A systematic literature review of computer vision-based biomechanical models for physical workload estimation.

Ergonomic risks, driven by strenuous physical demands in complex work settings, are prevalent across industries. Addressing these challenges through detailed assessment and effective interventions enhances safety and employee well-being. Proper and timely measurement of physical workloads is the initial step towards holistic ergonomic control. This study comprehensively explores existing computer vision-based biomechanical analysis methods for workload assessment, assessing their performance against traditional techniques, and categorising them for easier use. Recent strides in artificial intelligence have revolutionised workload assessment, especially in realistic work settings where conventional methods fall short. However, understanding the accuracy, characteristics, and practicality of computer vision-based methods versus traditional approaches remains limited. To bridge this knowledge gap, a literature review along with a meta-analysis was completed in this study to illuminate model accuracy, advantages, and challenges, offering valuable insights for refined technology implementation in diverse work environments.