@inproceedings{dollinger2022eyetracking,
 abstract = {Mirror exposure is an important method in the treatment of body image disturbances. Eye tracking can support the unaffected assessment of attention biases during mirror exposure. However, the analysis of eye tracking data in mirror exposure comes with various difficulties and is associated with a high manual workload during data processing. We present an automated data processing framework that enables us to determine any body part as an area of interest without placing markers on the bodies of participants. A short, formative user study proved the quality compared to the gold standard. The automatic processing and openness for different systems allow a broad range of applications.},
 added-at = {2024-05-01T09:51:19.000+0200},
 author = {Döllinger, Nina and Göttfert, Christopher and Wolf, Erik and Mal, David and Latoschik, Marc Erich and Wienrich, Carolin},
 biburl = {https://www.bibsonomy.org/bibtex/2f96fda3610cda5c5ae767b4512de0df3/ewolf},
 booktitle = {2022 Conference on Mensch und Computer},
 doi = {10.1145/3543758.3547567},
 interhash = {908d7ce6957da0fea00561dae7f108e4},
 intrahash = {f96fda3610cda5c5ae767b4512de0df3},
 keywords = {myown},
 pages = {513–517},
 timestamp = {2025-05-19T20:28:10.000+0200},
 title = {Analyzing Eye Tracking Data in Mirror Exposure},
 url = {https://publications.wolf-research.com/2022-muc-eyetracking_in_mirror_exposition-preprint.pdf},
 year = {2022}
}