The human-centered design (HCD) approach places humans at the center of design in order to improve both products and processes, and to give users an effective, efficient and satisfying interactive experience. In industrial design and engineering, HCD is very useful in helping to achieve the novel Industry 5.0 concept, based on improving workers’ wellbeing by providing prosperity beyond jobs and growth, while respecting the production limits of the planet as recently promoted by the European Commission. In this context, the paper proposes an ergonomic assessment method based on the analysis of the workers’ workload to support the design of industrial products and processes. This allows the simultaneous analysis of the physical and cognitive workload of operators while performing their tasks during their shift. The method uses a minimum set of non-invasive wearable devices to monitor human activity and physiological parameters, in addition to questionnaires for subjective self-assessment. The method has been preliminarily tested on a real industrial case in order to demonstrate how it can help companies to support the design of optimized products and processes promoting the workers’ wellbeing.
This paper introduces a low-cost and low computational marker-less motion capture system based on the acquisition of frame images through standard RGB cameras. It exploits the open-source deep learning model CMU, from the tf-pose-estimation project. Its numerical accuracy and its usefulness for ergonomic assessment are evaluated by a proper experiment, designed and performed to: (1) compare the data provided by it with those collected from a motion capture golden standard system; (2) compare the RULA scores obtained with data provided by it with those obtained with data provided by the Vicon Nexus system and those estimated through video analysis, by a team of three expert ergonomists. Tests have been conducted in standardized laboratory conditions and involved a total of six subjects. Results suggest that the proposed system can predict angles with good consistency and give evidence about the tool’s usefulness for ergonomist.
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