Detection of prolonged stress in smart office

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

    Abstract

    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
    PublisherSpringer
    Pages1253-1261
    Number of pages9
    ISBN (Print)978-303-00117-6-5
    DOIs
    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
    Volume857
    ISSN2194-5357

    Conference

    ConferenceComputing Conference 2018, SAI 2018
    Abbreviated titleSAI 2018
    CountryUnited Kingdom
    CityLondon
    Period10/07/1812/07/18

    Fingerprint

    Trajectories
    Detectors
    Hidden Markov models
    Learning systems
    Cameras
    Health
    Monitoring

    Keywords

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

    Cite this

    Vildjiounaite, E., Huotari, V., Kallio, J., Kyllönen, V., Mäkelä, S. M., & Gimel’farb, G. (2018). Detection of prolonged stress in smart office. In S. Kapoor, R. Bhatia, & K. Arai (Eds.), Intelligent Computing: Proceedings of the 2018 Computing Conference (pp. 1253-1261). Springer. Advances in Intelligent Systems and Computing, Vol.. 857 https://doi.org/10.1007/978-3-030-01177-2_90
    Vildjiounaite, Elena ; Huotari, Ville ; Kallio, Johanna ; Kyllönen, Vesa ; Mäkelä, Satu Marja ; Gimel’farb, Georgy. / Detection of prolonged stress in smart office. Intelligent Computing: Proceedings of the 2018 Computing Conference. editor / Supriya Kapoor ; Rahul Bhatia ; Kohei Arai. Springer, 2018. pp. 1253-1261 (Advances in Intelligent Systems and Computing, Vol. 857).
    @inproceedings{c9ad2599cf4e4b31b6635075f8a0d5e6,
    title = "Detection of prolonged stress in smart office",
    abstract = "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.",
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    Vildjiounaite, E, Huotari, V, Kallio, J, Kyllönen, V, Mäkelä, SM & Gimel’farb, G 2018, Detection of prolonged stress in smart office. in S Kapoor, R Bhatia & K Arai (eds), Intelligent Computing: Proceedings of the 2018 Computing Conference. Springer, Advances in Intelligent Systems and Computing, vol. 857, pp. 1253-1261, Computing Conference 2018, SAI 2018, London, United Kingdom, 10/07/18. https://doi.org/10.1007/978-3-030-01177-2_90

    Detection of prolonged stress in smart office. / Vildjiounaite, Elena; Huotari, Ville; Kallio, Johanna; Kyllönen, Vesa; Mäkelä, Satu Marja; Gimel’farb, Georgy.

    Intelligent Computing: Proceedings of the 2018 Computing Conference. ed. / Supriya Kapoor; Rahul Bhatia; Kohei Arai. Springer, 2018. p. 1253-1261 (Advances in Intelligent Systems and Computing, Vol. 857).

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

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    Vildjiounaite E, Huotari V, Kallio J, Kyllönen V, Mäkelä SM, Gimel’farb G. Detection of prolonged stress in smart office. In Kapoor S, Bhatia R, Arai K, editors, Intelligent Computing: Proceedings of the 2018 Computing Conference. Springer. 2018. p. 1253-1261. (Advances in Intelligent Systems and Computing, Vol. 857). https://doi.org/10.1007/978-3-030-01177-2_90