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Are You Wearing a Mask? Detecting If a Person Wears a Mask Using a Wristband
Coronavirus 2019 (COVID-19) has posed a serious threat to the lives and health of the majority of people worldwide. Since the early days of the outbreak, South Korea’s government and citizens have made persistent efforts to provide effective prevention against further spread of the disease. In parti...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8915108/ https://www.ncbi.nlm.nih.gov/pubmed/35270893 http://dx.doi.org/10.3390/s22051745 |
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author | Msigwa, Constantino Baek, Seungwoo Bernard, Denis Yun, Jaeseok |
author_facet | Msigwa, Constantino Baek, Seungwoo Bernard, Denis Yun, Jaeseok |
author_sort | Msigwa, Constantino |
collection | PubMed |
description | Coronavirus 2019 (COVID-19) has posed a serious threat to the lives and health of the majority of people worldwide. Since the early days of the outbreak, South Korea’s government and citizens have made persistent efforts to provide effective prevention against further spread of the disease. In particular, the participation of individual citizens in complying with the necessary code of conduct to prevent spread of the infection, through measures such as social distancing and mask wearing, is as instrumental as the geographical tracking of the trajectory of the infected. In this paper, we propose an activity recognition method based on a wristband equipped with an IR array and inertial measurement unit (IMU) to detect individual compliance with codes of personal hygiene management, such as mask wearing, which are recommended to prevent the spread of infectious diseases. The results of activity recognition were comparatively analyzed by applying conventional machine learning algorithms and convolutional neural networks (CNNs) to the IMU time series and IR array thermal images collected from 25 subjects. When CNN and 24 × 32 thermal images were used, 97.8% accuracy was achieved (best performance), and when 6 × 8 low-resolution thermal images were used, similar performance with 97.1% accuracy was obtained. In the case of using IMU, the performance of activity recognition was lower than that obtained with the IR array, but an accuracy of 93% was achieved even in the case of applying machine learning algorithms, indicating that it is more suitable for wearable devices with low computational capability. |
format | Online Article Text |
id | pubmed-8915108 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89151082022-03-12 Are You Wearing a Mask? Detecting If a Person Wears a Mask Using a Wristband Msigwa, Constantino Baek, Seungwoo Bernard, Denis Yun, Jaeseok Sensors (Basel) Article Coronavirus 2019 (COVID-19) has posed a serious threat to the lives and health of the majority of people worldwide. Since the early days of the outbreak, South Korea’s government and citizens have made persistent efforts to provide effective prevention against further spread of the disease. In particular, the participation of individual citizens in complying with the necessary code of conduct to prevent spread of the infection, through measures such as social distancing and mask wearing, is as instrumental as the geographical tracking of the trajectory of the infected. In this paper, we propose an activity recognition method based on a wristband equipped with an IR array and inertial measurement unit (IMU) to detect individual compliance with codes of personal hygiene management, such as mask wearing, which are recommended to prevent the spread of infectious diseases. The results of activity recognition were comparatively analyzed by applying conventional machine learning algorithms and convolutional neural networks (CNNs) to the IMU time series and IR array thermal images collected from 25 subjects. When CNN and 24 × 32 thermal images were used, 97.8% accuracy was achieved (best performance), and when 6 × 8 low-resolution thermal images were used, similar performance with 97.1% accuracy was obtained. In the case of using IMU, the performance of activity recognition was lower than that obtained with the IR array, but an accuracy of 93% was achieved even in the case of applying machine learning algorithms, indicating that it is more suitable for wearable devices with low computational capability. MDPI 2022-02-23 /pmc/articles/PMC8915108/ /pubmed/35270893 http://dx.doi.org/10.3390/s22051745 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Msigwa, Constantino Baek, Seungwoo Bernard, Denis Yun, Jaeseok Are You Wearing a Mask? Detecting If a Person Wears a Mask Using a Wristband |
title | Are You Wearing a Mask? Detecting If a Person Wears a Mask Using a Wristband |
title_full | Are You Wearing a Mask? Detecting If a Person Wears a Mask Using a Wristband |
title_fullStr | Are You Wearing a Mask? Detecting If a Person Wears a Mask Using a Wristband |
title_full_unstemmed | Are You Wearing a Mask? Detecting If a Person Wears a Mask Using a Wristband |
title_short | Are You Wearing a Mask? Detecting If a Person Wears a Mask Using a Wristband |
title_sort | are you wearing a mask? detecting if a person wears a mask using a wristband |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8915108/ https://www.ncbi.nlm.nih.gov/pubmed/35270893 http://dx.doi.org/10.3390/s22051745 |
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