Abstract

Blood pressure monitoring is a significant concern in the field of healthcare, and the utilization of flexible encapsulated sensors presents a promising solution for achieving noninvasive and comfortable monitoring. This paper presents a study on the flexible encapsulation of MEMS pressure sensors and the development of an enhanced arterial tonometry method for blood pressure measurement, ultimately leading to the realization of a blood pressure monitoring system based on flexible encapsulated sensors. To improve wearer comfort and acquire reliable pulse signals, a flexible encapsulation sensor combining parylene and PDMS materials was fabricated. Additionally, to address the issue of low accuracy in blood pressure measurement, various machine learning algorithms were compared and analyzed, leading to the identification of the random forest model as the optimal regressor. Consequently, a blood pressure monitoring system based on the improved arterial tension method was designed and implemented. The experimental results demonstrate that the proposed system achieved a significant enhancement of 31.4% and 21% in the accuracy of systolic and diastolic blood pressure measurements, respectively, compared with the arterial tension method.

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