Wearable PiezoelectricBased System for Continuous BeattoBeat Blood Pressure Measurement Noninvasive continuous blood pressure measurement is an emerging issue that potentially can be applied to cardiovascular disease monitoring and prediction. Recently, many groups have proposed the pulse transition time (PTT) method to estimate blood pressure for longterm monitoring. However, the PTTbased methods for blood pressure estimation are limited by nonspecific estimation models and require multiple calibrations. This study aims to develop a lowcost wearable piezoelectricbased system for continuous beattobeat blood pressure measurement. The pressure change in the radial artery was extracted by systolic and diastolic feature points in pressure pulse wave (PPW) and the pressure sensitivity of the sensor. The proposed system showed a reliable accuracy of systolic blood pressure (SBP) (mean absolute error (MAE) xc2xb1 standard deviation (SD) 1.52 xc2xb1 0.30 mmHg) and diastolic blood pressure (DBP, MAE xc2xb1 SD 1.83 xc2xb1 0.50), and its performance agreed with standard criteria of MAE within 5 mmHg and SD within xc2xb18 mmHg. In conclusion, this study successfully developed a lowcost, highaccuracy piezoelectricbased system for continuous beattobeat SBP and DBP measurement without multiple calibrations and complex regression analysis. The system is potentially suitable for continuous, longterm blood pressuremonitoring applications.

Continuous Blood Pressure Estimation from TwoChannel PPG Parameters by XGBoost Cuffless and noninvasive continuous blood pressure (BP) measurement is essential for the monitoring of cardiovascular disease. It is widely recognized that the photoplethysmography (PPG) signal can track BP by extracting PPG feature points. In this study, the PPG signal of the two sensors fixed at the hand was extracted, include the timedomain features and the pulse transit time (PTT) features extracted by the twochannel PPG signal. This study proposes a novel feature selection algorithm to ensure continuous extraction of feature points from a lowquality PPG signal. The blood pressure estimator was trained by the XGBoost model, which can solve the problem of missing features. The data was collected from 20 subjects, and a total of 80 sets of data were used to train and validate our proposed model. The results attained the accuracy of 1.56 xc2xb1 3.39 mmHg for SBP and 3.11 xc2xb1 3.78 mmHg for DBP.