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1. Radar Monitoring in Sleep Medicine

   https://doi.org/10.1109/TELSIKS57806.2023.10316075  

   Lubecke, Victor; Borić-Lubecke, Olga; Ishrak, Mohammad Shadman; Wu, Teresa; Cai, Fulin (October 2023, IEEE)

   An overnight sleep study can provide vital health diagnostics yet typically involves applying and monitoring multiple body-contact sensors, which can interfere with sleep and require cumbersome manual data analysis. Doppler radar technology has been demonstrated to provide a non-invasive means of measuring vital signs through clothing and bedding, including respiratory rate, heart rate, motion activity, body position, and tidal respiratory volume. This paper examines the potential for applying physiological radar to assess sleep apnea and intervention strategies. 

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2. Dynamic Arctangent Center-Tracking Method for Respiratory Displacement Monitoring of Subjects in Arbitrary Positions

   https://doi.org/10.1109/EMBC40787.2023.10340361  

   Ishrak, Mohammad Shadman; Sameera, Jannatun Noor; Boric-Lubecke, Olga; Lubecke, Victor M (July 2023, IEEE)

   Accurate continuous measurement of respiratory displacement using continuous wave Doppler radar requires rigorous management of dc offset which changes when a subject changes distance from the radar measurement system. Effective measurement, therefore, requires robust dynamic calibration which can recognize and compensate for changes in the nominal position of a subject. In this paper, a respiratory displacement measurement algorithm is proposed which can differentiate between sedentary and non-sedentary conditions and continuously adapt to provide long-term monitoring of a subject’s sedentary respiration. Arctangent demodulation is an effective means of quantifying continuous displacement using a quadrature Doppler radar, yet it depends on accurate identification of dc offset and dc information contributions in the radar I-Q arc with the subject in a particular position. The dynamic calibration method proposed here is demonstrated to differentiate between sedentary and non-sedentary conditions for six subjects to produce accurate sedentary respiration measurements even when the subject arbitrarily changes position, once the appropriate thresholds are established for the measurement environment. 

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3. 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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4. Identity Authentication in Two-Subject Environments Using Microwave Doppler Radar and Machine Learning Classifiers

   https://doi.org/10.1109/TMTT.2022.3197413  

   Islam, Shekh M.; Boric-Lubecke, Olga; Lubecke, Victor M. (August 2022, IEEE Transactions on Microwave Theory and Techniques)

   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% 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. 

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5. 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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