A Scalable Solution for Signaling Face Touches to Reduce the Spread of Surface-based Pathogens

<jats:p>Hand-to-Face transmission has been estimated to be a minority, yet non-negligible, vector of COVID-19 transmission and a major vector for multiple other pathogens. At the same time, as it cannot be effectively addressed with mainstream protection measures, such as wearing masks or trac...

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Bibliographic Details
Main Authors: Rojas, Camilo, Poulsen, Niels, Van Tuyl, Mileva, Vargas, Daniel, Cohen, Zipporah, Paradiso, Joe, Maes, Pattie, Esvelt, Kevin, Adib, Fadel
Other Authors: Massachusetts Institute of Technology. Media Laboratory
Format: Article
Language:English
Published: Association for Computing Machinery (ACM) 2022
Online Access:https://hdl.handle.net/1721.1/146022
Description
Summary:<jats:p>Hand-to-Face transmission has been estimated to be a minority, yet non-negligible, vector of COVID-19 transmission and a major vector for multiple other pathogens. At the same time, as it cannot be effectively addressed with mainstream protection measures, such as wearing masks or tracing contacts, it remains largely untackled. To help address this issue, we have developed Saving Face - an app that alerts users when they are about to touch their faces, by analyzing the distortion patterns in the ultrasound signal emitted by their earphones. The system only relies on pre-existing hardware (a smartphone with generic earphones), which allows it to be rapidly scalable to billions of smartphone users worldwide. This paper describes the design, implementation and evaluation of the system, as well as the results of a user study testing the solution's accuracy, robustness, and user experience during various day-to-day activities (93.7% Sensitivity and 91.5% Precision, N=10). While this paper focuses on the system's application to detecting hand-to-face gestures, the technique can also be applicable to other types of gestures and gesture-based applications.</jats:p>