Optimized User Experience for Labeling Systems for Predictive Maintenance Applications
Hallmann, M., Stern, M., Henning, J., Franke, U., Ostertag, T., da Costa, J. P. & Voigt-Antons, J.-N.
Paper presented at the International Conference on Human-Computer Interaction (HCII 2025), Gothenburg, Sweden, June 2025
Summary
We present a UX-driven redesign of industrial labeling tools for predictive maintenance datasets. Through contextual inquiry with domain experts, we identify pain points in annotation flow, error recovery, and metadata capture, and evaluate prototypes that reduce time-on-task and labeling errors. Results inform design patterns for scalable, operator-friendly annotation in industrial contexts.
Written for this site, the published abstract is at the DOI.
Cite as
Hallmann, M., Stern, M., Franke, U., Ostertag, T., da Costa, J. P. & Voigt-Antons, J.-N. (2025, June). Optimized User Experience for Labeling Systems for Predictive Maintenance Applications. Paper presented at the International Conference on Human-Computer Interaction (HCII 2025). Gothenburg, Sweden.
