arXiv subjects

Yonathan Eder

Publications and source records attributed to Yonathan Eder.

3 recordsLinked to original sources

arXiv2026

SpiRadar: Radar-Based Non-Contact Spirometry via Sparse Polynomial Framework

Chronic respiratory diseases affect hundreds of millions of people worldwide, with spirometry serving as the gold standard for pulmonary function assessment. However, conventional spirometry's reliance on mouthpieces and nose clips creates discomfort and technical challenges that can compromise test quality, particularly in pediatric populations where cooperation difficulties are amplified. This study presents SpiRadar, a comprehensive radar-based framework for non-contact spirometry that eliminates physical contact requirements while enabling accurate curve reconstruction, clinical parameter estimation, and bronchodilator response (BDR) assessment. Our key contributions include: (1) a methodological framework integrating physiologically-motivated preprocessing for robust signal extraction, a feature-dependent polynomial transformation linking radar-measured thoracic displacement to spirometric volume curves, and sparse optimization enabling generalization to unseen subjects without individual calibration; (2) a rigorous validation on a pediatric cohort of 39 subjects (ages 6-18 years) undergoing 58 spirometry trials, including healthy children and asthma patients tested pre- and post-bronchodilator, using subject-level leave-one-out cross-validation; (3) clinical-grade performance outperforming alternative non-contact methods, achieving accurate curve reconstruction (mean RMSE: 0.23 +- 0.12 L) and strong correlations (above 0.84) for key spirometric parameters. BDR classification achieved 89.5% accuracy with balanced sensitivity (90.9%) and specificity (87.5%). These results establish clinical feasibility for our non-contact spirometry approach, with particular promise for pediatric asthma monitoring and remote patient care.

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arXiv2025

Robust Phantom-Assisted Framework for Multi-Person Localization and Vital Signs Monitoring Using MIMO FMCW Radar

With the rising prevalence of cardiovascular and respiratory disorders and an aging global population, healthcare systems face increasing pressure to adopt efficient, non-contact vital sign monitoring (NCVSM) solutions. This study introduces a robust framework for multi-person localization and vital signs monitoring, using multiple-input-multiple-output frequency-modulated continuous wave radar, addressing challenges in real-world, cluttered environments. Two key contributions are presented. First, a custom hardware phantom was developed to simulate multi-person NCVSM scenarios, utilizing recorded thoracic impedance signals to replicate realistic cardiopulmonary dynamics. The phantom's design facilitates repeatable and rapid validation of radar systems and algorithms under diverse conditions to accelerate deployment in human monitoring. Second, aided by the phantom, we designed a robust algorithm for multi-person localization utilizing joint sparsity and cardiopulmonary properties, alongside harmonics-resilient dictionary-based vital signs estimation, to mitigate interfering respiration harmonics. Additionally, an adaptive signal refinement procedure is introduced to enhance the accuracy of continuous NCVSM by leveraging the continuity of the estimates. Performance was validated and compared to existing techniques through 12 phantom trials and 12 human trials, including both single- and multi-person scenarios, demonstrating superior localization and NCVSM performance. For example, in multi-person human trials, our method achieved average respiration rate estimation accuracies of 94.14%, 98.12%, and 98.69% within error thresholds of 2, 3, and 4 breaths per minute, respectively, and heart rate accuracies of 87.10%, 94.12%, and 95.54% within the same thresholds. These results highlight the potential of this framework for reliable multi-person NCVSM in healthcare and IoT applications.

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arXiv2022

Sparsity Based Non-Contact Vital Signs Monitoring of Multiple People Via FMCW Radar

Non-contact technology for monitoring multiple people's vital signs, such as respiration and heartbeat, has been investigated in recent years due to the rising cardiopulmonary morbidity, the risk of transmitting diseases, and the heavy burden on the medical staff. Frequency modulated continuous wave (FMCW) radars have shown great promise in meeting these needs. However, contemporary techniques for non-contact vital signs monitoring (NCVSM) via FMCW radars, are based on simplistic models, and present difficulties coping with noisy environments containing multiple objects. In this work, we develop an extended model of FMCW radar signals in a noisy setting containing multiple people and clutter. By utilizing the sparse nature of the modeled signals in conjunction with human-typical cardiopulmonary features, we can accurately localize humans and reliably monitor their vital signs, using only a single channel and a single-input-single-output setup. To this end, we first show that spatial sparsity allows for both accurate detection of multiple people and computationally efficient extraction of their Doppler samples, using a joint sparse recovery approach. Given the extracted samples, we develop a method named Vital Signs based Dictionary Recovery (VSDR), which uses a dictionary-based approach to search for the desired rates of respiration and heartbeat over high-resolution grids corresponding to normal cardiopulmonary activity. The advantages of the proposed method are illustrated through examples that combine the proposed model with real data of $30$ monitored individuals. We demonstrate accurate human localization in a clutter-rich scenario that includes both static and vibrating objects, and show that our VSDR approach outperforms existing techniques, based on several statistical metrics. The findings support the widespread use of FMCW radars with the proposed algorithms in healthcare.

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