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.