Sleep respiratory monitoring plays a crucial role in the early diagnosis and health management of sleep-related respiratory disorders. However, conventional monitoring devices often suffer from some critical issues, such as poor wearing comfort and insufficient capability for long-term continuous monitoring, hindering their applications in non-invasive and prolonged sleep monitoring. In recent years, hydrogel-based mechanical sensors have attracted increasing attention for sleep respiratory monitoring owing to their outstanding flexibility, biocompatibility, mechanical compatibility with biological tissues, and excellent sensing performance, demonstrating great potential for practical applications. A comprehensive overview of recent advances in hydrogel-based mechanical sensors for sleep-related respiratory monitoring is provided in this review. The sensing mechanisms, materials, fabrication strategies, and representative applications are systematically summarized, with particular emphasis on the key factors governing sensor performance and their translation toward practical use. Furthermore, current challenges and corresponding optimization strategies are discussed. This review aims to provide valuable insights into the rational design and optimization of high-performance hydrogel-based mechanical sensors, and important strategies for the further development of advanced technologies for sleep-related respiratory monitoring.

Sleep-related breathing disorders, particularly the obstructive sleep apnea syndrome, lead to an urgent demand for continuous, comfortable, and accurate monitoring technologies. However, conventional polysomnography is limited by complex procedures, poor wearing comfort, and restricted suitability for long-term monitoring. Owing to their skin-like mechanical performance, high biocompatibility, high ionic conductivity, and excellent tissue compatibility, hydrogels have emerged as promising materials for flexible bioelectronic devices. Recent multimodal hydrogel-based sensors enable simultaneous acquisition of mechanical, thermohygrometric, and electrophysiological signals, providing new opportunities for comprehensive sleep respiratory monitoring. This review systematically discusses the mechanisms for acquiring and analyzing multimodal physiological signals, including respiratory motion, respiratory airflow, auxiliary physiological signals, and AI-assisted sleep respiratory state recognition. Furthermore, structural design strategies, including single-layer, multilayer, biomimetic, and integrated architectures, are reviewed to clarify how structural configurations facilitate multimodal signal acquisition, signal interference suppression, and sensing performance optimization. This review provides valuable references for the future development of high-performance wearable multimodal hydrogel-based sensors toward sleep respiratory monitoring.