Advancements in sensing technology and artificial intelligence have revolutionized industrial settings by introducing robots that work alongside humans, enhancing productivity and flexibility. However, ensuring safety in human-robot interactions has become more challenging. Established safety standards emphasize risk assessment, protective measures, and real-time monitoring systems, where safety complexities arise from intricate industrial interactions. The study focuses on "Speed and Separation Monitoring" (SSM), a collaborative type defined by ISO/TS 15066. This thesis aims to address unknowns within SSM, particularly on the parameter accounting for the robot system to respond to the operator's presence, crucial for decision-making on speed and separation limits. A proximity sensor was utilized to assess the overall delay of a classic industrial network between the sensing node for the operator detection (AI-based vision system) and the triggering of the safety node to the robot.