An IoMT-Driven Framework for Precision Cardiovascular Assessment Incorporating Multiscale Perspectives and Microfiber Bragg Grating

Cardiovascular disease remains a leading global health challenge, necessitating precise and continuous monitoring of blood pressure (BP) and cardiac function. Traditional noninvasive measurement techniques often present operational complexities and discomfort, while photoplethysmography (PPG) technology lacks the ability to capture comprehensive hemodynamic parameters. Herein, we introduce an innovative Internet of Medical Things (IoMT) framework for personalized hemodynamic assessment, driven by advanced flexible sensing technologies and multiscale modeling. Specifically, we propose a pulse wave detection system utilizing microfiber Bragg grating (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> FBG) sensors for comprehensive spatiotemporal monitoring. The system leverages a multiscale perception network (MSP-Net) to achieve precise BP estimation, with mean errors (MEs) and standard deviations (SDs) of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$- 0.05~\pm ~2.15$ </tex-math></inline-formula> mmHg for systolic BP (SBP) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$- 0.19~\pm ~3.48$ </tex-math></inline-formula> mmHg for diastolic BP (DBP). The system’s performance surpasses traditional PPG-based methods across multiple hemodynamic parameters. Leveraging pretraining and fine-tuning strategies, this study realizes personalized estimation models, furnishing technical support for precision medicine. Furthermore, we utilize a cloud-edge collaborative framework to achieve hemodynamic assessment across multiple IoMT devices via local area network and the RV1126 platform, providing real-time feedback, thereby establishing a closed-loop control system. Our study presents an innovative approach for the comprehensive assessment of cardiovascular function and the realization of personalized medicine, potentially serving as a promising solution for Healthcare 5.0 applications.