RGB Camera-Based Blood Pressure Measurement Using U-Net Basic Generative Model

Abstract

Blood pressure is a fundamental health metric widely employed to predict cardiac diseases and monitor overall well-being. However, conventional blood pressure measurement methods, such as the cuff method, necessitate additional equipment and can be inconvenient for regular use. This study aimed to develop a novel approach to blood pressure measurement using only an RGB camera, which promises enhanced convenience and accuracy. We employed the U-Net Basic generative model to achieve our objectives. Through rigorous experimentation and data analysis, our approach demonstrated promising results, attaining BHS (British Hypertension Society) baseline performance with grade A accuracy for diastolic blood pressure (DBP) and grade C accuracy for systolic blood pressure (SBP). The mean absolute error (MAE) achieved for DBP was 4.43 mmHg, and for SBP, it was 6.9 mmHg. Our findings indicate that blood pressure measurement using an RGB camera shows significant potential and may be utilized as an alternative or supplementary method for blood pressure monitoring. The convenience of using a commonly available RGB camera without additional specialized equipment can empower individuals to track their blood pressure regularly and proactively predict potential heart-related issues.