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The COVID-19 pandemic has dramatically increased the use of face masks across the world. Aside from physical distancing, they are among the most effective protection for healthcare workers and the general population. Face masks are passive devices, however, and cannot alert the user in case of improper fit or mask degradation. Additionally, face masks are optimally positioned to give unique insight into some personal health metrics. Recognizing this limitation and opportunity, we present FaceBit: an open-source research platform for smart face mask applications. FaceBit’s design was informed by needfinding studies with a cohort of health professionals. Small and easily secured into any face mask, FaceBit is accompanied by a mobile application that provides a user interface and facilitates research. It monitors heart rate without skin contact via ballistocardiography, respiration rate via temperature changes, and mask-fit and wear time from pressure signals, all on-device with an energy-efficient runtime system. FaceBit can harvest energy from breathing, motion, or sunlight to supplement its tiny primary cell battery that alone delivers a battery lifetime of 11 days or more. FaceBit empowers the mobile computing community to jumpstart research in smart face mask sensing and inference, and provides a sustainable, convenient form factor for health management, applicable to COVID-19 frontline workers and beyond.

FaceBit Overview

Authors


Northwestern University

Georgia Institute of Technology

University of California, Los Angeles

Citation

@article{curtiss2021facebit,
  title={FaceBit: Smart Face Masks Platform},
  author={Curtiss, Alexander and Rothrock, Blaine and Bakar, Abu and Arora, Nivedita and Huang, Jason and Englhardt, Zachary and Empedrado, Aaron-Patrick and Wang, Chixiang and Ahmed, Saad and Zhang, Yang and Alshurafa, Nabil and Hester, Josiah},
  journal={Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
  volume={5},
  number={4},
  articleno={151},
  numpages={44},
  year={2021},
  publisher={ACM New York, NY, USA},
  url = {https://doi.org/10.1145/3494991},
  doi = {10.1145/3494991}
}

Acknowledgements

This research is based upon work supported by the National Science Foundation under award number CNS-2032408, as well as CNS-1850496, CNS-2038853, CNS-2030251, and CNS-2107400. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The authors would like to thank the anonymous associate editor for helpful guiding comments during the revision phase, Thomas Cohen and Alex Cindric for illustrations and visual design for figures, and Andrea Maioli for graciously helping us test FaceBit on many occasions.

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