Internet of Things and Machine Learning Enabled Smart e-Textile with Exceptional Breathability for Point-of-Care Diagnostics

Abstract

In recent years, the convergence of smart electronic textile (e-textile) and digital technology has emerged as a transformative shift in healthcare, offering innovative solutions for point-of-care diagnostics. However, the development of textile electronics with exceptional functionality and comfort still remains challenging. Here, all-electrospun piezoelectric smart e-textile empowered is reported by Internet of Things (IoT) and machine learning for advanced point-of-care diagnostics. The resulting e-textile exhibits exceptional breathability (b ≈ 4.13 kg m⁻² d⁻¹), flexibility, water-resistive properties (water contact angle ≈137°), and mechano-sensitivity of 1.5 V N⁻¹ due to its mechanical-to-electrical energy conversion abilities. It can efficiently monitor different critical biomedical healthcare signals, such as, arterial pulse and respiration rate. Importantly, the e-textile sensor demonstrates remarkable attributes, generating an open circuit voltage of 10.5 V, a short circuit current of 7.7 µA, and power density of 4.2 µW cm⁻². Moreover, the e-textile provides real-time, non-invasive monitoring of human physiological movements through IoT. It is worth highlighting that the machine learning showcases an impressive 96% of accuracy in detecting respiratory signals, representing a significant accomplishment. Thus, this e-textile has enormous potential in remote patient monitoring and early disease detection, aiming to reduce healthcare costs, enhance patient outcomes, and improve the overall quality of medical care.