Multimodal Hydrogel-Based Sensors for Sleep Respiratory Monitoring: Signal Sensing Mechanisms and Structural Design Strategies

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.