Key result
IoMT framework using microfiber sensors and MSP-Net achieves precise BP estimation with ~0.05 mmHg SBP error.
Why the study?
Traditional noninvasive measurement techniques present operational complexities and discomfort, while photoplethysmography lacks the ability to capture comprehensive hemodynamic parameters.
Does an IoMT framework utilizing μFBG sensors and MSP-Net improve blood pressure estimation accuracy compared to traditional PPG-based methods?
Does an IoMT framework utilizing μFBG sensors and MSP-Net improve blood pressure estimation accuracy compared to traditional PPG-based methods?
Answers are grounded in this paper and cite their sources.
An innovative IoMT framework using μFBG sensors and MSP-Net provides highly accurate, continuous blood pressure estimation, offering a promising tool for personalized cardiovascular monitoring.
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 ( <tex-math notation="LaTeX">μ </tex-math> 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 <tex-math notation="LaTeX">- 0.05~± ~2.15 </tex-math> mmHg for systolic BP (SBP) and <tex-math notation="LaTeX">- 0.19~± ~3.48 </tex-math> 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.
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Liu et al. (2024) studied Cardiovascular disease. Pulse wave detection system utilizing microfiber Bragg grating sensors and multiscale perception network (MSP-Net) vs. Traditional PPG-based methods was evaluated on Blood pressure estimation (SBP and DBP). An IoMT framework using microfiber Bragg grating sensors and MSP-Net achieved precise blood pressure estimation with mean errors of -0.05 ± 2.15 mmHg for SBP and -0.19 ± 3.48 mmHg for DBP.
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