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ESTIMATION OF RESTING BLOOD PRESSURE USING FACIAL THERMAL IMAGES BY SEPARATING ACUTE STRESS VARIATIONS

Abstract:

Objective: This study explores the potential of utilizing facial thermal images to estimate resting blood pressure by isolating acute stress variations. The objective is to develop a non-invasive, contactless method for continuous monitoring of blood pressure in real-time, enhancing the convenience and accessibility of healthcare.

Methods: Facial thermal imaging data were collected from a diverse group of participants under controlled conditions. Concurrently, blood pressure measurements were obtained using standard methods. Deep learning algorithms were employed to analyze facial thermal patterns and extract features indicative of acute stress variations. A novel approach was developed to separate stress-related thermal changes from baseline thermal patterns.

Results: The study demonstrated a strong correlation between facial thermal patterns and acute stress variations, allowing for the estimation of resting blood pressure. The developed algorithm successfully distinguished stress-related variations from normal thermal fluctuations, providing a reliable metric for blood pressure estimation. The accuracy of the proposed method was validated against conventional blood pressure measurements, showing promising results.

Conclusion: The findings suggest that facial thermal imaging, coupled with advanced machine learning techniques, has the potential to serve as a non-invasive tool for estimating resting blood pressure. The ability to separate acute stress variations from baseline thermal patterns enhances the precision of the proposed method. This approach opens avenues for continuous, unobtrusive monitoring of blood pressure, offering a convenient and accessible solution for healthcare applications.

Keywords: Resting Blood Pressure, Facial Thermal Imaging, Acute Stress Variations, Deep Learning, Non-Invasive Monitoring, Healthcare Technology.