🤖 AI Summary
This study addresses the limitations of traditional ergonomics assessments, which rely on specialized equipment and lack compatibility with multimodal motion data, hindering deployment in resource-constrained settings. To overcome these challenges, the authors propose a multimodal upper-limb musculoskeletal risk assessment system based on the Rapid Upper Limb Assessment (RULA) method, integrating inertial measurement units (IMUs) and monocular video-based human pose estimation. This approach enables, for the first time, universal support for heterogeneous motion capture data—including low-cost video streams. The system combines deep learning–based pose estimation, IMU-derived joint angle computation, RULA scoring, and multimodal fusion. Evaluated in a conveyor-belt assembly task, it demonstrates high agreement with IMU-based ground truth RULA scores and robust performance using only monocular video input, offering both high reliability and scalability.