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Observational postural assessment methods which are commonly used in industry are time consuming and have issues of inter-and intra-rater reliability. Computer vision (CV) based methods have been proposed, but they have mainly been tested inside lab environments. This study aims to develop and evaluate an upper body postural assessment system in a real industry environment using a single depth camera and OpenPose for the task of forklift driving. The results were compared with XSens, an Inertial Measurement Unit (IMU) based system. Data from three forklift drivers performing seven indoor and outdoor tasks were recorded with a depth camera and XSens sensors. The data were then analyzed with OpenPose with additional custom processing. The angles calculated by the computer vision system showed small errors compared to the XSens system and generally followed the trend of the XSens system joint angle values. However, the results after applying ergonomic thresholds were vastly different and the two systems rarely agreed. These findings suggest that the CV system needs further study to improve the robustness on self-occlusion and angle calculations. Also, XSens needs further study to assess its consistency and reliability in industrial environments.

The planning and design process of manufacturing factory layouts is commonly performed using digital tools, enabling engineers to define and test proposals in virtual environments before implementing them physically. However, this approach often relies on the experience of the engineers involved and input from various cross-disciplinary functions, leading to a time-consuming and subjective process with a high risk of human error. To address these challenges, new tools and methods are needed. The Industry 5.0 initiative aims to further automate and assist human tasks, reinforcing the human-centric perspective when making decisions that influence production environments and working conditions. This includes improving the layout planning process by making it more objective, efficient, and capable of considering multiple objectives simultaneously. This research presents a demonstrator solution for layout planning using digital support, incorporating a virtual multi-objective optimization approach to consider safety regulations, area boundaries, workers’ well-being, and walking distance. The demonstrator provides a cross-disciplinary and transparent approach to layout planning for an assembly station in the context of battery production. The demonstrator solution illustrates how layout planning can become a cross-disciplinary and transparent activity while being automated to a higher degree, providing results that support decision-making and balance cross-disciplinary requirements.

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