This study proposes an integrated ergonomic evaluation designed to identify unsafe postures, whereby postural risks during industrial work are assessed in the context of virtual reality-based smart manufacturing. Unsafe postures were recognised by identifying the displacements of the centre of mass (COM) of body keypoints using a computer vision-based deep learning (DL) convolutional neural network approach. The risk levels for the identified unsafe postures were calculated using ergonomic risk assessment tools rapid upper limb assessment and rapid whole-body assessment. An analysis of variance was conducted to determine significant differences between the vertical and horizontal directions of postural movements associated with the most unsafe postures. The findings assess the ergonomic risk levels and identify the most unsafe postures during industrial work in smart manufacturing using DL method. The identified postural risks can help industry managers and researchers acquire a better understanding of unsafe postures.
Keywords: Ergonomics; REBA; RULA; deep learning; virtual reality.
This study aims to identify unsafe postures and calculate risk levels in a VR-based smart manufacturing context. Deep learning is applied to identify unsafe postures by detecting COM displacements and risk levels are calculated using ergonomic risk assessment tools. Results revealed the most unsafe body postures, crucial for workers’ safety.