A novel approach for monocular RGB-based ergonomics monitoring in industrial workspaces employing synthetic datasets to train a deep learning model

Physical ergonomics is critical for worker well-being, safety, and productivity, in Industry 4.0 and in the emerging Industry 5.0 paradigm, which emphasize human-machine collaboration. Advances in motion capture technologies, especially marker-less systems using RGB cameras, offer new possibilities for real-time ergonomic assessments. These systems effectively address the intrusiveness issues inherent to marker-based approaches and partially overcome challenges such as lighting variations, occlusions and calibration commonly associated with RGB-D camera-based systems. Nonetheless, their accuracy can be suboptimal due to limitations arising from perspective distortions. To overcome this issue, the present paper introduces an “inductive” RGB camera-based approach to ergonomic assessment, leveraging Deep Learning models trained on a synthetic dataset of 14,400 unique postures captured from 24 viewpoints in Unity 3D. Unlike traditional methods reliant on measured angles, this approach predicts ergonomic evaluations directly from posture observations. We implemented the proposed approach on a prototypal system that allowed us to experiment and collect quantitative results. We analysed the system’s performance through comparative testing, juxtaposing predictions from our system with measurements obtained from an inertial system worn by individuals while performing tasks in a controlled laboratory environment. Experimentations highlighted the satisfactory performance of the proposed inductive approach and related technology, highlighting its potential for delivering more accurate real-time ergonomic assessments.