Issue 31, 2024

Functional fibers/textiles for smart sensing devices and applications in personal healthcare systems

Abstract

Personalized medical diagnostics and monitoring have become increasingly important due to inefficient and delayed medical services of traditional centralized healthcare systems. To enhance the comfort and portability, flexible health monitoring systems have been developed in recent years. In particular, smart fiber/textile-based sensing devices show superiority for continuously monitoring personal health and vital physiological parameters owing to their light weight, good flexibility and inherent miniaturization. This review focuses on the recent advances in smart fiber/textile-based sensing devices for wearable electronic applications. First, fabrication strategies of smart sensing fibers/textiles are introduced in detail. In addition, sensing performances, working principles and applications of smart sensing fibers/textiles such as pressure sensing fibers/textiles, stretchable strain sensing fibers/textiles, temperature sensing fibers/textiles, and biofluid, gas and humidity sensing fibers/textiles in health monitoring are also reviewed systematically. Finally, we propose current challenges and future prospects in the area of fiber/textile-based sensors for wearable healthcare monitoring and diagnosis systems. In general, this review aims to give an overall perspective of the promising field by reviewing various fiber/textile-based sensing devices and highlighting the importance for researchers to keep up with the sequential exploration of soft sensing fibers/textiles for applications in wearable smart systems.

Graphical abstract: Functional fibers/textiles for smart sensing devices and applications in personal healthcare systems

Article information

Article type
Critical Review
Submitted
17 iyn 2024
Accepted
03 iyl 2024
First published
04 iyl 2024

Anal. Methods, 2024,16, 5372-5390

Functional fibers/textiles for smart sensing devices and applications in personal healthcare systems

W. Lu, G. Wu, L. Gan, Y. Zhang and K. Li, Anal. Methods, 2024, 16, 5372 DOI: 10.1039/D4AY01127A

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