Arduino Based Smart Helmet for Coal Mine Safety

Sawant, Pradeep Mahadev; Godse, Suraj; Thigale, Varad; Kasar, Krishnakant

doi:10.2139/ssrn.3645335

Cited by 6 publications

(3 citation statements)

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“…Generally, when Q h = 1 − PRESS(h) SS(h−1) < 0.0975, it indicates that the insertion of the hth new component has no significant improvement on the prediction ability of the model [54]. It is calculated that Q h < 0.0975 when h = 4, therefore only three principal components need to be extracted in this study, and the expression is shown in Formula (7).…”

Section: Extraction Of Principal Componentsmentioning

confidence: 95%

“…A smart helmet is a head-mounted intelligent device that combines Internet of things technology with an ordinary helmet to realize corresponding intelligent functions. Smart helmets are often used in industries such as counter-terrorism, fire protection, and mining [6][7][8]. In recent years, smart helmets used by construction workers have also emerged.…”

Section: Introductionmentioning

confidence: 99%

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

Cheng

et al. 2021

Mathematics

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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.

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Section: Extraction Of Principal Componentsmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

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

Cheng

et al. 2021

Mathematics

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“…[13].A specific alert is also necessary especially in mines for any emergency situations. In such cases, a panic button must be implemented for the to avoid in fatal situations [14].To be able to integrate all these sensors in a single platform like NODEMCU is a hard task. Node MCU turns out to be really efficient in such applications for integrating different types of sensors [15].Also, providing all this information to Blynk and storing it as well as its analyzing can all together provide an efficient and a complete smart safety helmet for miners.…”

Section: Literature Reviewmentioning

confidence: 99%

IOT Empowered Helmets: Pioneering Safety in the Mining Sector

Puja Cholke

2023

IJRITCC

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Mining sector is the most prominent to accidents. The accident rate in mining is about 87% .Mining activity involves a lot of tedious and time-consuming tasks as well. Out of the various risks associated with mining there exists many different types of hazardous gases like carbon monoxide, hydrogen sulphide,methane and excess of carbon dioxide. Mining also includes the risk of tremors and landslides as well. Along with that there is the danger of falling of large and bulky objects as well. On top of that removal of helmet is even more dangerous. Moreover ,monitoring each and every miner is also an important factor when it comes to mining. As a result there are lot of risks associated with the same. At times these risks can be life-threatening and result into fatal error .Lifesaving helmet for miners is an IOT based complete protection of miners. As the traditional model of smart helmet doesn’t take all the above mentioned safety threats into consideration our aim through this proposed system is to develop a complete safety equipment in the form of helmet to safeguard miners from the various dangers present in the mining industry.

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