Towards Mental Stress Detection Using Wearable Physiological Sensors

Jacqueline Wijsman, Bernard Grundlehner, Hao Liu, Hermie Hermens, Julien Penders

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

    81 Citations (Scopus)

    Abstract

    Early mental stress detection can prevent many stress related health problems. This study aimed at using a wearable sensor system to measure physiological signals and detect mental stress. Three different stress conditions were presented to a healthy subject group. During the procedure, ECG, respiration, skin conductance, and EMG of the trapezius muscles were recorded. In total, 19 physiological features were calculated from these signals. After normalization of the feature values and analysis of correlations among these features, a subset of 9 features was selected for further analysis. Principal component analysis reduced these 9 features to 7 principal components (PCs). Using these PCs and different classifiers, a consistent classification accuracy between stress and non stress conditions of almost 80% was found. This suggests that a promising feature subset was found for future development of a personalized stress monitor.
    Original languageEnglish
    Title of host publication33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011
    Place of PublicationPiscataway, NJ
    PublisherIEEE Engineering in Medicine and Biology Society
    Pages1798-1801
    Number of pages4
    ISBN (Print)978-1-4244-4122-8
    DOIs
    Publication statusPublished - 31 Aug 2011
    Event33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011 - Boston Marriott Copley Place Hotel, Boston, United States
    Duration: 30 Aug 20113 Sep 2011
    Conference number: 33

    Conference

    Conference33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011
    Abbreviated titleEMBC
    CountryUnited States
    CityBoston
    Period30/08/113/09/11

    Keywords

    • Body sensor networks
    • Stress
    • Electromyography
    • Frequency measurement
    • Biomedical monitoring
    • Protocols
    • Skin
    • Feature extraction

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