A deep learning-enabled smart garment for accurate and versatile sleep conditions monitoring in daily life
| Autor: | Tang, Chenyu, Yi, Wentian, Xu, Muzi, Jin, Yuxuan, Zhang, Zibo, Chen, Xuhang, Liao, Caizhi, Smielewski, Peter, Occhipinti, Luigi G. |
|---|---|
| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 20 pages, 5 figures, 1 table |
| Databáze: | arXiv |
| Externí odkaz: |
|