Safety in industrial environments, such as construction and factories, is a daily concern for companies, since it directly affects the workers and reputation. One existing approach to improve safety in Industry 4.0 is the integration of the Internet of Things (IoT) and Decision-Making methods, enabling continuous monitoring and risk situation detection. However, it is necessary to deploy these modern solutions in the usual equipment of workers without harming their daily tasks. Within this context, this paper presents an integrated solution comprising a monitoring IoT-helmet and edge computing, which increases occupational safety and reduces response time in risk situations. The IoT-helmet collects data about the current status of the worker (including eye movement, body position, and others), while the station consolidates and displays this information for alert situations (such as falling, drowsiness, fainting, etc.) detected by AI models. This paper presents preliminary results of the development of the solution, encompassing the experiments related to posture monitoring and events of falls and drowsiness. The results indicate the capacity of the solution to identify risk situations with a suitable response time.