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Mask Wearing Status Estimation with Smartwatches

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arxiv 2205.06113 v1 pith:M6H5JMC7 submitted 2022-05-12 cs.HC

classification cs.HC
keywords maskmaskreminderestimationremindsmartwatchesstatusstrapwear
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. XRF V2: A Dataset for Action Summarization with Wi-Fi Signals, and IMUs in Phones, Watches, Earbuds, and Glasses

    cs.CV 2025-01 conditional novelty 6.0 of 10

    A new dataset plus a Mamba-based model and a consistency metric for localizing and summarizing daily activities from Wi-Fi and wearable IMU data.

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