A deep learning-enabled smart garment for accurate and versatile sleep conditions monitoring in daily life
<|reference_start|>A deep learning-enabled smart garment for accurate and versatile sleep conditions monitoring in daily life: 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.<|reference_end|>
@article{tang2024a,
    title={A deep learning-enabled smart garment for accurate and versatile sleep
  conditions monitoring in daily life},
    author={Chenyu Tang, Wentian Yi, Muzi Xu, Yuxuan Jin, Zibo Zhang, Xuhang Chen,
  Caizhi Liao, Peter Smielewski, Luigi G. Occhipinti},
    journal={arXiv preprint arXiv:2408.00753},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00753},
    primaryClass={eess.SP cs.AI}
}