Optimized User Experience for Labeling Systems for Predictive Maintenance Applications
Published in Paper presented at the International Conference on Human-Computer Interaction (HCII 2024), Washington DC, USA, June 2024, 2024
Hallmann, M., Stern, M., Vona, F., Franke, U., Ostertag, T., Schlüter, B. & Voigt-Antons, J.-N.
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.
Recommended citation: Hallmann, M., Stern, M., Vona, F., Franke, U., Ostertag, T., Schlüter, B. & Voigt-Antons, J.-N. (2024, June). Optimized User Experience for Labeling Systems for Predictive Maintenance Applications. Paper presented at the International Conference on Human-Computer Interaction (HCII 2024). Washington DC, USA. https://doi.org/10.1007/978-3-031-76821-7_4