Vision-based progress compliance and worker health monitoring in concrete pouring and vibration

This paper proposes a vision-based framework named concrete progress and concrete health monitoring (CPCHM) for automated compliance supervision and health risk assessment during concrete pouring and vibration operations. The framework integrates YOLOv8-pose for worker posture estimation and YOLOv8-detection for equipment identification, achieving average accuracies of 92.8% and 99.5%, respectively. By fusing posture and equipment features, a support vector machine classifier distinguishes between pouring and vibration operations with 90.8% accuracy and an F1-score of 90.3%. Further, a spatiotemporal graph convolutional network is employed to model elbow joint dynamics and assess musculoskeletal risks, reaching a behavioral classification accuracy of 89.6%. To address occlusion and multiworker collaboration, CPCHM introduces a distance-based operator identification method and an adaptive region-of-interest inference mechanism, maintaining stable keypoint tracking and continuous elbow-angle estimation even under partial visibility. The framework is embedded on a Jetson nano edge device, which automatically triggers an acoustic buzzer alert when pouring durations exceed 90 min or vibration times fall outside the 5-15 s standard. CPCHM provides a compact, sensor-free, and scalable solution for integrating progress compliance monitoring and ergonomic health assessment, enabling intelligent and real-time supervision in dynamic concrete construction environments.