Cargando…
Intelligent IoT (IIoT) Device to Identifying Suspected COVID-19 Infections Using Sensor Fusion Algorithm and Real-Time Mask Detection Based on the Enhanced MobileNetV2 Model
This paper employs a unique sensor fusion (SF) approach to detect a COVID-19 suspect and the enhanced MobileNetV2 model is used for face mask detection on an Internet-of-Things (IoT) platform. The SF algorithm avoids incorrect predictions of the suspect. Health data are continuously monitored and re...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8955349/ https://www.ncbi.nlm.nih.gov/pubmed/35326932 http://dx.doi.org/10.3390/healthcare10030454 |
_version_ | 1784676314969538560 |
---|---|
author | Shinde, Rupali Kiran Alam, Md. Shahinur Park, Seong Gyoon Park, Sang Myeong Kim, Nam |
author_facet | Shinde, Rupali Kiran Alam, Md. Shahinur Park, Seong Gyoon Park, Sang Myeong Kim, Nam |
author_sort | Shinde, Rupali Kiran |
collection | PubMed |
description | This paper employs a unique sensor fusion (SF) approach to detect a COVID-19 suspect and the enhanced MobileNetV2 model is used for face mask detection on an Internet-of-Things (IoT) platform. The SF algorithm avoids incorrect predictions of the suspect. Health data are continuously monitored and recorded on the ThingSpeak cloud server. When a COVID-19 suspect is detected, an emergency email is sent to healthcare personnel with the GPS position of the suspect. A lightweight and fast deep learning model is used to recognize appropriate mask positioning; this restricts virus transmission. When tested with the real-world masked face dataset (RMFD) dataset, the enhanced MobileNetV2 neural network is optimal for Raspberry Pi. Our IoT device and deep learning model are 98.50% (compared to commercial devices) and 99.26% accurate, respectively, and the time required for face mask evaluation is 31.1 milliseconds. The proposed device is useful for remote monitoring of covid patients. Thus, the method will find medical application in the detection of COVID-19-positive patients. The device is also wearable. |
format | Online Article Text |
id | pubmed-8955349 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89553492022-03-26 Intelligent IoT (IIoT) Device to Identifying Suspected COVID-19 Infections Using Sensor Fusion Algorithm and Real-Time Mask Detection Based on the Enhanced MobileNetV2 Model Shinde, Rupali Kiran Alam, Md. Shahinur Park, Seong Gyoon Park, Sang Myeong Kim, Nam Healthcare (Basel) Article This paper employs a unique sensor fusion (SF) approach to detect a COVID-19 suspect and the enhanced MobileNetV2 model is used for face mask detection on an Internet-of-Things (IoT) platform. The SF algorithm avoids incorrect predictions of the suspect. Health data are continuously monitored and recorded on the ThingSpeak cloud server. When a COVID-19 suspect is detected, an emergency email is sent to healthcare personnel with the GPS position of the suspect. A lightweight and fast deep learning model is used to recognize appropriate mask positioning; this restricts virus transmission. When tested with the real-world masked face dataset (RMFD) dataset, the enhanced MobileNetV2 neural network is optimal for Raspberry Pi. Our IoT device and deep learning model are 98.50% (compared to commercial devices) and 99.26% accurate, respectively, and the time required for face mask evaluation is 31.1 milliseconds. The proposed device is useful for remote monitoring of covid patients. Thus, the method will find medical application in the detection of COVID-19-positive patients. The device is also wearable. MDPI 2022-02-28 /pmc/articles/PMC8955349/ /pubmed/35326932 http://dx.doi.org/10.3390/healthcare10030454 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 Shinde, Rupali Kiran Alam, Md. Shahinur Park, Seong Gyoon Park, Sang Myeong Kim, Nam Intelligent IoT (IIoT) Device to Identifying Suspected COVID-19 Infections Using Sensor Fusion Algorithm and Real-Time Mask Detection Based on the Enhanced MobileNetV2 Model |
title | Intelligent IoT (IIoT) Device to Identifying Suspected COVID-19 Infections Using Sensor Fusion Algorithm and Real-Time Mask Detection Based on the Enhanced MobileNetV2 Model |
title_full | Intelligent IoT (IIoT) Device to Identifying Suspected COVID-19 Infections Using Sensor Fusion Algorithm and Real-Time Mask Detection Based on the Enhanced MobileNetV2 Model |
title_fullStr | Intelligent IoT (IIoT) Device to Identifying Suspected COVID-19 Infections Using Sensor Fusion Algorithm and Real-Time Mask Detection Based on the Enhanced MobileNetV2 Model |
title_full_unstemmed | Intelligent IoT (IIoT) Device to Identifying Suspected COVID-19 Infections Using Sensor Fusion Algorithm and Real-Time Mask Detection Based on the Enhanced MobileNetV2 Model |
title_short | Intelligent IoT (IIoT) Device to Identifying Suspected COVID-19 Infections Using Sensor Fusion Algorithm and Real-Time Mask Detection Based on the Enhanced MobileNetV2 Model |
title_sort | intelligent iot (iiot) device to identifying suspected covid-19 infections using sensor fusion algorithm and real-time mask detection based on the enhanced mobilenetv2 model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8955349/ https://www.ncbi.nlm.nih.gov/pubmed/35326932 http://dx.doi.org/10.3390/healthcare10030454 |
work_keys_str_mv | AT shinderupalikiran intelligentiotiiotdevicetoidentifyingsuspectedcovid19infectionsusingsensorfusionalgorithmandrealtimemaskdetectionbasedontheenhancedmobilenetv2model AT alammdshahinur intelligentiotiiotdevicetoidentifyingsuspectedcovid19infectionsusingsensorfusionalgorithmandrealtimemaskdetectionbasedontheenhancedmobilenetv2model AT parkseonggyoon intelligentiotiiotdevicetoidentifyingsuspectedcovid19infectionsusingsensorfusionalgorithmandrealtimemaskdetectionbasedontheenhancedmobilenetv2model AT parksangmyeong intelligentiotiiotdevicetoidentifyingsuspectedcovid19infectionsusingsensorfusionalgorithmandrealtimemaskdetectionbasedontheenhancedmobilenetv2model AT kimnam intelligentiotiiotdevicetoidentifyingsuspectedcovid19infectionsusingsensorfusionalgorithmandrealtimemaskdetectionbasedontheenhancedmobilenetv2model |