A Multi-Wavelength Optical Sensing Framework for Calibration-Free Wearable Blood Pressure Monitoring

Abstract

Blood pressure (BP) is a key indicator of cardiovascular health, with hypertension leading to significant morbidity and mortality worldwide. Continuous monitoring of BP is essential for early detection of cardiovascular disease, however current tools are either cumbersome, unreliable, or not suited for long-term use. Traditional cuff-based BP measurement, while reliable, is impractical for continuous monitoring. Recent advances using photoplethysmography (PPG) waveforms offer an alternative, but they face challenges such as limited interpretability, high computational complexity, and susceptibility to motion artifacts. In this paper, we introduce a novel multi-wavelength optical sensing framework designed for calibration-free wearable blood pressure monitoring. Our system utilizes a broad spectrum of wavelengths and interpretable features, combined with machine learning, to estimate systolic (SBP), diastolic (DBP), and mean arterial pressure (MAP). The framework was tested in a proof-of-concept study involving 9 subjects across varied postures and BP levels, demonstrating accuracy comparable to standard cuff-based techniques. This approach eliminates the need for continuous calibration and provides a scalable, interpretable solution for real-time, wearable BP monitoring.