Abstract

Chronical cardiovascular decreases such as hypertension requires real-time and continuous monitoring of blood pressures (BPs). Pulse wave that contains critical and ample information on cardiovascular dynamics is a direct vital sign to extract BP and therefore an epidermal wearable device enabling real-time acquisition of pulse waves becomes necessary. In this work, we propose and study a flexible pulse wave sensory array aiming for real-time wearable pulse wave acquisition with robustness. A piezoelectric sensor together with a thin-film transistor-based sensor interface circuit is used to detect multiple pulse waveforms at the location of radial artery, which are input to a convolutional neural network for a deep-learning BP estimation model training. The estimated BPs are assessed by a correlation study with the BPs measured by conventional sphygmomanometers. Our study shows a reasonable accuracy of mean deviation and standard deviation of 2.84 ± 7.53 mmHg for systolic BP and 0.88 ± 7.06 mmHg for diastolic BP.

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