Preventing work-related musculoskeletal disorders (WMSDs) is crucial in reducing their impact on individuals and society. However, the existing mainstream 2D image-based approach is insufficient in capturing the complex 3D movements and postures involved in many occupational tasks. To address this, an improved deep learning-based rapid entire body assessment (REBA) method has been proposed. The method takes working videos as input and automatically outputs the corresponding REBA score through 3D pose reconstruction. The proposed method achieves an average precision of 94.7% on real-world data, which is comparable to that of ergonomic experts. Furthermore, the method has the potential to be applied across a wide range of industries as it has demonstrated good generalisation in multiple scenarios. The proposed method offers a promising solution for automated and accurate risk assessment of WMSDs, with implications for various industries to ensure the safety and well-being of workers.
Keywords: 3D pose reconstruction; Deep learning; Improved REBA; Musculoskeletal disorders; Various industries.
This paper proposes a deep learning-based improved rapid entire body assessment (REBA) method for assessing work-related musculoskeletal disorders (WMSDs) risks using 3D pose reconstruction from videos, achieving 94.7% precision, comparable to ergonomic experts, with potential applications across various industries.