Mileva Van Tuyl’s research while affiliated with MIT Lincoln Laboratory and other places

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (1)


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

March 2021

·

30 Reads

·

15 Citations

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies

·

Niels Poulsen

·

Mileva Van Tuyl

·

[...]

·

Fadel Adib

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.

Citations (1)


... Recent advances in mobile sensing technologies and artificial intelligence (AI) have led to the emergence of research on intelligent, just-in-time interventions (JITIs) using mobile or wearable devices [3,57,58,86,91,95,128]. A typical research paradigm usually starts by identifying a target undesirable behavior, followed by data collection from mobile and/or wearable devices, machine learning (ML) model development, and finally, real-time system evaluation (e.g., [3,26,46,53,79]). ...

Reference:

WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch
A Scalable Solution for Signaling Face Touches to Reduce the Spread of Surface-based Pathogens
  • Citing Article
  • March 2021

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies