Center-of-Gravity-Aware Graph Convolution for Unsafe Behavior Recognition of Construction Workers.

Falls from height are a critical safety concern in the construction industry, underscoring the need for effective identification of high-risk worker behaviors near hazardous edges for proactive accident prevention. This study aimed to address this challenge by developing an improved action recognition model. We propose a novel dynamic spatio-temporal graph convolutional network (CoG-STGCN) that incorporates a center of gravity (CoG)-aware mechanism. The method computes global and local CoG using anthropometric priors and extracts four key dynamic CoG features, which a Multi-Layer Perceptron (MLP) then uses to generate modulation weights that dynamically adjust the skeleton graph's adjacency matrix, enhancing sensitivity to stability changes. On a self-constructed dataset of eight typical edge-related hazardous behaviors, CoG-STGCN achieved a Top-1 accuracy of 95.83% (baseline ST-GCN: 93.75%) and an average accuracy of 94.17% in fivefold cross-validation (baseline ST-GCN: 92.91%), with significant improvements in recognizing actions involving rapid CoG shifts. The CoG-STGCN provides a more effective and physically informed approach for intelligent unsafe behavior recognition and early warning in built environments.