Battery-Free, Wireless Graphene Pressure Sensor for Machine Learning-assisted Posture Classification and VR/AR Visualization in Smart Healthcare Environments

Abstract

Continuous monitoring of pressure and temperature at skin interfaces is essential for preventing tissue damage and circulation-related complications in immobile patients. However, most existing healthcare pressure sensors remain bulky, wired, and battery-powered, which limit their suitability for long term use. Here, we report a battery-free, wireless multimodal sensing platform in which single-layer graphene functions as a high-performance pressure-sensing active layer, achieving high sensitivity (1.75 × 10⁻³ kPa⁻¹, gauge factor = 8.6) and excellent stability (over 1,000 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 (DNN), 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.