A deep learning-enabled smart garment for accurate and versatile monitoring of sleep conditions in daily life

• 成果类型: Article
• 署名作者: Tang, Chenyu; Yi, Wentian; Xu, Muzi; Jin, Yuxuan; Zhang, Zibo; Chen, Xuhang; Liao, Caizhi; Kang, Mengtian; Gao, Shuo; Smielewski, Peter; Occhipinti, Luigi G.
• 署名单位: University of Cambridge; University of Cambridge; University of Cambridge; Capital Medical University; Beihang University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-12954
• DOI: 10.1073/pnas.2420498122
• 发表日期: 2025-02-18
• 关键词: management; bruxism; adults

摘要

In wearable smart systems, continuous monitoring and accurate classification of dif-ferent 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 reli-ability 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 artifacts 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 AI, 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.