Sleep quality directly affects daytime social functioning, cognitive performance, and physical and mental health. Wearable sleep monitoring technologies serve as a valuable complement to the gold standard polysomnography. This review systematically analyzes current limitations in wearable sleep monitoring hardware and sleep analysis algorithms, with a focus on sensor technology, algorithm design, and clinical validation, and provides insights and methodological guidance for future research and clinical application of wearable sleep monitoring technologies. The analysis shows that electroencephalogram-based detection systems typically achieve the highest recognition accuracy in sleep stage classification (around 90%) and can identify all sleep stages. Whereas accelerometer- and photoplethysmography-based systems, which are commonly used in wearables, have relatively lower recognition accuracy of the multi-stage sleep structure (around 70%). Multimodal signal fusion techniques have the potential to overcome the limitations of single-modality signals and improve recognition accuracy in sleep stage classification, although detecting sleep onset (N1) stage remains challenging. Therefore, future wearable technologies should advance toward innovations in diverse sensor designs and multimodal fusion algorithms in conjunction with clinical expertise, to promote the development of standardized consumer-grade sleep monitoring technologies.

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