Automatic Feature Selection and Classification of Physical and Mental Load using Data from Wearable Sensors

Juha Pärkkä, Miikka Ermes, Mark van Gils

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

    11 Citations (Scopus)

    Abstract

    Long-term monitoring of health is essential in many chronic conditions, but automatic monitoring is not yet utilized routinely with mental stress. Accelerometers, magnetometers, ECG, respiratory effort, skin temperature and pulse oximetry were used with 12 health volunteers in this study for monitoring 1) heavy mental load, 2) normal mental load, 3) walking, 4) running and 5) lying. Heavy mental load consisted of a 20-min IQ test and normal mental load was represented by reading a comic book. Automatic feature selection using sequential forward search was used to select the best features for classification of the five activities. Normalized heart rate, utilizing activity context information was found to be the most powerful feature for discriminating heavy mental load from normal. Classification accuracy for all 5 activities was 89% with a custom decision tree and with a k-nearest neighbor classifier and 85% with an artificial neural network.
    Original languageEnglish
    Title of host publication10th IEEE International Conference on Information Technology and Applications in Biomedicine, ITAB 2010. Corfu, Greece, 2 - 5 Nov. 2010
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Pages1-5
    ISBN (Print)978-1-4244-6560-6
    DOIs
    Publication statusPublished - 2010
    MoE publication typeA4 Article in a conference publication
    Event10th IEEE International Conference on Information Technology and Applications in Biomedicine, ITAB 2010 - Corfu, Greece
    Duration: 2 Nov 20105 Nov 2010

    Conference

    Conference10th IEEE International Conference on Information Technology and Applications in Biomedicine, ITAB 2010
    Abbreviated titleITAB 2010
    Country/TerritoryGreece
    CityCorfu
    Period2/11/105/11/10

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