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Updated: Aug 5, 2025

A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings

A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings

Published on: January 22, 2018

Mental Stress Detection Using a Wearable In-Ear Plethysmography.

Hika Barki¹, Wan-Young Chung^1,2

¹Department of AI Convergence, Pukyong National University, Busan 48513, Republic of Korea.

|March 29, 2023

Summary

This study developed an ear-mounted photoplethysmography (PPG) system for mental stress detection. The system achieved high accuracy, showing promise for early mental health intervention.

Keywords:

continuous wavelet transform (CWT)convolutional neural network (CNN)mental stress photoplethysmography (PPG)scalograms

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Last Updated: Aug 5, 2025

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Area of Science:

Biomedical Engineering
Signal Processing
Mental Health Technology

Background:

Mental stress significantly impacts health and well-being.
Early detection of mental stress is vital for preventing associated illnesses.
Current detection methods may lack continuous, non-invasive monitoring capabilities.

Purpose of the Study:

To develop and evaluate an ear-mounted photoplethysmography (PPG) system for detecting mental stress.
To assess the system's accuracy and reliability in classifying stress levels.
To explore signal processing techniques for enhancing stress detection performance.

Main Methods:

Utilized an ear-mounted PPG system to collect physiological data from 14 participants under controlled stress-inducing conditions (Stroop test, mathematical calculations).
Preprocessed raw PPG signals and transformed them into scalograms using continuous wavelet transform (CWT).
Employed a convolutional neural network (CNN) classifier to differentiate between stressed and non-stressed states based on PPG signal features.

Main Results:

The PPG system demonstrated high classification accuracy (92.04%) and F1-score (90.8%) for mental stress detection.
Introducing white Gaussian noise to PPG signals further improved performance, achieving 96.02% accuracy and 95.24% F1-score.
The CWT-based feature extraction and CNN classification effectively identified stress-related changes in PPG signals.

Conclusions:

The proposed ear-mounted PPG system is a promising tool for reliable, non-invasive mental stress detection.
The system's high accuracy suggests potential for early intervention and improved mental health management.
Further research and development could integrate this technology into wearable devices for continuous well-being monitoring.