Intelligent Safety Helmet For Miners Using Arduino Leveraging Support Vector Machines

In the fast-evolving era of mining and development, ensuring the safety and well-being of miners remains paramount. Recent advancements in the Internet of Things (IoT) have paved the way for innovative solutions in mining techniques. This paper introduces an intelligent mining helmet equipped with Arduino UNO, ESP8266 wireless connectivity, and an array of sensors (gas, temperature, humidity, and ultrasonic). These sensors monitor environmental conditions in real-time, coupled with an alarm system to enhance miner safety and operational efficiency. This implementation of the safety helmet employs a multi-approach methodology, integrating support vector machines (SVMs) for predictive analysis of hazardous conditions. The SVM model is trained on historical data to predict potential risks based on current sensor readings and Our Model got a very good accuracy. The intelligent helmet continuously checks environmental conditions, and in the event of potential hazards, such as high gas levels or elevated temperatures, it alerts miners with a buzzer, prompting immediate evacuation to prevent accidents. The results demonstrate that the intelligent helmet significantly improves safety by reducing the response time to hazardous conditions. Additionally, it optimizes working conditions through real-time monitoring and predictive maintenance by analyzing environmental trends. Such an SVM-based predictive model is very important in minimizing false alarms and enhancing the reliability of the system.