With rising cardiovascular and respiratory disorders and an aging population, healthcare systems require 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 (MIMO) frequency-modulated continuous wave (FMCW) radar in cluttered environments. First, we developed a hardware phantom that simulates multi-person NCVSM scenarios using recorded thoracic impedance signals to replicate cardiopulmonary dynamics, enabling repeatable validation under diverse conditions. Second, we designed a robust algorithm for multi-person localization leveraging sparse signal processing with physiological characteristics, alongside dictionary-based vital signs estimation that handles interfering respiration harmonics. An adaptive signal refinement procedure enhances continuous NCVSM accuracy by leveraging estimate continuity. Performance was validated through 12 phantom trials and 12 human trials in single- and multi-person scenarios, demonstrating superior performance. In multi-person human trials, our method achieved respiration rate 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, highlighting the framework's potential for reliable healthcare and IoT applications.

The rising demand for multi-person non-contact vital signs monitoring (NCVSM) in healthcare highlights the potential of radar technology, especially in cluttered environments. Single-input multiple-output frequency-modulated continuous- wave (FMCW) radars enable multi-object localization, which is crucial for multi-person NCVSM. However, detecting and positioning humans in crowded scenarios is challenging due to resolution limitations. This work first proposes an iterative method for multi-human localization exploiting joint sparsity and cardiopulmonary properties. Then, the method is unfolded into a neural network that preserves the data’s unique features to further enhance accuracy and reduce computational cost. Simulations containing real-world data show the network’s superior performance in detecting and positioning multiple adjacent humans, outperforming existing techniques via key metrics. This approach can be integrated into advanced NCVSM systems where accuracy and computational efficiency are paramount.

Gold nanoparticles (GNPs) hold promise to improve the detection and treatment of diseases such as cancer and Alzheimer's. However, detecting GNPs in the body remains an unmet technological challenge. This work introduces a new methodology for remotely localizing and tracking GNPs in various therapeutic settings, including through biological tissues, using a compact millimeter-wave radar system. By modeling GNP detection as a sparse recovery problem, we propose a method that localizes GNPs in cluttered environments as well as detects changes in concentrations that relate to changes in the intensity of the reflected signals. Our approach paves the way to a first-ever noninvasive, non-ionizing, simple approach for bioimaging and tracking GNPs inside the body.

Non-contact vital signs monitoring (NCVSM) of multiple people is becoming a necessity in healthcare due to increasing morbidity and manpower shortage. In meeting these requirements, frequency modulated continuous wave (FMCW) radars have shown great potential. However, current techniques present difficulties in locating and monitoring humans in noisy environments containing multiple objects. In this work, we first develop a model for NCVSM of multiple people via FMCW radar, based on a single-input-multiple-output setup. By considering the sparse nature of the modeled signals along with human-typical cardiopulmonary characteristics, we provide a joint-sparse recovery mechanism to accurately localize targets in a clutter-rich scenario where existing techniques struggle. Then, we present a robust method for NCVSM of the found individuals, with improved performance results when compared to current NCVSM techniques using several statistical metrics. Our approach offers excellent performance in a medical application where high accuracy is required.

Non-contact technology for monitoring the vital signs of multiple individuals, such as respiration and heartbeat, has been investigated in recent years due to the rising cardiopulmonary morbidity, the risk of disease transmission, and the heavy burden on medical staff. Frequency-modulated continuous wave (FMCW) radars have shown great promise in meeting these needs, even using a single-input-single-output (SISO) setup. However, contemporary techniques for non-contact vital signs monitoring (NCVSM) via SISO FMCW radar, are based on simplistic models and present difficulties in coping with noisy environments containing multiple objects. In this work, we first develop an extended model for multi-person NCVSM via SISO FMCW radar. Then, by utilizing the sparse nature of the modeled signals in conjunction with human-typical cardiopulmonary features, we present accurate localization and NCVSM of multiple individuals in a cluttered scenario, even with only a single channel. Specifically, we provide a joint-sparse recovery mechanism to localize people and develop a robust method for NCVSM called Vital Signs-based Dictionary Recovery (VSDR), which uses a dictionary-based approach to search for the rates of respiration and heartbeat over high-resolution grids corresponding to human cardiopulmonary activity. The advantages of our method are illustrated through examples that combine the proposed model with in-vivo data of 30 individuals. We demonstrate accurate human localization in a noisy scenario that includes both static and vibrating objects and show that our VSDR approach outperforms existing NCVSM techniques based on several statistical metrics. The findings support the widespread use of FMCW radars with the proposed algorithms in healthcare.