Assessing Physical Ergonomics in Industry 5.0: A Preliminary Deep Learning-Based Approach

Musculoskeletal disorders are frequent workplace injuries, especially during manual lifting activities. They are influenced by posture, lifting technique, and repetitive movements. Various ergonomic assessment methods exist, but each has limitations: observational methods can be slow and prone to error, while contact sensor-based methods, although more accurate, tend to be invasive and expensive. Recent developments have focused on non-contact sensors, such as RGB and RGB-D cameras, combined with Deep Learning algorithms and observational methods, to improve efficiency and reliability. This study proposes a solution combining a skeleton-based Deep Learning algorithm for Human Pose Estimation with an observational method for postural assessment. Using an RGB camera, four lifting techniques (stoop, squat, semi-squat, and weightlifter) were analyzed, evaluating their impact on worker posture through the REBA score. Among handle-assisted lifts, the stoop and weightlifter techniques showed the lowest average maximum REBA scores (5.375 and 6.125), while the squat and semi-squat techniques scored highest at 7. The semi-squat without handles showed the greatest postural risk (7.875). Future work will integrate 3D data and validate the approach with a larger, more diverse population.