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.
Original language | English |
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Title of host publication | Proceedings - ETRA 2018 |
Subtitle of host publication | 2018 ACM Symposium on Eye Tracking Research and Applications |
Publisher | Association of Computing Machinery |
ISBN (Electronic) | 9781450357067 |
DOIs | |
Publication status | Published - 2018 |
Event | 10th ACM Symposium on Eye Tracking Research and Applications, ETRA 2018 - Warsaw, Poland Duration: 14 Jun 2018 → 17 Jun 2018 |
Conference
Conference | 10th ACM Symposium on Eye Tracking Research and Applications, ETRA 2018 |
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Country/Territory | Poland |
City | Warsaw |
Period | 14/06/18 → 17/06/18 |
Keywords
- Evaluation
- Eye-tracking
- Model
- Recommend
- Similarity
- Time-series
- Visual analytics
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Human-Computer Interaction
- Ophthalmology
- Sensory Systems
Fields of Expertise
- Information, Communication & Computing