MetaBP: A Meta-Learning Approach for Contact-Based Blood Pressure Measurement via Camera on Smart Devices

Blood pressure is a critical indicator of human health, making convenient and easy-to-use monitoring methods a significant research focus. Existing blood pressure monitoring techniques often rely on additional sensors or devices, imposing both usage and economic burdens on users. To tackle this problem, we propose MetaBP, a blood pressure monitoring system that leverages the camera on smart devices. The basic idea is to record a video when the fingertip covers the camera to capture the tiny skin color changes caused by the heartbeat and extract pulse signals from the video frames. Furthermore, MetaBP employs a meta-learning approach, enabling the model to quickly adapt to the blood pressure monitoring task with minimal data and develop personalized models. By leveraging meta-learning, the model can efficiently learn the unique features of an individual’s physiological signals, providing accurate and personalized blood pressure estimation even with limited training samples. To obtain accurate measurements, we use a variational mode decomposition (VMD) method to reduce signal noise, ensuring that the pulse signals are both reliable and precise. To evaluate the robustness of MetaBP, we conduct experiments with 30 participants and implement MetaBP on commercial devices with camera parameters. The results demonstrate that MetaBP can accurately estimate systolic and diastolic blood pressure, with mean errors of 1.37 and 0.82 mmHg and standard deviations of 7.39 and 5.83 mmHg. Furthermore, our results demonstrate the feasibility of using widely available smart device cameras for reliable blood pressure monitoring, offering a practical solution for continuous health tracking.