Personalized mental stress detection with self-organizing map: From laboratory to the field

Jaakko Tervonen (Corresponding Author), Sampsa Puttonen, Mikko J. Sillanpää, Leila Hopsu, Zsolt Homorodi, Janne Keränen, Janne Pajukanta, Antti Tolonen, Arttu Lämsä, Jani Mäntyjärvi

    Research output: Contribution to journalArticleScientificpeer-review

    3 Citations (Scopus)

    Abstract

    Stress has become a major health concern and there is a need to study and develop new digital means for real-time stress detection. Currently, the majority of stress detection research is using population based approaches that lack the capability to adapt to individual differences. They also use supervised learning methods, requiring extensive labeling of training data, and they are typically tested on data collected in a laboratory and thus do not generalize to field conditions. To address these issues, we present multiple personalized models based on an unsupervised algorithm, the Self-Organizing Map (SOM), and we propose an algorithmic pipeline to apply the method for both laboratory and field data. The performance is evaluated on a dataset of physiological measurements from a laboratory test and on a field dataset consisting of four weeks of physiological and smartphone usage data. In these tests, the performance on the field data was steady across the different personalization levels (accuracy around 60%) and a fully personalized model performed the best on the laboratory data, achieving accuracy of 92% which is comparable to state-of-the-art supervised classifiers. These results demonstrate the feasibility of SOM in personalized mental stress detection both in constrained and free-living environment.

    Original languageEnglish
    Article number103935
    JournalComputers in Biology and Medicine
    Volume124
    DOIs
    Publication statusPublished - Sep 2020
    MoE publication typeA1 Journal article-refereed

    Keywords

    • Behavior
    • Clustering
    • Machine learning
    • Personalization
    • Stress detection
    • Unsupervised learning

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