Advancements in 3D-printed wearable sensors: a modern healthcare

Abstract

Modern healthcare has been transformed by introducing 3D-printed wearable sensors, providing rapid, inexpensive, and customised diagnostic alternatives. An overview of 3D-printed wearable sensors, their development history, and the evolution of 3D printing technology are explored in this study, offering a thorough analysis of the developments associated with wearable sensors for modern healthcare and biomedical applications. Different 3D printing techniques, such as stereolithography, inkjet printing, fused deposition modelling, and other 3D printing methods, are summarised. The materials used for these techniques, such as flexible substrates, biocompatible composites, and conductive polymers, are thoroughly reviewed simultaneously, focusing on their relevance to healthcare. This review comprehensively examines the materials and methodologies used in developing 3D-printed wearable sensors for healthcare and biomedical applications, emphasising their significance, potential applications, and key findings from recent research. The study analyses the significant challenges posed by material limitations, printing resolution, and biocompatibility while critically assessing the primary advantages of 3D-printed wearable sensors, such as personalisation, rapid prototyping, and scalability. Design considerations are also thoroughly evaluated to maximise sensor performance and reliability, emphasising flexibility, durability, and user comfort. The overview further describes various healthcare applications of these sensors, from real-time diagnostic tools and continuous vital sign monitoring to rehabilitation devices. This review aims to provide valuable insights for researchers, engineers, and healthcare professionals by combining recent advancements and identifying current limitations. The review also explores future directions, focusing on sustainable materials for environmentally friendly sensor development and the integration of AI and IoT technologies for improved monitoring and diagnostics.