Edge-AI Assisted MXene-Based Fiber Optic IoT Biosensor Framework for Intelligent Wearable Healthcare Electronics

Abstract

Wearable healthcare electronics have become widespread and rapid advancement of this technology has pushed for intelligent, real-time and non-invasive biosensing technologies. The traditional wearable sensors are generally low in sensitivity, susceptible to electromagnetic interference, and consume too much energy, which makes them unable to perform long-term healthcare monitoring. In this work, an Edge-AI enabled MXene-based fiber optic IoT biosensor architecture is proposed for intelligent wearable health care application. The proposed system combines MXene nanomaterial-coated fiber optic sensors, IoT communication, and edge artificial intelligence to facilitate real-time monitoring of biomedical signals and intelligent prediction of healthcare. Real-time analysis is performed on the biomedical signals acquired from the fiber optic biosensors by performing signal conditioning and feature extraction followed by lightweight classification algorithm on the edge-AI. This framework can enable low-latency communication, increased sensing sensitivity and energy efficient healthcare monitoring. Experimental results show the system’s improved signal stability, quick response speed and accurate health-state prediction when compared with traditional wearable sensing systems. The proposed framework has great potential in smart healthcare, Telemedicine, Wearable Electronics, and AI based remote patient monitoring applications.