Estimation of behavioral user state based on eye gaze and head pose-application in an e-learning environment

Asteriadis, S; Tzouveli, P; Karpouzis, K; Kollias, S

dc.contributor.author	Asteriadis, S	en
dc.contributor.author	Tzouveli, P	en
dc.contributor.author	Karpouzis, K	en
dc.contributor.author	Kollias, S	en
dc.date.accessioned	2014-03-01T01:30:25Z
dc.date.available	2014-03-01T01:30:25Z
dc.date.issued	2009	en
dc.identifier.issn	1380-7501	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19570
dc.subject	Eye gaze	en
dc.subject	Facial feature detection and tracking	en
dc.subject	Head pose	en
dc.subject	User attention estimation	en
dc.subject.classification	Computer Science, Information Systems	en
dc.subject.classification	Computer Science, Software Engineering	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Eye movements	en
dc.subject.other	Feedback	en
dc.subject.other	Image recording	en
dc.subject.other	Image segmentation	en
dc.subject.other	Internet	en
dc.subject.other	Multimedia systems	en
dc.subject.other	State feedback	en
dc.subject.other	Word processing	en
dc.subject.other	Behavioral states	en
dc.subject.other	E - learnings	en
dc.subject.other	Electronic documents	en
dc.subject.other	Eye and hands	en
dc.subject.other	Eye gaze	en
dc.subject.other	Facial feature detection and tracking	en
dc.subject.other	Fuzzy networks	en
dc.subject.other	Head pose	en
dc.subject.other	Level of interests	en
dc.subject.other	Reading performances	en
dc.subject.other	State based	en
dc.subject.other	Testbed	en
dc.subject.other	User attention estimation	en
dc.subject.other	User feedbacks	en
dc.subject.other	User profiles	en
dc.subject.other	WEB cameras	en
dc.subject.other	E-learning	en
dc.title	Estimation of behavioral user state based on eye gaze and head pose-application in an e-learning environment	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/s11042-008-0240-1	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s11042-008-0240-1	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	Most e-learning environments which utilize user feedback or profiles, collect such information based on questionnaires, resulting very often in incomplete answers, and sometimes deliberate misleading input. In this work, we present a mechanism which compiles feedback related to the behavioral state of the user (e.g. level of interest) in the context of reading an electronic document; this is achieved using a non-intrusive scheme, which uses a simple web camera to detect and track the head, eye and hand movements and provides an estimation of the level of interest and engagement with the use of a neuro-fuzzy network initialized from evidence from the idea of Theory of Mind and trained from expert-annotated data. The user does not need to interact with the proposed system, and can act as if she was not monitored at all. The proposed scheme is tested in an e-learning environment, in order to adapt the presentation of the content to the user profile and current behavioral state. Experiments show that the proposed system detects reading- and attention-related user states very effectively, in a testbed where children's reading performance is tracked. © 2008 Springer Science+Business Media, LLC.	en
heal.publisher	SPRINGER	en
heal.journalName	Multimedia Tools and Applications	en
dc.identifier.doi	10.1007/s11042-008-0240-1	en
dc.identifier.isi	ISI:000262506300006	en
dc.identifier.volume	41	en
dc.identifier.issue	3	en
dc.identifier.spage	469	en
dc.identifier.epage	493	en