Case Study Analysis of AI-Powered Sensor Fabrics for Continuous Health Monitoring in Chronic Disease Management

Abstract

AI-powered sensor fabrics have emerged as a groundbreaking innovation in healthcare, enabling continuous, real-time health monitoring through smart textiles embedded with biosensors and AI-driven analytics. Unlike traditional health monitoring devices, these sensor fabrics offer seamless, non-invasive tracking of physiological parameters such as heart rate, glucose levels, blood pressure, and respiratory function, facilitating early disease detection and proactive medical intervention. This study adopts a case study methodology, examining three distinct cases to evaluate the effectiveness, usability, and adoption of AI-integrated sensor fabrics in healthcare. The first case study investigates remote patient monitoring, analyzing how smart textiles improve chronic disease management and adherence to treatment plans. The second case study explores the real-world clinical implementation of sensor fabrics in hospital environments, focusing on nursing workload reduction, patient safety, and integration with electronic health records (EHRs). The third case study provides a comparative analysis of patient outcomes, assessing the differences between patients using AI-powered textiles versus those relying on conventional health monitoring tools. The findings reveal that AI-powered fabrics significantly enhance patient engagement, reduce hospital readmissions by 30%–40%, and improve the efficiency of healthcare professionals by decreasing manual health checks by 45%. However, challenges such as sensor durability, data security, and affordability remain key obstacles to widespread adoption. The study underscores the potential of AI-integrated sensor fabrics in shifting healthcare toward preventive care, emphasizing the need for further technological advancements, regulatory compliance, and cost-effective production strategies to ensure broader accessibility and clinical reliability.