Breaking Barriers in Health Monitoring: Multi-Scenario Vital Sign Detection Using Mm-Wave MIMO FMCW Radar

This paper explores the deployment of mm-wave Frequency Modulated Continuous Wave (FMCW) radar for vital sign detection across multiple scenarios. We focus on overcoming the limitations of traditional sensing methods by enhancing signal processing techniques to capture subtle physiological changes effectively. Our study introduces novel adaptations of the Prony and MUSIC algorithms tailored for real-time heart and respiration rate monitoring, significantly advancing the accuracy and reliability of non-contact vital sign monitoring using radar technologies. Notably, these algorithms demonstrate a robust ability to suppress noise and harmonic interference. For instance, the mean absolute errors (MAE) for MUSIC and Prony in heart rate detection are 1.8 and 0.81, respectively, while for respiration rate, the MAEs are 1.01 and 0.8, respectively. These results underscore the potential of FMCW radar as a reliable, non-invasive solution for continuous vital sign monitoring in healthcare settings, particularly in clinical and emergency scenarios where traditional contact-based monitoring is impractical.

Comprehensive mm-Wave FMCW Radar Dataset for Vital Sign Monitoring: Embracing Extreme Physiological Scenarios

Recent advancements in non-invasive health monitoring technologies underscore the potential of mm-Wave Frequency-Modulated Continuous Wave (FMCW) radar in real-time vital sign detection. This paper introduces a novel dataset, the first of its kind, derived from mm-Wave FMCW radar, meticulously capturing heart rate and respiratory rate under various conditions. Comprising data from ten participants, including scenarios with elevated heart rates and participants with diverse physiological profiles such as asthma and meditation practitioners, this dataset is validated against the Polar H10 sensor, ensuring its reliability for scientific research. This dataset can offer a significant resource for developing and testing algorithms aimed at non-invasive health monitoring, promising to facilitate advancements in remote health monitoring technologies.

Non-Invasive Monitoring of Vital Signs in Calves Using Thermal Imaging Technology

This study presents a non-invasive method using thermal imaging to estimate heart and respiration rates in calves, avoiding the stress from wearables. Using Kernelised Correlation Filters (KCF) for movement tracking and advanced signal processing, we targeted one ROI for respiration and four for heart rate based on their thermal correlation. Achieving Mean Absolute Percentage Errors (MAPE) of 3.08% for respiration and 3.15% for heart rate validates the efficacy of thermal imaging in vital signs monitoring, offering a practical, less intrusive tool for Precision Livestock Farming (PLF), improving animal welfare and management.