Unsupervised stress detection algorithm and experiments with real life data

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

    11 Citations (Scopus)

    Abstract

    Stress is the major problem in the modern society and a reason for at least half of lost working days in European enterprises, but existing stress detectors are not sufficiently convenient for everyday use. One reason is that stress perception and stress manifestation vary a lot between individuals; hence, "one-fits-all-persons" stress detectors usually achieve notably lower accuracies than person-specific methods. The majority of existing approaches to person-specific stress recognition, however, employ fully supervised training, requiring to collect fairly large sets of labelled data from each end user. These sets should contain examples of stresses and normal conditions, and such data collection effort may be tiring for end users. Therefore this work proposes an algorithm to train person-specific stress detectors using only unlabelled data, not necessarily containing examples of stresses. The proposed method, based on Hidden Markov Models with maximum posterior marginal decision rule, was tested using real life data of 28 persons and achieved average stress detection accuracy of 75%, which is similar to the accuracies of state-of-the-art supervised algorithms for real life data.
    Original languageEnglish
    Title of host publicationProgress in Artificial Intelligence
    EditorsZita Vale, Eugenio Oliveira, Joao Gama, Henrique Lopes Cardoso
    PublisherSpringer
    Pages95-107
    Number of pages13
    ISBN (Print)978-3-319-65339-6, 978-3-319-65340-2
    DOIs
    Publication statusPublished - 1 Jan 2017
    MoE publication typeA4 Article in a conference publication
    EventPortuguese Conference on Artificial Intelligence, EPIA 2017 - Porto, Portugal
    Duration: 5 Sep 20178 Sep 2017

    Publication series

    SeriesLecture Notes in Computer Science
    Volume10423 LNAI
    ISSN0302-9743

    Conference

    ConferencePortuguese Conference on Artificial Intelligence, EPIA 2017
    Abbreviated titleEPIA 2017
    CountryPortugal
    CityPorto
    Period5/09/178/09/17

    Keywords

    • stress detection
    • unsupervised learning
    • hidden Markov models

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