Wide‐Bandwidth Nanocomposite‐Sensor Integrated Smart Mask for Tracking Multiphase Respiratory Activities

  1. Jiao Suo
  2. Yifan Liu
  3. Cong Wu
  4. Meng Chen
  5. Qingyun Huang
  6. Yiming Liu
  7. Kuanming Yao
  8. Yangbin Chen
  9. Qiqi Pan
  10. Xiaoyu Chang
  11. Alice Yeuk Lan Leung
  12. Ho‐yin Chan
  13. Guanglie Zhang
  14. Zhengbao Yang
  15. Walid Daoud
  16. Xinyue Li
  17. Vellaisamy A. L. Roy
  18. Jiangang Shen
  19. Xinge Yu
  20. Jianping Wang
  21. Wen Jung Li

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Abstract

Wearing masks has been a recommended protective measure due to the risks of coronavirus disease 2019 (COVID‐19) even in its coming endemic phase. Therefore, deploying a “smart mask” to monitor human physiological signals is highly beneficial for personal and public health. This work presents a smart mask integrating an ultrathin nanocomposite sponge structure‐based soundwave sensor (≈400 µm), which allows the high sensitivity in a wide‐bandwidth dynamic pressure range, i.e., capable of detecting various respiratory sounds of breathing, speaking, and coughing. Thirty‐one subjects test the smart mask in recording their respiratory activities. Machine/deep learning methods, i.e., support vector machine and convolutional neural networks, are used to recognize these activities, which show average macro‐recalls of ≈95% in both individual and generalized models. With rich high‐frequency (≈4000 Hz) information recorded, the two‐/tri‐phase coughs can be mapped while speaking words can be identified, demonstrating that the smart mask can be applicable as a daily wearable Internet of Things (IoT) device for respiratory disease identification, voice interaction tool, etc. in the future. This work bridges the technological gap between ultra‐lightweight but high‐frequency response sensor material fabrication, signal transduction and processing, and machining/deep learning to demonstrate a wearable device for potential applications in continual health monitoring in daily life.

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  1. Version published to 10.1002/advs.202203565
  2. ScreenIT

    SciScore for 10.1101/2022.03.28.22273021: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2022.03.28.22273021v1 on medRxiv