Real‐time ergonomic risk assessment in construction using a co‐learning‐powered 3D human pose estimation model

Abstract

AbstractWork‐related musculoskeletal disorders pose significant health risks to construction workers, making it essential to monitor their postures and identify physical exposure to mitigate these risks. This study presents a novel framework for real‐time ergonomic risk assessment of workers in construction environments. Specifically, this study develops a lightweight human pose estimation (HPE) model with a residual log‐likelihood estimation head and adopts pose‐tracking technology to enable real‐time recognition of workers’ three‐dimensional (3D) postures. In particular, this study proposes a novel co‐learning method that enables the HPE model to learn two‐dimensional (2D) and 3D features from multi‐dimension datasets simultaneously, substantially enhancing the model's ability to capture 3D postures from 2D images. The proposed framework facilitates real‐time ergonomic risk assessment, reducing potential risks to construction workers and offering promising practical applications.