FaceOff: Detecting Face Touching with a Wrist-Worn Accelerometer
This addresses a health issue for the general public by providing a potential tool to reduce infection risk, but it is incremental as it builds on existing motion-sensing ideas with limited validation.
The paper tackles the problem of detecting face-touching behavior to prevent COVID-19 transmission by developing FaceOff, a wrist-worn accelerometer technique, achieving results from a preliminary user test with 3 participants over 90 minutes.
According to the CDC, one key step of preventing oneself from contracting coronavirus (COVID-19) is to avoid touching eyes, nose, and mouth with unwashed hands. However, touching one's face is a frequent and spontaneous behavior---one study observed subjects touching their faces on average 23 times per hour. Creative solutions have emerged amongst some recent commercial and hobbyists' projects, yet most either are closed-source or lack validation in performance. We develop FaceOff---a sensing technique using a commodity wrist-worn accelerometer to detect face-touching behavior based on the specific motion pattern of raising one's hand towards the face. We report a survey (N=20) that elicits different ways people touch their faces, an algorithm that temporally ensembles data-driven models to recognize when a face touching behavior occurs and results from a preliminary user testing (N=3 for a total of about 90 minutes).