Unsupervised learning for mental stress detection exploration of self-organizing maps

Dorien Huysmans, Elena Smets, Walter De Raedt, Chris Van Hoof, Katleen Bogaerts, Ilse Van Diest, Denis Helic

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

    Abstract

    One of the major challenges in the field of ambulant stress detection lies in the model validation. Commonly, different types of questionnaires are used to record perceived stress levels. These only capture stress levels at discrete moments in time and are prone to subjective inaccuracies. Although, many studies have already reported such issues, a solution for these difficulties is still lacking. This paper explores the potential of unsupervised learning with Self-Organizing Maps (SOM) for stress detection. In unsupervised learning settings, the labels from perceived stress levels are not needed anymore. First, a controlled stress experiment was conducted during which relax and stress phases were alternated. The skin conductance (SC) and electrocardiogram (ECG) of test subjects were recorded. Then, the structure of the SOM was built based on a training set of SC and ECG features. A Gaussian Mixture Model was used to cluster regions of the SOM with similar characteristics. Finally, by comparison of features values within each cluster, two clusters could be associated to either relax phases or stress phases. A classification performance of 79.0% (±5.16) was reached with a sensitivity of 75.6% (±11.2). In the future, the goal is to transfer these first initial results from a controlled laboratory setting to an ambulant environment.

    Original languageEnglish
    Title of host publicationBIOSIGNALS 2018 - 11th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018
    PublisherSciTePress 2013
    Pages26-35
    Number of pages10
    Volume4
    ISBN (Electronic)9789897582790
    Publication statusPublished - 1 Jan 2018
    Event11th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2018 - Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018 - Funchal, Madeira, Portugal
    Duration: 19 Jan 201821 Jan 2018

    Conference

    Conference11th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2018 - Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018
    CountryPortugal
    CityFunchal, Madeira
    Period19/01/1821/01/18

    Fingerprint

    Unsupervised learning
    Self organizing maps
    Electrocardiography
    Skin
    Labels
    Experiments

    Keywords

    • Electrocardiogram
    • Mental Stress Detection
    • Skin Conductance
    • SOM
    • Unsupervised Learning

    ASJC Scopus subject areas

    • Signal Processing
    • Biomedical Engineering
    • Electrical and Electronic Engineering

    Cite this

    Huysmans, D., Smets, E., De Raedt, W., Van Hoof, C., Bogaerts, K., Van Diest, I., & Helic, D. (2018). Unsupervised learning for mental stress detection exploration of self-organizing maps. In BIOSIGNALS 2018 - 11th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018 (Vol. 4, pp. 26-35). SciTePress 2013.

    Unsupervised learning for mental stress detection exploration of self-organizing maps. / Huysmans, Dorien; Smets, Elena; De Raedt, Walter; Van Hoof, Chris; Bogaerts, Katleen; Van Diest, Ilse; Helic, Denis.

    BIOSIGNALS 2018 - 11th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018. Vol. 4 SciTePress 2013, 2018. p. 26-35.

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

    Huysmans, D, Smets, E, De Raedt, W, Van Hoof, C, Bogaerts, K, Van Diest, I & Helic, D 2018, Unsupervised learning for mental stress detection exploration of self-organizing maps. in BIOSIGNALS 2018 - 11th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018. vol. 4, SciTePress 2013, pp. 26-35, 11th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2018 - Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018, Funchal, Madeira, Portugal, 19/01/18.
    Huysmans D, Smets E, De Raedt W, Van Hoof C, Bogaerts K, Van Diest I et al. Unsupervised learning for mental stress detection exploration of self-organizing maps. In BIOSIGNALS 2018 - 11th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018. Vol. 4. SciTePress 2013. 2018. p. 26-35
    Huysmans, Dorien ; Smets, Elena ; De Raedt, Walter ; Van Hoof, Chris ; Bogaerts, Katleen ; Van Diest, Ilse ; Helic, Denis. / Unsupervised learning for mental stress detection exploration of self-organizing maps. BIOSIGNALS 2018 - 11th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018. Vol. 4 SciTePress 2013, 2018. pp. 26-35
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