This study presents ME-WARD (Multimodal Ergonomic Workplace Assessment and Risk from Data), a novel system for ergonomic assessment and musculoskeletal risk evaluation that implements the Rapid Upper Limb Assessment (RULA) method. ME-WARD is designed to process joint angle data from motion capture systems, including inertial measurement unit (IMU)-based setups, and deep learning human body pose tracking models. The tool's flexibility enables ergonomic risk assessment using any system capable of reliably measuring joint angles, extending the applicability of RULA beyond proprietary setups. To validate its performance, the tool was tested in an industrial setting during the assembly of conveyor belts, which involved high-risk tasks such as inserting rods and pushing conveyor belt components. The experiments leveraged gold standard IMU systems alongside a state-of-the-art monocular 3D pose estimation system. The results confirmed that ME-WARD produces reliable RULA scores that closely align with IMU-derived metrics for flexion-dominated movements and comparable performance with the monocular system, despite limitations in tracking lateral and rotational motions. This work highlights the potential of integrating multiple motion capture technologies into a unified and accessible ergonomic assessment pipeline. By supporting diverse input sources, including low-cost video-based systems, the proposed multimodal approach offers a scalable, cost-effective solution for ergonomic assessments, paving the way for broader adoption in resource-constrained industrial environments.

翻译：本研究提出ME-WARD（多模态工效学工作场所评估与数据风险分析系统），这是一种用于工效学评估与肌肉骨骼风险分析的新型系统，实现了快速上肢评估（RULA）方法。ME-WARD设计用于处理来自动作捕捉系统的关节角度数据，包括基于惯性测量单元（IMU）的装置以及深度学习人体姿态跟踪模型。该工具的灵活性使其能够利用任何可可靠测量关节角度的系统进行工效学风险评估，从而将RULA的适用性扩展到专有系统之外。为验证其性能，该工具在传送带组装的工业场景中进行了测试，该场景涉及插入杆件和推动传送带组件等高风险任务。实验采用黄金标准的IMU系统与先进的单目三维姿态估计系统进行对比验证。结果表明，尽管在侧向和旋转运动跟踪方面存在局限，ME-WARD生成的RULA评分与IMU导出的屈曲主导运动指标高度吻合，且与单目系统性能相当。这项工作凸显了将多种动作捕捉技术整合到统一且易用的工效学评估流程中的潜力。通过支持包括低成本视频系统在内的多样化输入源，所提出的多模态方法为工效学评估提供了可扩展、成本效益高的解决方案，为在资源受限的工业环境中更广泛的应用铺平了道路。