Wearable Sensors for Monitoring Abnormalities in Sleep-Related Breathing

Sleep-disordered breathing, particularly obstructive sleep apnea, is a highly prevalent condition linked to fragmented sleep, impaired quality of life, and elevated risks of cardiovascular, metabolic, and neurocognitive disorders. Polysomnography remains the diagnostic gold standard for detecting sleep-related breathing abnormalities. However, its high cost, operational complexity, and patient discomfort hinder large-scale deployment and long-term monitoring. In recent years, wearable sensing technologies have emerged as promising alternatives, enabling portable, noninvasive, and user-friendly assessment of respiratory function in daily environments. By integrating diverse sensing modalities, including mechanical, optical, acoustic, and electrophysiological approaches, wearable sensors can capture critical physiological parameters, such as oxygen saturation, oronasal airflow, thoracoabdominal movement, and cardiac activity, supporting subsequent AI-assisted analysis. This review provides a comprehensive overview of current progress in wearable sensors for monitoring sleep-related breathing abnormalities, with emphasis on sensing principles, representative designs, and application scenarios. Key limitations, including motion artifacts, comfort constraints, and challenges in multimodal data fusion, are critically examined. By outlining these opportunities and challenges, this review aims to inform the development of next-generation wearable sensors and to facilitate the accurate, efficient, and personalized management of sleep health in both research and clinical practice.