Detecting Face-Touch Hand Moves Using Smartwatch Inertial Sensors and Convolutional Neural Networks

Abstract

As per World Health Organization (WHO), avoiding touching the face when people are in public or crowded places is an effective way to prevent respiratory viral infections. This recommendation has become more crucial with the current health crisis and the worldwide spread of COVID-19 pandemic. However, most face touches are done unconsciously, that is why it is difficult for people to monitor their hand moves and try to avoid touching the face all the time. Hand-worn wearable devices like smartwatches are equipped with multiple sensors that can be utilized to track hand moves automatically. This work proposes a smartwatch application that uses small, efficient, and end-to-end Convolutional Neural Networks (CNN) models to classify hand motion and identify Face-Touch moves. To train the models, a large dataset is collected for both left and right hands with over 28k training samples that represents multiple hand motion types, body positions, and hand orientations. The app provides real-time feedback and alerts the user with vibration and sound whenever attempting to touch the face. Achieved results show state of the art face-touch accuracy with average recall, precision, and F1-Score of 96.75%, 95.1%, 95.85% respectively, with low False Positives Rate (FPR) as 0.04%. By using efficient configurations and small models, the app achieves high efficiency and can run for long hours without significant impact on battery which makes it applicable on most off-the-shelf smartwatches.