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Predicting observer's task from eye movement patterns during motion image analysis

: Hild, Jutta; Voit, Michael; Kühnle, Christian; Beyerer, Jürgen

Fulltext urn:nbn:de:0011-n-5066052 (1.4 MByte PDF)
MD5 Fingerprint: 36c9d75d7acd4a0c22c2b4767658e7a5
Created on: 16.8.2018

Association for Computing Machinery -ACM-:
ETRA 2018, ACM Symposium on Eye Tracking Research & Applications. Proceedings : Warsaw, Poland, June 14 - 17, 2018
New York: ACM, 2018
ISBN: 978-1-4503-5706-7
Art. 58, 5 pp.
Symposium on Eye Tracking Research & Applications (ETRA) <2018, Warsaw>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()
eye movement; task prediction; machine learning; Motion image analysis; experiment

Predicting an observer's tasks from eye movements during several viewing tasks has been investigated by several authors. This contribution adds task prediction from eye movements tasks occurring during motion image analysis: Explore, Observe, Search, and Track. For this purpose, gaze data was recorded from 30 human observers viewing a motion image sequence once under each task. For task decoding, the classification methods Random Forest, LDA, and QDA were used; features were fixation- or saccade-related measures. Best accuracy for prediction of the three tasks Observe, Search, Track from the 4-minute gaze data samples was 83.7% (chance level 33%) using Random Forest. Best accuracy for prediction of all four tasks from the gaze data samples containing the first 30 seconds of viewing was 59.3% (chance level 25%) using LDA. Accuracy decreased significantly for task prediction on small gaze data chunks of 5 and 3 seconds, being 45.3% and 38.0% (chance 25%) for the four tasks, and 52.3% and 47.7% (chance 33%) for the three tasks.