Improved REBA: deep learning based rapid entire body risk assessment for prevention of musculoskeletal disorders
提出一种改进的深度学习REBA方法，通过3D姿态重建从工作视频自动输出风险评分，在真实数据上达到94.7%的平均精度，可替代专家评估，适用于多行业预防工作相关肌肉骨骼疾病。
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.