Abstract
We developed a new concept to improve the efficiency of visual analysis through visual recommendations. It uses a novel eye-gaze based recommendation model that aids users in identifying interesting time-series patterns. Our model combines time-series features and eye-gaze interests, captured via an eye-tracker. Mouse selections are also considered. The system provides an overlay visualization with recommended patterns, and an eye-history graph, that supports the users in the data exploration process. We conducted an experiment with 5 tasks where 30 participants explored sensor data of a wind turbine. This work presents results on pre-attentive features, and discusses the precision/recall of our model in comparison to final selections made by users. Our model helps users to efficiently identify interesting time-series patterns.
Originalsprache | englisch |
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Titel | Proceedings - ETRA 2018 |
Untertitel | 2018 ACM Symposium on Eye Tracking Research and Applications |
Herausgeber (Verlag) | Association of Computing Machinery |
ISBN (elektronisch) | 9781450357067 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2018 |
Veranstaltung | 10th ACM Symposium on Eye Tracking Research and Applications, ETRA 2018 - Warsaw, Polen Dauer: 14 Juni 2018 → 17 Juni 2018 |
Konferenz
Konferenz | 10th ACM Symposium on Eye Tracking Research and Applications, ETRA 2018 |
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Land/Gebiet | Polen |
Ort | Warsaw |
Zeitraum | 14/06/18 → 17/06/18 |
ASJC Scopus subject areas
- Maschinelles Sehen und Mustererkennung
- Human-computer interaction
- Ophthalmologie
- Sensorische Systeme
Fields of Expertise
- Information, Communication & Computing