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Abstract Ergonomics evaluation methods are crucial for assessing risks for work-related musculoskeletal disorders and ensuring operator well-being, productivity, and safety. Despite the increased use of digital twins and AI-supported tools in production system design and operation, ergonomics evaluations still primarily rely on observational techniques such as expert assessments or checklist-based tools like the Rapid Entire Body Assessment and the Rapid Upper Limb Assessment. These methods are time-consuming, imprecise, and prone to subjectivity arising from variability in the judgment of the ergonomists and ambiguity in scoring criteria. As an alternative, ergonomics evaluation methods based on using technologies for direct measurements can provide semi-automation of the assessments and offer greater objectivity and precision. This study investigates the capability of a computer vision-based motion capture approach to support direct measurement ergonomics evaluations and compares its results with those of an inertial measurement unit-based system in an industrial task. The comparison was conducted by studying output data of the two systems and by feeding the data into a direct measurement-based ergonomics evaluation method. A representative industrial assembly task involving upper-body movement and dynamic wrist activity was recorded simultaneously using a single monocular RGB camera and an IMU-based system. Both datasets were processed using two parallel workflows that followed the same structure to extract joint angles and segment positions over time. The comparison and evaluation of the results demonstrates that computer vision-based motion capture has the potential to provide human posture and motion data suitable for direct measurement ergonomics evaluations in industrial environments.
Díaz et al. (Sun,) studied this question.