From Object Detection to Safety-State Assessment: A Worker-Centered Systems Framework for Continuous PPE Compliance Monitoring in Industry 4.0 Workplaces

Industry 4.0 increasingly requires intelligent safety monitoring systems capable of continuously recognizing and assessing workers’ safety status in complex industrial environments. In settings where multiple types of personal protective equipment (PPE) must be worn simultaneously, object detection alone is insufficient to determine whether individual workers are actually wearing the required PPE and how their wearing status changes over time. This study proposes a worker-centered systems framework that extends object detection toward continuous PPE safety-state assessment. The proposed framework integrates worker tracking, pose-based region-of-interest (ROI) generation, worker–PPE matching, temporal correction using an Ensemble Kalman Filter, and final wearing-status determination. The proposed framework was evaluated using work videos containing transitions between wearing and non-wearing states and visual disturbances such as sparks, smoke, and partial occlusions. Experimental results showed overall wear and non-wear precision values of 0.992 and 0.991, respectively, while recall for the non-wearing state reached 0.937 under visually challenging conditions. The framework processed an average of 41.83 frames per second. These results show that integrating spatial worker–PPE relationships with temporal state information extends object-level PPE detection toward continuous worker-level safety-state assessment. The proposed framework provides an integrated systems approach to worker-centered PPE compliance monitoring in Industry 4.0 workplaces.