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

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 impo...

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Published in:Nano energy 2024-11, Vol.130, p.110124, Article 110124
Main Authors: Gu, Jimin, Jung, Yongsu, Ahn, Junseong, Ahn, Jihyeon, Choi, Jungrak, Kang, Byeongmin, Jeong, Yongrok, Ha, Ji-Hwan, Kim, Taehwan, Jung, Young, Park, Jaeho, Jung, Jiyoung, Ryu, Seunghwa, Lee, Ikjin, Park, Inkyu
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container_start_page 110124
container_title Nano energy
container_volume 130
creator Gu, Jimin
Jung, Yongsu
Ahn, Junseong
Ahn, Jihyeon
Choi, Jungrak
Kang, Byeongmin
Jeong, Yongrok
Ha, Ji-Hwan
Kim, Taehwan
Jung, Young
Park, Jaeho
Jung, Jiyoung
Ryu, Seunghwa
Lee, Ikjin
Park, Inkyu
description 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. [Display omitted] •A piezo-transmittance type strain sensor using auxetic structure was optimized using a machine-learning surrogate model.•The optimized sensor structure was obtained through Bayesian optimization method among various types of auxetic structures.•A self-powered wireless sensing system using different optimized sensors was developed with flexible solar cell and BLE.•The customized sensor system is utilized in structural health monitoring, human motion monitoring, and integrated with AR.
doi_str_mv 10.1016/j.nanoen.2024.110124
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subjects AR wearable system
Auxetic structure
Customizable performance
Machine learning
Self-powered sensor
Strain sensor
title Auxetic kirigami structure-based self-powered strain sensor with customizable performance using machine learning
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