Machine Learning Based Smart Helmet for Air Quality and Hazardous Event Detection in the Mining Industry

Ensuring the safety of underground miners is of utmost importance in the mining industry, given the devastating consequences of mining accidents. To address this concern, this research study has developed an intelligent headgear equipped with an array of sensors to continuously monitor the working conditions of miners. This advanced device is engineered to identify critical parameters such as temperature, humidity, concentrations of harmful gases, and vibrations. It is equipped with dedicated sensors for each of these parameters, and it promptly transmits real-time data to a centralized control centre. The control centre can analyse this information and take immediate actions, which may include worker evacuation or improvements to working conditions. The proposed system utilizes an Arduino microcontroller for processing the data from these sensors and then transmits it via a Wi-Fi module. The proposed model has utilized Cloud-based loT and machine learning algorithms to enhance the accuracy of the sensor readings. Specifically, an Artificial Neural Network (ANN) algorithm is employed to train a dataset of sensor information stored in CS V format. This innovative system has the potential to significantly elevate safety standards and minimize the risk of mining accidents.