Auxetic kirigami structure-based self-powered strain sensor with customizable performance using machine learning

Abstract

Recently, soft material based wearable sensors have discovered numerous applications in healthcare, sports monitoring, and virtual reality/augmented reality (VR/AR) systems. For these sensors, fulfilling user-specified requirements rather than just improving the sensor performance has become an important issue. In this study, a self-powered piezo-transmittance type strain sensor based on auxetic structures was optimized for configurable and user-specified characteristics using a machine-learning surrogate model. The sensor mechanism is based on the optical transmittance change induced by the gap opening of the auxetic kirigami structure. The sensor performance was analyzed according to the geometric design variables, and the optimal design was determined using Bayesian and Gaussian process to maximize the sensor performance for different purposes. The optimally designed geometries were used for self-powered sensors on a structural health monitoring (SHM) system, a human motion monitoring (HMM) system for monitoring sports performance and incorporated into an AR system.