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.