Integrative Analysis of Open Datasets for Stress Prediction

Purpose Stress is the physiological or psychological response to internal or external stressors. By causing physiological and behavioral changes, persistent stress affects psychological and physiological functionality, potentially reducing the quality of life. Therefore, it is of great interest to s...

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Bibliographic Details
Published in:Journal of medical and biological engineering Vol. 45; no. 3; pp. 385 - 399
Main Authors: Ladakis, Ioannis, Fotopoulos, Dimitris, Chouvarda, Ioanna
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2025
Springer Nature B.V
Subjects:
Accuracy
Biological Techniques
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedical Engineering/Biotechnology
Biomedicine
Classification
Data collection
Datasets
Deep learning
Electrocardiography
Electromyography
Feature selection
Heart rate
Machine learning
Open data
Original Article
Physiology
Predictions
Quality of life
Regenerative Medicine/Tissue Engineering
Sensors
Signal processing
Stress
Support vector machines
Wearable computers
Deep learning
Stress detection
Physiological data
Machine learning
Biosignals
Stress assessment
Supervised models
Open datasets
ISSN:1609-0985, 2199-4757
Online Access:Get full text
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Summary:Purpose Stress is the physiological or psychological response to internal or external stressors. By causing physiological and behavioral changes, persistent stress affects psychological and physiological functionality, potentially reducing the quality of life. Therefore, it is of great interest to systematically monitor individuals daily to investigate their physiological reactions to potential stressors. The aim of this study is to investigate the potential of training reliable and accurate ML and DL models for the prediction of acute stress based on available open datasets. Methods The open datasets include physiological signals collected using wearable sensors during stress characterization experiments and made available to the public. Various machine and deep learning (ML/DL) models were tested and compared based on their accuracy (ACC). Harmonizing techniques were also included to increase the generalizability of the models and reduce their dependence on sensors and experimental protocols variations. Results The models achieved test-set accuracies over 80% for different classification problems, while the combination of the available data led to more accurate models for the binary classification problem (over 90%). Harmonizing differences between participants and datasets led to more generalizable models with encouraging initial results (around 70% accuracy) when evaluated with unseen subjects. Conclusion The extracted models can all be used depending on the classification problem and the experimental settings/protocols. All models could play a significant role towards forming and developing a stress prediction ‘service’, taking into account the impact of using different sensors, and, thus, having to proceed to required adjustments.
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ISSN:1609-0985
2199-4757
DOI:10.1007/s40846-025-00958-z