Unsupervised stress detection algorithm and experiments with real life data

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

    14 Citations (Scopus)


    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
    Subtitle of host publication18th EPIA Conference on Artificial Intelligence, EPIA 2017
    EditorsEugénio Oliveira, João Gama, Zita Vale, Henrique Lopes Cardoso
    ISBN (Electronic)978-3-319-65340-2
    ISBN (Print)978-3-319-65339-6
    Publication statusPublished - 1 Jan 2017
    MoE publication typeA4 Article in a conference publication
    EventPortuguese Conference on Artificial Intelligence, EPIA 2017 - Porto, Portugal
    Duration: 5 Sept 20178 Sept 2017

    Publication series

    SeriesLecture Notes in Computer Science


    ConferencePortuguese Conference on Artificial Intelligence, EPIA 2017
    Abbreviated titleEPIA 2017


    • stress detection
    • unsupervised learning
    • hidden Markov models


    Dive into the research topics of 'Unsupervised stress detection algorithm and experiments with real life data'. Together they form a unique fingerprint.

    Cite this