Estimating the level of risk that an operator might be exposed to is essential in industrial settings, especially in the age of Industry 4.0, where the availability of sensors and the Internet of Things is spreading. A real-time assessment of the risk is required to safeguard worker health and safety, prevent accidents, and reduce the likelihood of occupational diseases. This thesis centers on developing a decision-making support system that employs fuzzy logic to analyze a range of data gathered from diverse types of sensors - such as physiological, environmental, and manufacturing process data - to estimate and characterize potential risks to operators in hazardous manufacturing settings.