  Continuous monitoring and accurate detection of complex sleep patterns
associated to different sleep-related conditions is essential, not only for
enhancing sleep quality but also for preventing the risk of developing chronic
illnesses associated to unhealthy sleep. Despite significant advances in
research, achieving versatile recognition of various unhealthy and sub-healthy
sleep patterns with simple wearable devices at home remains a significant
challenge. Here, we report a robust and durable ultrasensitive strain sensor
array printed on a smart garment, in its collar region. This solution allows
detecting subtle vibrations associated with multiple sleep patterns at the
extrinsic laryngeal muscles. Equipped with a deep learning neural network, it
can precisely identify six sleep states-nasal breathing, mouth breathing,
snoring, bruxism, central sleep apnea (CSA), and obstructive sleep apnea
(OSA)-with an impressive accuracy of 98.6%, all without requiring specific
positioning. We further demonstrate its explainability and generalization
capabilities in practical applications. Explainable artificial intelligence
(XAI) visualizations reflect comprehensive signal pattern analysis with low
bias. Transfer learning tests show that the system can achieve high accuracy
(overall accuracy of 95%) on new users with very few-shot learning (less than
15 samples per class). The scalable manufacturing process, robustness, high
accuracy, and excellent generalization of the smart garment make it a promising
tool for next-generation continuous sleep monitoring.
