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

Li Li, Jiahui Yu, Hang Cheng and Miaojuan Peng
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Li Li: School of Mechanics and Engineering Science, Shanghai University, Shanghai 200044, China
Jiahui Yu: School of Mechanics and Engineering Science, Shanghai University, Shanghai 200044, China
Hang Cheng: School of Communication & Information Engineering, Shanghai University, Shanghai 200044, China
Miaojuan Peng: School of Mechanics and Engineering Science, Shanghai University, Shanghai 200044, China

Mathematics, 2021, vol. 9, issue 21, 1-20

Abstract: 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.

Keywords: personnel health monitoring; construction site management; smart helmet; infrared temperature measurement; temperature error compensation; BP neural network; COVID-19 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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