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Related Experiment Video

Updated: May 24, 2025

A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings
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Physical, Social and Cognitive Stressor Identification using Electrocardiography-derived Features and Machine

Maxine He, Jonathan Cerna, Abdul Alkurdi

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    Summary

    This study used wearable sensors and machine learning to detect stress in college students. Findings show potential for predicting anxiety symptoms using physiological data from smart clothing.

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    Area of Science:

    • Physiological monitoring
    • Machine learning applications
    • Mental health technology

    Background:

    • Anxiety is a significant mental health issue in young adults, especially college students.
    • Predicting anxiety is crucial for developing personalized treatments.
    • Objective, non-invasive, continuous monitoring tools are needed for anxiety prediction.

    Purpose of the Study:

    • To investigate the relationship between different stressors and heart rate variability (HRV).
    • To assess the performance of machine learning models in classifying stressor types using HRV features.
    • To evaluate the generalizability of physiological changes across various stressors and participants.

    Main Methods:

    • Twenty-six college students wore a Hexoskin smartshirt during non-stressful and stressful conditions.
    • Electrocardiography (ECG) derived features from the wearable device were analyzed.
    • Support Vector Machine (SVM) architecture was employed for binary and multi-class classification of stressor types.

    Main Results:

    • Condition-wise binary classification accuracy reached 76.2%.
    • Multi-class classification accuracy achieved 79.1% using the SVM model.
    • The study demonstrated the feasibility of differentiating stressor types based on HRV.

    Conclusions:

    • Machine learning models, utilizing wearable ECG data, can effectively detect different types of stressors.
    • These findings support the development of wearable devices for objective, continuous monitoring and prediction of anxiety symptoms.
    • Further research can refine these tools for broader clinical application in mental health.