New publication by our group in the ZWF - Zeitschrift für wirtschaftliche Fabrikbetrieb!

Industrial exoskeletons and other physical support systems are still largely selected and developed on the basis of empirical values. A key reason for this is that findings from digital and physical simulations, robot tests and user studies are rarely brought together in a standardised way. As a result, decisions are difficult to understand and valuable cross-domain knowledge remains unutilised.

In this article, our team presents a knowledge-based expert system that aims to close this gap. It integrates cross-domain data and evaluation criteria to provide transparent, reproducible recommendations for the development and evaluation of exoskeletons.

Important contributions:

- Consistent integration of data from simulation, robot testing and user studies

- A structured catalogue of criteria as a common basis for evaluation

- Multi-criteria evaluation that enables comprehensible, data-driven decisions

- AI-supported recommendations that go beyond experience-based assessments

The goal is to advance the development of exoskeletons from experience to evidence - transparent, reproducible and grounded in interdisciplinary data.

We look forward to exchanging ideas with colleagues from the fields of manufacturing technology, ergonomics and applied AI research.

Read the article here.