Understanding Human Behaviors and Injury Factors in Underground Mines using Data Analytics

This study aims to understand human behaviors and associated injury causing factors in underground mines using data analytics of historical mining data. Decision tree and association rule were used to provide a statistical analysis of leading factors of hazards in underground mines. Based on the results, we were able to explore hazard feature identification using image feature recognition aiming to provide real-time monitoring for miners to secure healthy and safety operation via wearable computing.