ME-WARD：一种多模态人体工学分析工具，用于从惯性与视频数据中评估肌肉骨骼风险
ME-WARD: A multimodal ergonomic analysis tool for musculoskeletal risk assessment from inertial and video data in working plac
AI总结：
ME-WARD是一种多模态工具，利用惯性和视频数据评估人体工学风险，通过整合多种运动捕捉技术提供可靠且经济的解决方案。
摘要：
本研究提出了ME-WARD（多模态工作场所评估与风险数据从属），一种新的用于人体工学评估和肌肉骨骼风险评估的系统，实现了快速上肢评估（RULA）方法。ME-WARD旨在处理来自运动捕捉系统的关节角度数据，包括基于惯性测量单元（IMU）的设置和深度学习人体姿态跟踪模型。该工具的灵活性使能够使用任何能够可靠测量关节角度的系统进行人体工学风险评估，从而将RULA方法扩展到非专有设置之外。为了验证其性能，该工具在工业环境中进行了测试，期间参与了传送带组装任务，其中包括高风险任务，如插入杆件和推动传送带组件。实验利用了黄金标准IMU系统以及最先进的单目3D姿态估计系统。结果证实，ME-WARD产生的RULA分数在屈曲主导的运动中与IMU衍生的指标紧密一致，并且在单目系统上表现出可比的性能，尽管在追踪侧向和旋转运动方面存在限制。这项工作突显了将多种运动捕捉技术整合到统一且可访问的人体工学评估流程中的潜力。通过支持多种输入源，包括低成本的视频系统，所提出的多模态方法提供了一种可扩展、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地采用人体工学评估铺平了道路。
英文摘要：
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.
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