%0 Conference Paper %1 Schulz2015Visual %A Schulz, Christoph %A Burch, Michael %A Weiskopf, Daniel %B Proceedings of the Symposium on Eye Tracking and Visualization (ETVIS) %D 2015 %K A01 visus:weiskopf sfbtrr161 2015 from:mueller vis(us) visus:burchml visus visus:schulzch %T Visual Data Cleansing of Eye Tracking Data %U http://etvis.visus.uni-stuttgart.de/etvis2015/papers/etvis15_schulz.pdf %X 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 worthinvestigating low-level eye tracking data prior to high-level analysis, since today’s eye tracking systems measure and infer data fromboth eyes separately. In this work, we present an approach that supports visual analysis and cleansing of low-level time-varying datafor a wide range of eye tracking experiments. The visualization helps researchers get insights into the quality in terms of uncertainty—not only for both eyes in combination but each eye individually. Furthermore, we discuss uncertainty originating from eye tracking,how to reveal it for visualization and illustrate its usefulness using our approach by applying it to eye movement data formerly recordedwith a Tobii T60XL stationary eye tracker using a prototypical implementation.

@inproceedings{Schulz2015Visual, abstract = {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 worthinvestigating low-level eye tracking data prior to high-level analysis, since today’s eye tracking systems measure and infer data fromboth eyes separately. In this work, we present an approach that supports visual analysis and cleansing of low-level time-varying datafor a wide range of eye tracking experiments. The visualization helps researchers get insights into the quality in terms of uncertainty—not only for both eyes in combination but each eye individually. Furthermore, we discuss uncertainty originating from eye tracking,how to reveal it for visualization and illustrate its usefulness using our approach by applying it to eye movement data formerly recordedwith a Tobii T60XL stationary eye tracker using a prototypical implementation.}, added-at = {2020-10-09T12:34:20.000+0200}, author = {Schulz, Christoph and Burch, Michael and Weiskopf, Daniel}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2782dc091655d4f2baecfb9198f8748ee/visus}, booktitle = {Proceedings of the Symposium on Eye Tracking and Visualization (ETVIS)}, description = {Visual Data Cleansing of Eye Tracking Data}, interhash = {12db416024bf94eed4b64df12e6919bc}, intrahash = {782dc091655d4f2baecfb9198f8748ee}, keywords = {A01 visus:weiskopf sfbtrr161 2015 from:mueller vis(us) visus:burchml visus visus:schulzch}, note = {Rezensiertes Abstract}, timestamp = {2020-10-09T10:34:20.000+0200}, title = {Visual Data Cleansing of Eye Tracking Data}, url = {http://etvis.visus.uni-stuttgart.de/etvis2015/papers/etvis15_schulz.pdf}, year = 2015 }