In wearable smart systems, continuous monitoring and accurate classification of different sleep-related conditions are critical for enhancing sleep quality and preventing sleep-related chronic conditions. However, the requirements for device-skin coupling quality in electrophysiological sleep monitoring systems hinder the comfort and reliability of night wearing. Here, we report a washable, skin-compatible smart garment sleep monitoring system that captures local skin strain signals under weak device-skin coupling conditions without positioning or skin preparation requirements. A printed textile-based strain sensor array responds to strain from 0.1% to 10% with a gauge factor as high as 100 and shows independence to extrinsic motion artefacts via strain-isolating printed pattern design. Through reversible starching treatment, ink penetration depth during direct printing on garments is controlled to achieve batch-to-batch performance variation < 10%. Coupled with deep learning, explainable artificial intelligence (XAI), and transfer learning data processing, the smart garment is capable of classifying six sleep states with an accuracy of 98.6%, maintaining excellent explainability (classification with low bias) and generalization (95% accuracy on new users with few-shot learning less than 15 samples per class) in practical applications, paving the way for next-generation daily sleep healthcare management.

翻译：在可穿戴智能系统中，持续监测并准确分类不同睡眠相关状态对于提升睡眠质量及预防睡眠相关慢性疾病至关重要。然而，电生理睡眠监测系统对设备-皮肤耦合质量的要求限制了夜间穿戴的舒适性与可靠性。本文报道了一种可水洗、皮肤兼容的智能服装睡眠监测系统，该系统可在弱设备-皮肤耦合条件下捕获局部皮肤应变信号，无需定位或皮肤预处理。基于印刷纺织品的应变传感器阵列可响应0.1%至10%的应变，其应变系数高达100，并通过应变隔离印刷图案设计实现了对外部运动伪影的独立性。通过可逆上浆处理，控制油墨在服装直接印刷过程中的渗透深度，实现了批次间性能变异小于10%。结合深度学习、可解释人工智能（XAI）及迁移学习数据处理技术，该智能服装能够以98.6%的准确率分类六种睡眠状态，并在实际应用中保持优异的可解释性（低偏差分类）与泛化能力（通过每类少于15个样本的小样本学习，对新用户的准确率达95%），为下一代日常睡眠健康管理开辟了道路。