InMind, the Inquisitive Mind Magazine, has published an article on our recent work on curiosity recognition published at UbiComp 2015.
 | Sabrina Hoppe; Tobias Loetscher; Stephanie Morey; Andreas Bulling Recognition of Curiosity Using Eye Movement Analysis Inproceedings Adj. Proc. of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2015), pp. 185-188, 2015. Abstract | Links | BibTeX @inproceedings{Hoppe15_ubicomp,
title = {Recognition of Curiosity Using Eye Movement Analysis},
author = {Sabrina Hoppe and Tobias Loetscher and Stephanie Morey and Andreas Bulling},
url = {https://perceptual.mpi-inf.mpg.de/files/2015/07/Hoppe_Ubicomp15.pdf
http://de.in-mind.org/blog/post/das-fenster-zum-gehirn-was-computer-in-unseren-blicken-lesen},
doi = {10.1145/2800835.2800910},
year = {2015},
date = {2015-09-09},
booktitle = {Adj. Proc. of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2015)},
pages = {185-188},
abstract = {Among the different personality traits that guide our behaviour, curiosity is particularly interesting for context-aware assistive systems as it is closely linked to our well-being and the way we learn. This work proposes eye movement analysis for automatic recognition of different levels of curiosity. We present a 26-participant gaze dataset recorded during a real-world shopping task with empirically validated curiosity questionnaires as ground truth. Using a support vector machine classifier and a leave-one-person-out evaluation scheme we can discriminate between two to four classes of standard curiosity scales well above chance. These results are promising and point towards a new class of context-aware systems that take the user's curiosity into account, thereby enabling new types of interaction and user adaptation.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Among the different personality traits that guide our behaviour, curiosity is particularly interesting for context-aware assistive systems as it is closely linked to our well-being and the way we learn. This work proposes eye movement analysis for automatic recognition of different levels of curiosity. We present a 26-participant gaze dataset recorded during a real-world shopping task with empirically validated curiosity questionnaires as ground truth. Using a support vector machine classifier and a leave-one-person-out evaluation scheme we can discriminate between two to four classes of standard curiosity scales well above chance. These results are promising and point towards a new class of context-aware systems that take the user's curiosity into account, thereby enabling new types of interaction and user adaptation. |
