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1. UAV Radar Sensing of Respiratory Variations for COVID-Type Disorders

   https://doi.org/10.1109/APMC47863.2020.9331613  

   Islam, Shekh M.; Grado, Christian; Lubecke, Victor; Lubecke, Lana C. (December 2020, 020 IEEE Asia-Pacific Microwave Conference (APMC))

   null (Ed.)

   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. 

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2. 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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3. Empirical Mode Decomposition (EMD) for Platform Motion Compensation in Remote Life Sensing Radar

   https://doi.org/10.1109/RWS53089.2022.9719920  

   Islam, Shekh M.; Oba, Lupua; Lubecke, Victor M. (January 2022, 2022 IEEE Radio and Wireless Symposium (RWS))

   Radar sensing of respiratory motion from unmanned aerial vehicles (UAVs) offers great promise for remote life sensing especially in post-disaster search and rescue applications. One major challenge for this technology is the management of motion artifacts from the moving UAV platform. Prior research has focused on using an adaptive filtering approach which requires installing a secondary radar module for capturing platform motion as a noise reference. This paper investigates the potential of the empirical mode decomposition (EMD) technique for the compensation of platform motion artifacts using only primary radar measurements. Experimental results demonstrated that the proposed EMD approach can extract the fundamental frequency of the breathing motion from the combined breathing and platform motion using only one radar, with an accuracy above 87%. 

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4. 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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5. Harnessing the Power of Vocal Signals in COVID-19 Detection Utilizing Machine Learning

   https://doi.org/10.1109/BigData62323.2024.10825050  

   Mann, Aleesa; Jadhav, Ajinkya P; Matovu, Richard; Gupta, Vibhuti (January 2025, IEEE)

   The global COVID-19 pandemic has strained healthcare systems and highlighted the need for accessible and efficient diagnostic methods. Traditional diagnostic tools, such as nasal swabs and biosensors, while accurate, pose significant logistical challenges and high costs, limiting their scalability. This paper explores an alternative, non-invasive approach to COVID-19 detection using machine learning algorithms to analyze vocal patterns, particularly cough and breathing sounds. Leveraging a publicly available dataset, we developed machine learning models capable of classifying audio samples as COVID-19 positive or negative. Our models achieve an AUC of up to 85% and an F1- score of 81%, demonstrating the potential of machine learning in enabling rapid, cost-effective COVID-19 diagnosis. These findings suggest that audio-based diagnostics could be a practical and scalable solution, particularly in resource-limited settings where traditional methods are less feasible. 

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