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Video stimuli suitable for stress estimation based on biosignals.

Masaki Ohata, Masaya Togashi, Ingon Chanpornpakdi

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    Summary

    Identifying effective video stimuli is key for accurate stress estimation. High-valence or high-arousal videos showed lower prediction errors, suggesting their effectiveness in detecting stress early.

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    Area of Science:

    • Psychophysiology
    • Machine Learning
    • Mental Health Diagnostics

    Background:

    • Stress is a significant factor in mental health disorders like depression and anxiety.
    • Early detection of stress is crucial for timely intervention and management of mental health conditions.
    • Observing biological signal changes in response to sensory stimuli, such as videos, offers a promising method for stress detection.

    Purpose of the Study:

    • To identify specific video stimuli that are most effective for accurate stress estimation.
    • To investigate the influence of emotional states evoked by video stimuli on the precision of stress detection.
    • To determine if emotional valence and arousal levels of video stimuli correlate with stress estimation accuracy.

    Main Methods:

    • Utilized an open video dataset with 444 emotion scale responses (valence and arousal).

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  • Categorized 90 videos into emotion subsets based on valence and arousal ratings.
  • Measured biological signals during video presentations and constructed machine learning models for each subset to compare prediction errors.

    • Main Results:

    • Machine learning models demonstrated lower prediction errors when using video stimuli from high valence and high arousal subsets.
    • The accuracy of stress estimation was significantly influenced by the emotional content of the video stimuli.
    • Subsets characterized by high valence and high arousal proved more effective for stress detection.

    Conclusions:

    • Video stimuli eliciting high valence or high arousal are effective for accurate stress estimation.
    • The emotional impact of video content is a critical factor in the success of biological signal-based stress detection.
    • This research provides a foundation for developing more precise stress monitoring tools using tailored video stimuli.