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

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.

Continuous monitoring of body temperature for objective detection of health and safety risks in construction sites: An analysis of the accuracy and comfort of off-the-shelf wearable sensors

“…Lastly, for infectious diseases, there is one past study in the construction domain that attempted to continuously measure body temperature to detect symptoms of COVID-19 [ 33 ]. They used a simulated construction experiment and measured the forehead temperature continuously using a helmet with a non-contact infrared temperature sensor.…”

A Smart Airport Mobile Application Concept and Possibilities of Its Use for Predictive Modeling and Analysis

“…Testing of the mobile application as part of the process of its creation is an important phase that serves to verify functionality, detect errors, and ensure a high-quality standard of the application before it is put into operation. One of the options for testing a mobile application is user session testing, which is a procedure that examines the user's behavior in real-time while interacting with the application [27,28]. User session testing makes it possible to gain useful knowledge about how users behave and how they interact with the application in different scenarios.…”

“…Based on these insights, development and design teams can optimize the user interface, fix bugs, and improve the overall usability and user experience of the mobile app. Integrating comments and feedback from testers into the final version of the app is key to achieving a quality and competitive mobile app that is acceptable and functional for users [28,29].…”

“…Data collection and analysis of data from mobile applications have become an important source of information for airport operators. The data is collected from mobile apps, websites, advanced parking reservations, online passenger feedback, social media, and Wi-Fi analytics [28,29]. The ability to combine online shopping with offline retail can become one of the greatest strengths of airports [30].…”

Preface to the Special Issue on “Numerical Computation, Data Analysis and Software in Mathematics and Engineering”

“…With the spread of COVID-19 in the world, the implementation of personnel health monitoring in construction sites has become an important problem in construction management. The paper submitted to this Special Issue by Li et al [22] develops a smart helmet equipped with an infrared temperature sensor and conducts a simulated construction experiment to collect data of temperature and its influencing factors in indoor and outdoor construction operation environments. A partial least-square-back propagation neural network (PLS-BPNN) model is presented to correct the temperature measurement results of the smart helmet.…”