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FaceGuard: A Wearable System To Avoid Face Touching

Most people touch their faces unconsciously, for instance to scratch an itch or to rest one’s chin in their hands. To reduce the spread of the novel coronavirus (COVID-19), public health officials recommend against touching one’s face, as the virus is transmitted through mucous membranes in the mout...

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Autores principales: Michelin, Allan Michael, Korres, Georgios, Ba’ara, Sara, Assadi, Hadi, Alsuradi, Haneen, Sayegh, Rony R., Argyros, Antonis, Eid, Mohamad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8060563/
https://www.ncbi.nlm.nih.gov/pubmed/33898529
http://dx.doi.org/10.3389/frobt.2021.612392
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author Michelin, Allan Michael
Korres, Georgios
Ba’ara, Sara
Assadi, Hadi
Alsuradi, Haneen
Sayegh, Rony R.
Argyros, Antonis
Eid, Mohamad
author_facet Michelin, Allan Michael
Korres, Georgios
Ba’ara, Sara
Assadi, Hadi
Alsuradi, Haneen
Sayegh, Rony R.
Argyros, Antonis
Eid, Mohamad
author_sort Michelin, Allan Michael
collection PubMed
description Most people touch their faces unconsciously, for instance to scratch an itch or to rest one’s chin in their hands. To reduce the spread of the novel coronavirus (COVID-19), public health officials recommend against touching one’s face, as the virus is transmitted through mucous membranes in the mouth, nose and eyes. Students, office workers, medical personnel and people on trains were found to touch their faces between 9 and 23 times per hour. This paper introduces FaceGuard, a system that utilizes deep learning to predict hand movements that result in touching the face, and provides sensory feedback to stop the user from touching the face. The system utilizes an inertial measurement unit (IMU) to obtain features that characterize hand movement involving face touching. Time-series data can be efficiently classified using 1D-Convolutional Neural Network (CNN) with minimal feature engineering; 1D-CNN filters automatically extract temporal features in IMU data. Thus, a 1D-CNN based prediction model is developed and trained with data from 4,800 trials recorded from 40 participants. Training data are collected for hand movements involving face touching during various everyday activities such as sitting, standing, or walking. Results showed that while the average time needed to touch the face is 1,200 ms, a prediction accuracy of more than 92% is achieved with less than 550 ms of IMU data. As for the sensory response, the paper presents a psychophysical experiment to compare the response time for three sensory feedback modalities, namely visual, auditory, and vibrotactile. Results demonstrate that the response time is significantly smaller for vibrotactile feedback (427.3 ms) compared to visual (561.70 ms) and auditory (520.97 ms). Furthermore, the success rate (to avoid face touching) is also statistically higher for vibrotactile and auditory feedback compared to visual feedback. These results demonstrate the feasibility of predicting a hand movement and providing timely sensory feedback within less than a second in order to avoid face touching.
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spelling pubmed-80605632021-04-23 FaceGuard: A Wearable System To Avoid Face Touching Michelin, Allan Michael Korres, Georgios Ba’ara, Sara Assadi, Hadi Alsuradi, Haneen Sayegh, Rony R. Argyros, Antonis Eid, Mohamad Front Robot AI Robotics and AI Most people touch their faces unconsciously, for instance to scratch an itch or to rest one’s chin in their hands. To reduce the spread of the novel coronavirus (COVID-19), public health officials recommend against touching one’s face, as the virus is transmitted through mucous membranes in the mouth, nose and eyes. Students, office workers, medical personnel and people on trains were found to touch their faces between 9 and 23 times per hour. This paper introduces FaceGuard, a system that utilizes deep learning to predict hand movements that result in touching the face, and provides sensory feedback to stop the user from touching the face. The system utilizes an inertial measurement unit (IMU) to obtain features that characterize hand movement involving face touching. Time-series data can be efficiently classified using 1D-Convolutional Neural Network (CNN) with minimal feature engineering; 1D-CNN filters automatically extract temporal features in IMU data. Thus, a 1D-CNN based prediction model is developed and trained with data from 4,800 trials recorded from 40 participants. Training data are collected for hand movements involving face touching during various everyday activities such as sitting, standing, or walking. Results showed that while the average time needed to touch the face is 1,200 ms, a prediction accuracy of more than 92% is achieved with less than 550 ms of IMU data. As for the sensory response, the paper presents a psychophysical experiment to compare the response time for three sensory feedback modalities, namely visual, auditory, and vibrotactile. Results demonstrate that the response time is significantly smaller for vibrotactile feedback (427.3 ms) compared to visual (561.70 ms) and auditory (520.97 ms). Furthermore, the success rate (to avoid face touching) is also statistically higher for vibrotactile and auditory feedback compared to visual feedback. These results demonstrate the feasibility of predicting a hand movement and providing timely sensory feedback within less than a second in order to avoid face touching. Frontiers Media S.A. 2021-04-08 /pmc/articles/PMC8060563/ /pubmed/33898529 http://dx.doi.org/10.3389/frobt.2021.612392 Text en Copyright © 2021 Michelin, Korres, Ba’ara, Assadi, Alsuradi, Sayegh, Argyros and Eid. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Michelin, Allan Michael
Korres, Georgios
Ba’ara, Sara
Assadi, Hadi
Alsuradi, Haneen
Sayegh, Rony R.
Argyros, Antonis
Eid, Mohamad
FaceGuard: A Wearable System To Avoid Face Touching
title FaceGuard: A Wearable System To Avoid Face Touching
title_full FaceGuard: A Wearable System To Avoid Face Touching
title_fullStr FaceGuard: A Wearable System To Avoid Face Touching
title_full_unstemmed FaceGuard: A Wearable System To Avoid Face Touching
title_short FaceGuard: A Wearable System To Avoid Face Touching
title_sort faceguard: a wearable system to avoid face touching
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8060563/
https://www.ncbi.nlm.nih.gov/pubmed/33898529
http://dx.doi.org/10.3389/frobt.2021.612392
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