A Smart Helmet-Based PLS-BPNN Error Compensation Model for Infrared Body Temperature Measurement of Construction Workers during COVID-19

In the context of the long-term coexistence between COVID-19 and human society, the implementation of personnel health monitoring in construction sites has become one of the urgent needs of current construction management. The installation of infrared temperature sensors on the helmets required to b...

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Autores principales:	Li Li, Jiahui Yu, Hang Cheng, Miaojuan Peng
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	personnel health monitoring construction site management smart helmet infrared temperature measurement temperature error compensation BP neural network Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/2216cd70afeb4898aa995a9417cb0e3d
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id	oai:doaj.org-article:2216cd70afeb4898aa995a9417cb0e3d
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spelling	oai:doaj.org-article:2216cd70afeb4898aa995a9417cb0e3d2021-11-11T18:20:29ZA Smart Helmet-Based PLS-BPNN Error Compensation Model for Infrared Body Temperature Measurement of Construction Workers during COVID-1910.3390/math92128082227-7390https://doaj.org/article/2216cd70afeb4898aa995a9417cb0e3d2021-11-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/21/2808https://doaj.org/toc/2227-7390In the context of the long-term coexistence between COVID-19 and human society, the implementation of personnel health monitoring in construction sites has become one of the urgent needs of current construction management. The installation of infrared temperature sensors on the helmets required to be worn by construction personnel to track and monitor their body temperature has become a relatively inexpensive and reliable means of epidemic prevention and control, but the accuracy of measuring body temperature has always been a problem. This study developed a smart helmet equipped with an infrared temperature sensor and conducted a simulated construction experiment to collect data of temperature and its influencing factors in indoor and outdoor construction operation environments. Then, a Partial Least Square–Back Propagation Neural Network (PLS-BPNN) temperature error compensation model was established to correct the temperature measurement results of the smart helmet. The temperature compensation effects of different models were also compared, including PLS-BPNN with Least Square Regression (LSR), Partial Least Square Regression (PLSR), and single Back Propagation Neural Network (BPNN) models. The results showed that the PLS-BPNN model had higher accuracy and reliability, and the determination coefficient of the model was 0.99377. After using PLS-BPNN model for compensation, the relative average error of infrared body temperature was reduced by 2.745 °C and RMSE was reduced by 0.9849. The relative error range of infrared body temperature detection was only 0.005~0.143 °C.Li LiJiahui YuHang ChengMiaojuan PengMDPI AGarticlepersonnel health monitoringconstruction site managementsmart helmetinfrared temperature measurementtemperature error compensationBP neural networkMathematicsQA1-939ENMathematics, Vol 9, Iss 2808, p 2808 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	personnel health monitoring construction site management smart helmet infrared temperature measurement temperature error compensation BP neural network Mathematics QA1-939
spellingShingle	personnel health monitoring construction site management smart helmet infrared temperature measurement temperature error compensation BP neural network Mathematics QA1-939 Li Li Jiahui Yu Hang Cheng Miaojuan Peng A Smart Helmet-Based PLS-BPNN Error Compensation Model for Infrared Body Temperature Measurement of Construction Workers during COVID-19
description	In the context of the long-term coexistence between COVID-19 and human society, the implementation of personnel health monitoring in construction sites has become one of the urgent needs of current construction management. The installation of infrared temperature sensors on the helmets required to be worn by construction personnel to track and monitor their body temperature has become a relatively inexpensive and reliable means of epidemic prevention and control, but the accuracy of measuring body temperature has always been a problem. This study developed a smart helmet equipped with an infrared temperature sensor and conducted a simulated construction experiment to collect data of temperature and its influencing factors in indoor and outdoor construction operation environments. Then, a Partial Least Square–Back Propagation Neural Network (PLS-BPNN) temperature error compensation model was established to correct the temperature measurement results of the smart helmet. The temperature compensation effects of different models were also compared, including PLS-BPNN with Least Square Regression (LSR), Partial Least Square Regression (PLSR), and single Back Propagation Neural Network (BPNN) models. The results showed that the PLS-BPNN model had higher accuracy and reliability, and the determination coefficient of the model was 0.99377. After using PLS-BPNN model for compensation, the relative average error of infrared body temperature was reduced by 2.745 °C and RMSE was reduced by 0.9849. The relative error range of infrared body temperature detection was only 0.005~0.143 °C.
format	article
author	Li Li Jiahui Yu Hang Cheng Miaojuan Peng
author_facet	Li Li Jiahui Yu Hang Cheng Miaojuan Peng
author_sort	Li Li
title	A Smart Helmet-Based PLS-BPNN Error Compensation Model for Infrared Body Temperature Measurement of Construction Workers during COVID-19
title_short	A Smart Helmet-Based PLS-BPNN Error Compensation Model for Infrared Body Temperature Measurement of Construction Workers during COVID-19
title_full	A Smart Helmet-Based PLS-BPNN Error Compensation Model for Infrared Body Temperature Measurement of Construction Workers during COVID-19
title_fullStr	A Smart Helmet-Based PLS-BPNN Error Compensation Model for Infrared Body Temperature Measurement of Construction Workers during COVID-19
title_full_unstemmed	A Smart Helmet-Based PLS-BPNN Error Compensation Model for Infrared Body Temperature Measurement of Construction Workers during COVID-19
title_sort	smart helmet-based pls-bpnn error compensation model for infrared body temperature measurement of construction workers during covid-19
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2216cd70afeb4898aa995a9417cb0e3d
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_version_	1718431870175150080

A Smart Helmet-Based PLS-BPNN Error Compensation Model for Infrared Body Temperature Measurement of Construction Workers during COVID-19

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