Dual Photoplethysmography Pulse Wave Velocity-Based Blood Pressure Prediction for Wearable Healthcare Internet of Things

초록

The Internet of Things (IoT) is a modern means of communication and its integration with biosensing significantly enhanced the monitoring and sensing of human vital signs, with blood pressure (BP) emerging as a promising application within the healthcare IoT domain. This study introduces a real-time, machine learning (ML)-based 3-D printed wearable device for BP and heart rate (HR) estimation using an advanced bi-optical pulse-wave-velocity acquisition system (BOPAS), which employs a dual photoplethysmography with an IoT-integrated wireless architecture for remote healthcare monitoring. Unlike conventional approaches, the proposed architecture eliminates dependency on high-quality signals and computationally intensive ML models by directly demonstrating strong pulse wave velocity (PWV) and BP correlation (r =0.96 for systolic, 0.89 for diastolic). Moreover, it overcomes the limitations like pulse ejection period and real-time filtering delays found in existing PWV methods making it simpler, more accurate, and practical for real-time wearables. After evaluating 14 different ML models, support vector regressor (SVR) yielded the highest testing R-square (R2) scores, surpassing 0.9272 for systolic BP (SBP) and 0.7896 for diastolic BP (DBP). A trained SVR model is deployed on a custom made smartphone application and tested in real time, yielding a mean absolute error and standard deviation of 2.68 +/- 1.61 mmHg for SBP, 2.94 +/- 1.84 mmHg for DBP, and 0.81 +/- 0.55 beats per min for HR. Additionally, 90.1% of SBP samples and 85.0% of DBP samples exhibit absolute error less than 5 mmHg, demonstrating a promising results compared to the other approach.

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