Automatic Gate for Body Temperature Check and Masks Wearing Compliance Using an Embedded System and Deep Learning
A new coronavirus variant known as n-Cov has emerged with a fast transmission rate. The World Health Organization (WHO) has declared the related disease or COVID-19 as a global pandemic that requires special handling. Many parties have shown efforts to reduce virus transmission by implementing healt...
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| Format: | UMS Journal (OJS) |
| Language: | eng |
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Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia
2022
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| Online Access: | https://journals.ums.ac.id/index.php/khif/article/view/15205 |
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| _version_ | 1805342480749559808 |
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| author | Kesuma, Rahman Indra Fernandes, Rivaldo Manullang, Martin Clinton Tosima |
| author_facet | Kesuma, Rahman Indra Fernandes, Rivaldo Manullang, Martin Clinton Tosima |
| author_sort | Kesuma, Rahman Indra |
| collection | OJS |
| description | A new coronavirus variant known as n-Cov has emerged with a fast transmission rate. The World Health Organization (WHO) has declared the related disease or COVID-19 as a global pandemic that requires special handling. Many parties have shown efforts to reduce virus transmission by implementing health protocols and adapting a new normal lifestyle. Implementation of the health protocol creates new problems, especially in the health check at the main entrance. The officers in charge of measuring body temperature are at risk of getting infected by COVID. Such a measurement is prone to errors. This study proposed a solution to build an automatic gate system that worked based on the new normal health protocol. The system utilizes the MLX90614 contactless temperature sensor to probe body temperature. It applies deep learning implementing the Convolutional Neural Network (CNN) algorithm with the MobileNetV2 architecture as a determinant of the conditions of wearing face masks. The system is equipped with an IoT-based remote controller to control the gate. Experimental results prove that the system works well. Temperature measurement takes a response time of 20 seconds for each user with 99% accuracy for the sensor and masks classification model. |
| format | UMS Journal (OJS) |
| id | oai:ojs2.journals.ums.ac.id:article-15205 |
| institution | Universitas Muhammadiyah Surakarta |
| language | eng |
| publishDate | 2022 |
| publisher | Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia |
| record_format | ojs |
| spelling | oai:ojs2.journals.ums.ac.id:article-15205 Automatic Gate for Body Temperature Check and Masks Wearing Compliance Using an Embedded System and Deep Learning Kesuma, Rahman Indra Fernandes, Rivaldo Manullang, Martin Clinton Tosima COVID-19; Gerbang Otomatis; Pemeriksanaan Suhu; Kelasifikasi Penggunaan Masker; CNN; MobileNetV2; MLX90614; A new coronavirus variant known as n-Cov has emerged with a fast transmission rate. The World Health Organization (WHO) has declared the related disease or COVID-19 as a global pandemic that requires special handling. Many parties have shown efforts to reduce virus transmission by implementing health protocols and adapting a new normal lifestyle. Implementation of the health protocol creates new problems, especially in the health check at the main entrance. The officers in charge of measuring body temperature are at risk of getting infected by COVID. Such a measurement is prone to errors. This study proposed a solution to build an automatic gate system that worked based on the new normal health protocol. The system utilizes the MLX90614 contactless temperature sensor to probe body temperature. It applies deep learning implementing the Convolutional Neural Network (CNN) algorithm with the MobileNetV2 architecture as a determinant of the conditions of wearing face masks. The system is equipped with an IoT-based remote controller to control the gate. Experimental results prove that the system works well. Temperature measurement takes a response time of 20 seconds for each user with 99% accuracy for the sensor and masks classification model. Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia COVID-19 face mask classification convolutional neural network MobileNetV2 MLX90614 2022-03-04 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://journals.ums.ac.id/index.php/khif/article/view/15205 10.23917/khif.v8i1.15205 Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika; Vol. 8 No. 1 April 2022; 42-51 Khazanah Informatika; Vol. 8 No. 1 April 2022; 42-51 2477-698X 2621-038X eng https://journals.ums.ac.id/index.php/khif/article/view/15205/7390 Copyright (c) 2022 Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika http://creativecommons.org/licenses/by/4.0 |
| spellingShingle | COVID-19; Gerbang Otomatis; Pemeriksanaan Suhu; Kelasifikasi Penggunaan Masker; CNN; MobileNetV2; MLX90614; Kesuma, Rahman Indra Fernandes, Rivaldo Manullang, Martin Clinton Tosima Automatic Gate for Body Temperature Check and Masks Wearing Compliance Using an Embedded System and Deep Learning |
| title | Automatic Gate for Body Temperature Check and Masks Wearing Compliance Using an Embedded System and Deep Learning |
| title_full | Automatic Gate for Body Temperature Check and Masks Wearing Compliance Using an Embedded System and Deep Learning |
| title_fullStr | Automatic Gate for Body Temperature Check and Masks Wearing Compliance Using an Embedded System and Deep Learning |
| title_full_unstemmed | Automatic Gate for Body Temperature Check and Masks Wearing Compliance Using an Embedded System and Deep Learning |
| title_short | Automatic Gate for Body Temperature Check and Masks Wearing Compliance Using an Embedded System and Deep Learning |
| title_sort | automatic gate for body temperature check and masks wearing compliance using an embedded system and deep learning |
| topic | COVID-19; Gerbang Otomatis; Pemeriksanaan Suhu; Kelasifikasi Penggunaan Masker; CNN; MobileNetV2; MLX90614; |
| topic_facet | COVID-19; Gerbang Otomatis; Pemeriksanaan Suhu; Kelasifikasi Penggunaan Masker; CNN; MobileNetV2; MLX90614; |
| url | https://journals.ums.ac.id/index.php/khif/article/view/15205 |
| work_keys_str_mv | AT kesumarahmanindra automaticgateforbodytemperaturecheckandmaskswearingcomplianceusinganembeddedsystemanddeeplearning AT fernandesrivaldo automaticgateforbodytemperaturecheckandmaskswearingcomplianceusinganembeddedsystemanddeeplearning AT manullangmartinclintontosima automaticgateforbodytemperaturecheckandmaskswearingcomplianceusinganembeddedsystemanddeeplearning |