Nearly two years into the global Covid-19 coronavirus pandemic, it was only a matter of time before astute innovators took face mask technology to the next level.
Expert engineers and academics collaborating between Northwestern University, Georgia Institute of Technology, UCLA and other participating labs worked on this exact concept and have created “FaceBit”— a new, ground-breaking platform to transform traditional face masks into “smart masks.”
The idea of making any device or item “smart” refers to the concept of digitizing it in a useful manner— transforming it into an adaptive, often metric-oriented or data driven product that provides more value to the user.
FaceBit is attempting to work on this very idea. The concept was originally published in the Proceedings of the ACM [Association for Computing Machinery] on Interactive, Mobile, Wearable and Ubiquitous Technologies. The abstract astutely describes the inspiration behind the technology: “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.”
With this basic premise in mind, engineers sought to advance face mask technology into something that can provide useful, data driven 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.”
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Furthermore, the device “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.”
As described by Amanda Morris in the official press release by Northwestern University, “The app can immediately alert the user when issues — such as elevated heart rate or a leak in the mask — unexpectedly arise. The physiological data also could be used to predict fatigue, physical health status and emotional state.”
Dr. Josiah Hester, one of the visionaries behind the device and founder of the Ka Moamoa lab within the Northwestern McCormick School of Engineering, explains: “We wanted to design an intelligent face mask for health care professionals that does not need to be inconveniently plugged in during the middle of a shift […] We augmented the battery’s energy with energy harvesting from various sources, which means that you can wear the mask for a week or two without having to charge or replace the battery.”
For many healthcare and front-line professionals, the pandemic introduced a new way of life, one which often entails donning a cumbersome mask for 12-16 hours a day in order to protect themselves and their family members. Especially with regards to medical grade masks such as the N-95 which offer a tighter fit with a higher range of protection against the virus, masking has become an essential part of the war against Covid-19.
Technology such as FaceBit provide a new value proposition to these masks; not only will they help protect against viral spread, but additionally, they may be able to provide better insights, metrics, and usable data for the wearer. Indeed, if the concept can be perfected with safety, efficacy, privacy, and security in mind, it may stand to inspire an entirely new technological and innovative platform for years to come.