Piezoelectric metamaterial blood pressure sensor

Abstract

Continuousblood pressure monitoring allows for detecting the earlyonset of cardiovascular disease and assessing personal health status.Conventional piezoelectric blood pressure monitoring techniques havethe ability to sense biosignals due to their good dynamic responsesbut have significant drawbacks in terms of power consumption, whichlimits the operation of blood pressure sensors. Although piezoelectricmaterials can be used to enhance the self-powered blood pressure sensorresponses, the structure of the piezoelectric element can be modifiedto achieve a higher output voltage. Here, a structural study on piezoelectricmetamaterials in blood pressure sensors is demonstrated, and outputvoltages are computed and compared to other architectures. Next, aBayesian optimization framework is defined to get the optimal designaccording to the metamaterial design space. Machine learning algorithmswere used for applying regression models to a simulated dataset, anda 2D map was visualized for key parameters. Finally, a time-dependentblood pressure was applied to the inner surface of an artery vesselinside a 3D tissue skin model to compare the output voltage for differentmetamaterials. Results revealed that all types of metamaterials cangenerate a higher electric potential in comparison to normal square-shapedpiezoelectric elements. Bayesian optimization showed that honeycombmetamaterials had the optimal performance in generating output voltage,which was validated according to regression model analysis resultingfrom machine learning algorithms. The simulation of time-dependentblood pressure in a 3D skin tissue model revealed that the designsuggested by the Bayesian optimization process can generate an electricpotential more than two times greater than that of a conventionalsquare-shaped piezoelectric element.