Mask Wearing Status Estimation with Smartwatches
Huina Meng, Xilei Wu, Xin Wang, Yuhan Fan, Jingang Shi, Han Ding, Fei, Wang

TL;DR
This paper introduces MaskReminder, a smartwatch-based system using deep learning to accurately detect mask-wearing status and remind users, aiding COVID-19 transmission prevention.
Contribution
It presents a novel deep learning approach with MLP-Mixer for mask-related movement recognition using smartwatch inertial data, achieving high accuracy and user-independent reminders.
Findings
89% average recognition accuracy
90% success rate in user-independent reminders
Effective detection of mask-related hand movements
Abstract
We present MaskReminder, an automatic mask-wearing status estimation system based on smartwatches, to remind users who may be exposed to the COVID-19 virus transmission scenarios, to wear a mask. MaskReminder with the powerful MLP-Mixer deep learning model can effectively learn long-short range information from the inertial measurement unit readings, and can recognize the mask-related hand movements such as wearing a mask, lowering the metal strap of the mask, removing the strap from behind one side of the ears, etc. Extensive experiments on 20 volunteers and 8000+ data samples show that the average recognition accuracy is 89%. Moreover, MaskReminder is capable to remind a user to wear with a success rate of 90% even in the user-independent setting.
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Taxonomy
TopicsGait Recognition and Analysis · Infection Control and Ventilation
