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, June 2024
Summary
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.
Written for this site, the published abstract is at the DOI.
Cite as
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
