Transformation and Upgrading of Health Monitoring Promoted by Wearable Electronics and Artificial Intelligence

Abstract

Traditional clinical health monitoring relies on offline, intermittent detection modes, which are discontinuous and can only acquire discrete physiological and biochemical data, failing to satisfy the rising demands driven by chronic diseases and population aging. Featuring excellent flexibility and portability, wearable electronic systems are capable of long-term acquisition of vital signs and biomarkers in body fluids. However, the massive raw data collected by wearable devices cannot be directly applied to clinical decision-making, and artificial intelligence (AI) serves as an effective solution to enable intelligent data analysis and interpretation. Recently, remarkable progress has been achieved and numerous studies have been reported in the fields of wearable electronic systems and AI algorithms. Thus, this review systematically summarizes the state-of-the-art advances of AI-empowered wearable health monitoring systems. This review comprehensively elaborates on flexible sensors for detecting human vital signs (heart rate, blood pressure, body temperature, etc.), as well as on optical and electrochemical wearable devices for quantitative analysis of multiple biochemical markers in body fluids. Furthermore, typical AI approaches widely adopted for wearable data processing and AI-empowered applications covering physiological parameter prediction as well as disease–health state classification are summarized. Eventually, this review highlights current technical bottlenecks with respect to the combination of wearable devices and AI models in practical popularization and clinical translation, and proposes targeted future development directions, aiming to provide systematic references for further optimization and transformation of intelligent wearable health monitoring technologies.