Gaze-X: Adaptive affective, multimodal interface for single-user office scenarios

L. Maat

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

    27 Citations (Scopus)

    Abstract

    This paper describes an intelligent system that we developed to support affective multimodal human-computer interaction (AMM-HCI) where the user’s actions and emotions are modeled and then used to adapt the interaction and support the user in his or her activity. The proposed system, which we named Gaze-X, is based on sensing and interpretation of the human part of the computer’s context, known as W5+ (who, where, what, when, why, how). It integrates a number of natural human communicative modalities including speech, eye gaze direction, face and facial expression, and a number of standard HCI modalities like keystrokes, mouse movements, and active software identification, which, in turn, are fed into processes that provide decision making and adapt the HCI to support the user in his or her activity according to his or her preferences. A usability study conducted in an office scenario with a number of users indicates that Gaze-X is perceived as effective, easy to use, useful, and affectively qualitative.
    Original languageUndefined
    Title of host publicationArtifical Intelligence for Human Computing
    EditorsT.S Huang, Antinus Nijholt, Maja Pantic, A. Pentland
    Place of PublicationBerlin
    PublisherSpringer
    Pages251-271
    Number of pages21
    ISBN (Print)978-3-540-72346-2
    DOIs
    Publication statusPublished - 2007

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer Verlag
    Number7/4451/2007
    Volume4451/2007
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Keywords

    • HMI-MI: MULTIMODAL INTERACTIONS
    • METIS-245909
    • IR-62093
    • EWI-11669

    Cite this

    Maat, L. (2007). Gaze-X: Adaptive affective, multimodal interface for single-user office scenarios. In T. S. Huang, A. Nijholt, M. Pantic, & A. Pentland (Eds.), Artifical Intelligence for Human Computing (pp. 251-271). (Lecture Notes in Computer Science; Vol. 4451/2007, No. 7/4451/2007). Berlin: Springer. https://doi.org/10.1007/978-3-540-72348-6, https://doi.org/10.1007/978-3-540-72348-6_13