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

Abstract

<p>Work-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.</p>