OPENJ: A Conceptual Framework for Open-Source Digital Human Modeling and Ergonomic Assessment in a CAD Environment
For researchers, small-to-medium enterprises, and educational institutions lacking access to costly proprietary DHM tools, this work provides a blueprint for an open-source solution to democratize ergonomic assessment and workplace design.
The paper identifies the lack of open-source digital human modeling (DHM) tools with integrated ergonomic assessment and CAD integration, which are currently only available in expensive proprietary platforms. It proposes a conceptual framework called OPENJ to guide the development of an open-source alternative, aiming to lower adoption barriers for researchers, SMEs, and educational institutions.
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.