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The US CDC has recognized moist-heat as one of the most effective and accessible methods of decontaminating N95 masks for reuse in response to the persistent N95 mask shortages caused by the COVID-19 pandemic. However, it is challenging to reliably deploy this technique in healthcare settings due to a lack of smart technologies capable of ensuring proper decontamination conditions of hundreds of masks simultaneously. To tackle these challenges, we developed an open-source wireless sensor platform---VeriMask1 ---that facilitates per-mask verification of the moist-heat decontamination process. VeriMask is capable of monitoring hundreds of masks simultaneously in commercially available heating systems and provides a novel throughput-maximization functionality to help operators optimize the decontamination settings. We evaluate VeriMask in laboratory and real-scenario clinical settings and find that it effectively detects decontamination failures and operator errors in multiple settings and increases the mask decontamination throughput. Our easy-to-use, low-power, low-cost, scalable platform integrates with existing hospital protocols and equipment, and can be broadly deployed in under-resourced facilities to protect front-line healthcare workers by lowering their risk of infection from reused N95 masks. We also memorialize the design challenges, guidelines, and lessons learned from developing and deploying VeriMask during the COVID-19 Pandemic. Our hope is that by reflecting and reporting on this design experience, technologists and front-line health workers will be better prepared to collaborate for future pandemics, regarding mask decontamination, but also other forms of crisis tech. 

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.