TY - GEN
T1 - Mixed Reality Workplace Training Systems for Smart Factories
T2 - 3rd IEEE International Conference on Human-Machine Systems, ICHMS 2022
AU - Pimminger, Sebastian
AU - Kurschl, Werner
AU - Schonbock, Johannes
N1 - Funding Information:
ACKNOWLEDGMENT The research leading to these results has been accomplished within the project Human Centered Workplace 4 Industry, funded by the Austrian Research Promotion Agency (FFG) within the 6th COIN - Cooperation and Innovation programme under grant agreement no. 856362.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - assistive technology
KW - extended reality
KW - human factors
KW - human in the loop
KW - human-computer interaction
KW - manufacturing systems
UR - http://www.scopus.com/inward/record.url?scp=85146261446&partnerID=8YFLogxK
U2 - 10.1109/ICHMS56717.2022.9980642
DO - 10.1109/ICHMS56717.2022.9980642
M3 - Conference contribution
AN - SCOPUS:85146261446
T3 - Proceedings of the 2022 IEEE International Conference on Human-Machine Systems, ICHMS 2022
BT - Proceedings of the 2022 IEEE International Conference on Human-Machine Systems, ICHMS 2022
A2 - Kaber, David
A2 - Guerrieri, Antonio
A2 - Fortino, Giancarlo
A2 - Nurnberger, Andreas
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 November 2022 through 19 November 2022
ER -