory Feature Extraction for Radar-Based Continuous Identity Authentication

   https://doi.org/10.1109/RWS45077.2020.9050013  

   Islam, Shekh M; Sylvester, Abraham; Orpilla, George; Lubecke, Victor M. (January 2020, 2020 IEEE Radio and Wireless Symposium (RWS))

   null (Ed.)

   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 in 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. 

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