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Abstract
In recent years, mixed reality applications for training unskilled workers have gained a lot of attention in Industry 4.0 and smart factories. One application domain with a high potential of acceptance through management and employees are manual assembly tasks, since just in time production and many variants makes it quite difficult for assembly workers to build high quality products. Additionally, labor shortages and high turnover have made training of new employees especially important. But what are the challenges for such workplace training systems and how can they support users to learn new work steps in an independent and effective way? For our analysis, we have built prototypes for three different assembly use cases. In addition, all prototypes were analyzed by user studies to gain deeper insights from the assembly worker perspective. Our analysis of these workplace training systems has revealed new challenges. For instance, these systems should consider didactic concepts to enable and verify both learning and retention of acquired skills. In addition, providing feedback and reflection mechanisms is essential for long-term user motivation. Last but not least, we should enable the integration of training and guidance, so that new employees can work completely independent of other co-workers form the very first minute.
Originalsprache | Englisch |
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Titel | Proceedings of the 2022 IEEE International Conference on Human-Machine Systems, ICHMS 2022 |
Redakteure/-innen | David Kaber, Antonio Guerrieri, Giancarlo Fortino, Andreas Nurnberger |
Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
ISBN (elektronisch) | 9781665452380 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2022 |
Veranstaltung | 3rd IEEE International Conference on Human-Machine Systems, ICHMS 2022 - Orlando, USA/Vereinigte Staaten Dauer: 17 Nov. 2022 → 19 Nov. 2022 |
Publikationsreihe
Name | Proceedings of the 2022 IEEE International Conference on Human-Machine Systems, ICHMS 2022 |
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Konferenz
Konferenz | 3rd IEEE International Conference on Human-Machine Systems, ICHMS 2022 |
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Land/Gebiet | USA/Vereinigte Staaten |
Ort | Orlando |
Zeitraum | 17.11.2022 → 19.11.2022 |
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Career Advisory System
Schönböck, J. (Leitende(r) Forscher/-in), Augstein, M. (Weitere Forschende), Bodenhofer, U. (CoPI), Ludwig, H. (Weitere Forschende) & Pimminger, S. (Weitere Forschende)
01.05.2021 → 31.10.2023
Projekt: Forschungsprojekt