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CONFERENCE[OC42]
Optimized User Experience for Labeling Systems for Predictive Maintenance Applications
Hallmann, M., Stern, M., Vona, F., Franke, U., Ostertag, T., Schlüter, B. & Voigt-Antons, J.-N.
Paper presented at the International Conference on Human-Computer Interaction (HCII 2024). Washington DC, USA
Abstract
This paper reports a UX overhaul of industrial labeling tools used to curate maintenance datasets. Through contextual interviews and iterative prototyping, we address pain points in task routing, annotation consistency, and error handling. A comparative study shows meaningful reductions in time-on-task and inter-annotator variance, yielding practical design patterns for reliable annotation at scale.
Record
- Reference
[OC42]in the Publikationsverzeichnis - TypeConference paper
- Year2024
- Research lineQuality of Experience
- Identifier
2024-06-01-OC41
BibTeX
@inproceedings{voigtantons2024oc41,
author = {Hallmann, M. and Stern, M. and Vona, F. and Franke, U. and Ostertag, T. and Schlüter, B. and Voigt-Antons, J.-N.},
title = {Optimized User Experience for Labeling Systems for Predictive Maintenance Applications},
year = {2024},
booktitle = {Paper presented at the International Conference on Human-Computer Interaction (HCII 2024). Washington DC, USA},
doi = {10.1007/978-3-031-76821-7_4},
}