Detecting Face-Mask Wearing Status Using Motion Sensors in Commercially Available Smartwatches

Abstract

Wearing a mask considerably mitigates the risk of infection from droplets. Automatic detection of whether a person wears a mask in his/her daily life and the type of masks the person wears can provide useful information for various services such as infection risk assessment, just-in-time alerts, and lifelogging. However, such automatic detection is difficult without the use of video processing or specialized equipment. In this study, the motion sensor of a commercially available smartwatch was used to detect the mask-wearing status. An investigation of the acceleration characteristic and an evaluation experiment of the mask-wearing state detection model revealed an accuracy of approximately 90% when specific motions were classified using motion sensors and machine learning. Furthermore, 98% accuracy was achieved when classifying sitting and walking activities.