Publications

Publications of the research group that appeared 2016 or later. For earlier publications, please visit Fabian Beck's Google Scholar or DBLP profile.

Art der Publikation: Beitrag in Sammelwerk

Visual Data Cleansing of Low-Level Eye Tracking Data

Autor(en):
Schulz, Christoph; Burch, Michael; Beck, Fabian; Weiskopf, Daniel
Herausgeber:
Burch, Michael; Chuang, Lewis; Fisher, Brian; Schmidt, Albrecht; Weiskopf, Daniel
Titel des Sammelbands:
Eye Tracking and Visualization
Seiten:
199-216
Verlag:
Springer
Veröffentlichung:
2017
Datei:
Visual Data Cleansing of Low-Level Eye Tracking Data (1,12 MB)
Zitation:
Download RIS

Kurzfassung

Analysis and visualization of eye movement data from eye tracking studies typically take into account gazes, fixations, and saccades of both eyes filtered and fused into a combined eye. Although this is a valid strategy, we argue that it is also worth investigating low-level eye tracking data prior to high-level analysis, because today’s eye tracking systems measure and infer data from both eyes separately. In this work, we present an approach that supports visual analysis and cleansing of low-level time-varying data for eye tracking experiments. The visualization helps researchers get insights into the quality of the data in terms of its uncertainty, or reliability. We discuss uncertainty originating from eye tracking, and how to reveal it for visualization, using a comparative approach for disagreement between plots, and a density-based approach for accuracy in volume rendering. Finally, we illustrate the usefulness of our approach by applying it to eye movement data recorded with two state-of-the-art eye trackers.