Determination of workers' compliance to safety regulations using a spatio-temporal graph convolution network

The safety of workers in construction remains a critical issue despite the automation of several tasks with fewer workers on site. As fatal accidents of workers account for a significant number of construction accidents, considerable effort has been made to monitor workers’ safety behaviors with additional personnel for supervising workers. With the advancement of data analytics, recent research has reported various human activity recognition methods based on image data to perform automated worker monitoring without additional labor. Nevertheless, unlike existing approaches based on a single image, a method that can capture a series of actions from sequential images is required to monitor workers’ compliance with safety behavior. To this end, an approach based on OpenPose and a spatio-temporal graph convolutional network is proposed in this study to evaluate workers’ compliance with safety regulations using sequential videos. The two primary functions of the developed method include 1) classifying each safety behavior among five representative behaviors stipulated in construction, and 2) determining the compliance of workers with each safety regulation. The results indicate that the developed approach can capture momentary safety behaviors and workers’ compliance with feasible accuracy of an average F1 score greater than 0.8. Furthermore, the proposed method can be extended to safety intervention policies with behavior-based feedback to inform workers of their non-compliance with safety behaviors. Therefore, this study contributes to proactive safety management by focusing on workers’ behavioral levels rather than on accident rate-based management.