Monitoring mental state during real life office work

Anne-Marie Brouwer, Loïs van de Water, Maarten Hogervorst, Wessel Kraaij, Jan Maarten Schraagen, Koen Hogenelst

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    5 Citations (Scopus)


    Monitoring an individual’s mental state using unobtrusively measured signals is regarded as an essential element in symbiotic human-machine systems. However, it is not straightforward to model the relation between mental state and such signals in real life, without resorting to (unnatural) emotion induction. We recorded heart rate, facial expression and computer activity of nineteen participants while working at the computer for ten days. In order to obtain ‘ground truth’ emotional state, participants indicated their current emotion using a valence-arousal affect grid every 15 min. We found associations between valence/arousal and the unobtrusively measured variables. There was some first success to predict subjective valence/arousal using personal classification models. Thus, real-life office emotions appear to vary enough, and can be reported well enough, to uncover relations with unobtrusively measured variables. This is required to be able to monitor individuals in real life more fine-grained than the frequency with which emotion is probed.
    Original languageEnglish
    Title of host publicationSymbiotic Interaction
    Subtitle of host publication6th International Workshop, Symbiotic 2017 Eindhoven, The Netherlands, December 18-19, 2017. Revised Selected Papers
    EditorsJaap Ham, Anna Spagnolli, Benjamin Blankertz, Luciano Gamberini, Giulio Jacucci
    Place of PublicationCham
    Number of pages12
    ISBN (Electronic)978-3-319-91593-7
    ISBN (Print)978-3-319-91592-0
    Publication statusPublished - 2018
    EventSymbiotic 2017: 6th International Workshop on Symbiotic Interaction - De Zwarte Doos, Eindhoven, Netherlands
    Duration: 18 Dec 201719 Dec 2017
    Conference number: 6

    Publication series

    NameLecture Notes in Computer Science
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    ConferenceSymbiotic 2017
    Abbreviated titleSymbiotic 2017


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