A Human Activity Determination Predicting Abnormality for Mining Field Workers

In numerous countries, over 50% of the workforce is engaged in the informal sector, lacking social protection for healthcare and facing a lack of regulatory enforcement for occupational health and safety standards. The impact of employment and working conditions on health equity is substantial, as the health of workers is fundamental for household income, productivity, and overall economic development. Workplace health risks, such as oxidation problems, body heat issues, and the risk of falls in highrisk zones, underscore the need for vigilant monitoring. The conditions of employment, occupation, and hierarchical position also play a crucial role in influencing health outcomes. This project aims to address these challenges by implementing a solution where worker conditions, including oxidation levels, heart rate, temperature, and position, are continually monitored. A machine learning model deployed on a chip analyzes this data to predict the worker's condition. An independent mobile app has been developed to promptly notify users of any abnormalities, providing an efficient worker monitoring system that contributes to saving lives.