A battery-free, wireless graphene pressure sensor for machine learning-assisted posture classification and VR/AR visualization in smart healthcare environments

Materials Horizons 2026

View at publisher
Authors: Myungwoo Choi, Younghan Kim, Hyeonseok Han, Gongkyu Byeon, Yonghee Lee, Jeong Seok Han, Nam Hyeong Lee, Sang Won Kim, Hakyoung Lee, Yoorim Loh, Sangbin Lee, Dong Gyun Hong
+12 more Sunwoo Lee, Seokjoo Cho, Jewook Kim, Jeong-O Lee, Jungmo Kim, Seung Yol Jeong, Jun Chang Yang, Sunjin Yu, Seokwoo Jeon, Donghwi Cho, Inkyu Park, Yong Suk Oh
DOI: 10.1039/d5mh02270c
Cited by: 2
References: 43

Abstract

, gauge factor = 8.6) and excellent stability (over 1000 operational cycles). The platform enables real-time, reversible detection of pressure and temperature at the skin-device interfaces without external power source. By leveraging deep-learning algorithms, particularly deep neural networks (DNNs), the acquired signals are classified into distinct sitting postures, thereby enabling intelligent and continuous monitoring of patient status. Furthermore, integrated augmented- and virtual-reality (AR/VR) interfaces visualize pressure distributions in real time, enabling immersive and remote healthcare oversight. Collectively, this work introduces a graphene-based smart sensing platform that seamlessly integrates wireless operation, AI-driven analytics, and AR/VR visualization for advanced patient monitoring as a sort of personalized and interactive smart healthcare.

Fields of Study & Subjects

Advanced Sensor and Energy Harvesting Materials Non-Invasive Vital Sign Monitoring Pressure Ulcer Prevention and Management

Publication details

Journal / source
Materials Horizons
Publisher
Royal Society of Chemistry (RSC)
Volume
13
Issue
8
Pages
3814–3826
Publication date
1 January 2026
Language
EN