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

• URL: http://arxiv.org/abs/2408.00753v2
• Date: Thu, 3 Oct 2024 16:13:26 GMT
• Title: A deep learning-enabled smart garment for accurate and versatile sleep conditions monitoring in daily life
• Authors: Chenyu Tang, Wentian Yi, Muzi Xu, Yuxuan Jin, Zibo Zhang, Xuhang Chen, Caizhi Liao, Peter Smielewski, Luigi G. Occhipinti,
• Abstract summary: We report a washable, skin-compatible smart garment sleep monitoring system that captures local skin strain signals 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. 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 accuracy on new users with few-shot learning less than 15 samples per class.
• Score: 2.8587098692786905
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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