Detection of prolonged stress in smart office

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

    4 Citations (Scopus)


    Detection of long-lasting stress in its early stage may allow prevention of irreversible health damages, but most of today’s stress detectors are not sufficiently convenient for long-term use. Some detectors rely on wearable physiological devices, known to have high abandonment rate, whereas other systems collect only behavioural data, but require each user to report occurrences of his/her stress during at least a month because behavioural patterns notably vary between individuals. Therefore, behaviour-based stress detection models should be learned separately for each person, and in existing systems such learning is typically supervised. In contrast, this work proposes a person-specific stress monitoring system, requiring no efforts from end users. This system acquires users’ motion trajectories via in-office depth cameras and employs a novel unsupervised method for analysis of these trajectories, based on discrete Hidden Markov Models. In 10-months-long real life study the proposed system correctly recognised the most stressful working periods of the monitored subjects and pointed out the most stress-prone persons.

    Original languageEnglish
    Title of host publicationIntelligent Computing
    Subtitle of host publicationProceedings of the 2018 Computing Conference
    EditorsSupriya Kapoor, Rahul Bhatia, Kohei Arai
    Number of pages9
    ISBN (Print)978-303-00117-6-5
    Publication statusPublished - 2018
    MoE publication typeNot Eligible
    EventComputing Conference 2018, SAI 2018 - London, United Kingdom
    Duration: 10 Jul 201812 Jul 2018

    Publication series

    SeriesAdvances in Intelligent Systems and Computing


    ConferenceComputing Conference 2018, SAI 2018
    Abbreviated titleSAI 2018
    Country/TerritoryUnited Kingdom


    • Ambient intelligence
    • Hidden Markov Models (HMM)
    • Stress detection
    • Unsupervised learning


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