开源数字人建模框架，让小机构也能低成本做人体工学评估。
OPENJ: A Conceptual Framework for Open-Source Digital Human Modeling and Ergonomic Assessment in a CAD Environment
- 提出开放架构设计，整合人体模型、姿态预测与工学评估
- 支持CAD环境内进行姿势模拟与风险评分（如RULA）
- 适合科研、教育及中小企业替代昂贵商用软件
工业工作场所面临肌肉骨骼疾病、工位布局不合理、任务流程低效及人机匹配差等问题。数字人建模（DHM）工具通过在计算机辅助设计（CAD）环境中放置可扩展的虚拟人体模型，使工程师能基于标准化方法（如RULA、REBA、NIOSH提举方程、OWAS）评估人因风险，优化工位可达性与可视性，通过逆运动学预测任务姿势，并在实体实施前模拟操作。尽管自1980年代宾夕法尼亚大学Jack系统以来已发展四十余年，集成的人体模型、姿态预测、工学评估与CAD融合能力仍仅限于商业平台（如Siemens Tecnomatix Jack、Dassault DELMIA、Humanetics RAMSIS、Iowa Santos）。这些平台采用专有定价模式，其采购与运维成本及闭源特性已成为个人研究者、中小企和教育机构的实际障碍。无资源者只能依赖人工观察（纸质表单结合照片或视频），丧失计算分析的预测力与可复现性。本文作为（OpenJane/Joe）的设计蓝图，旨在推动后续开源实现与社区采纳。
原文摘要 · Abstract (English)
Industrial workplace challenges range from musculoskeletal disorders -- a leading cause of occupational injury -- to suboptimal workstation layouts, inefficient task sequences, and poor human-equipment fit. Digital human modeling (DHM) tools address several of these challenges by placing a scalable virtual mannequin in a computer-aided design (CAD) environment, enabling engineers to evaluate ergonomic risk through standardized assessment methods (RULA, REBA, NIOSH Lifting Equation, OWAS), optimize workstation layouts for reach and visibility, predict task postures through inverse kinematics, and simulate operations before physical implementation. Despite four decades of development since the Jack system originated at the University of Pennsylvania in the 1980s, the integrated DHM capability set -- anthropometric mannequin, posture prediction, ergonomic assessment, and CAD integration -- remains exclusive to commercial platforms such as Siemens Tecnomatix Jack (Process Simulate), Dassault DELMIA, Humanetics RAMSIS, and the University of Iowa's Santos system. These platforms operate under proprietary, vendor-quoted pricing models, and their acquisition and operating costs, together with closed-source implementations, have been repeatedly identified as practical adoption barriers for individual researchers, small-to-medium enterprises, and educational institutions. Organizations without access resort to manual observational methods -- paper-based worksheets applied to photographs or video -- sacrificing the predictive power and reproducibility that computational analysis provides. The paper serves as a design blueprint for (OpenJane/Joe), positioning the project for subsequent open-source implementation and community adoption.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。