Publication Details
What you want to do next: A novel approach for intent prediction in gaze-based interaction
Vrzáková Hana, Ing. (FIT BUT)
Hradiš Michal, Ing., Ph.D. (DCGM FIT BUT)
gaze-based interaction, Midas touch, machine learn-ing, activity detection
(Recieved best paper honorable mention award.)
Interaction intent prediction and the Midas touch have been a longstanding challenge for eye-tracking researchers and users of gaze-based interaction. Inspired by machine learning approaches in biometric person authentication, we developed and tested an offline framework for task-independent prediction of interaction intents. We describe the principles of the method, the features extracted, normalization methods, and evaluation metrics. We systematically evaluated the proposed approach on an example dataset of gaze-augmented problem-solving sessions, and we present results of the three normalization methods, different feature sets and fusion of multiple feature types. Our results show that accuracy of up to 76 % can be achieved with Area Under Curve around 80 %. We discuss the possibility of applying the results for an online system capable of interaction intent prediction.
@INPROCEEDINGS{FITPUB9854, author = "Roman Bedna\v{r}\'{i}k and Hana Vrz\'{a}kov\'{a} and Michal Hradi\v{s}", title = "What you want to do next: A novel approach for intent prediction in gaze-based interaction", pages = "83--90", booktitle = "ETRA '12 Proceedings of the Symposium on Eye Tracking Research and Applications", year = 2012, location = "Santa Barbara, US", publisher = "Association for Computing Machinery", ISBN = "978-1-4503-1221-9", doi = "10.1145/2168556.2168569", language = "english", url = "https://www.fit.vut.cz/research/publication/9854" }