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Related Experiment Video

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A Modified Trier Social Stress Test for Vulnerable Mexican American Adolescents

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Stress and Emotion Open Access Data: A Review on Datasets, Modalities, Methods, Challenges, and Future Research

Aleksandr Ometov¹, Anzhelika Mezina², Hsiao-Chun Lin¹

¹Wireless Research Center, Tampere University, Tampere, Finland.

Journal of Healthcare Informatics Research

|July 29, 2025

Summary

This summary is machine-generated.

This review examines open-access datasets for emotion and stress detection using wearable sensors. Challenges remain in real-world data collection and validation for eHealth applications.

Keywords:

Dataset Detection Emotion Open access Recognition Review Stress Wearable eHealth

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Area of Science:

eHealth
Wearable Technology
Affective Computing

Background:

Remote patient monitoring is crucial for personalized eHealth care.
Emotion and stress recognition in eHealth faces challenges due to data subjectivity and complexity.

Purpose of the Study:

To review open-access multimodal datasets for emotion and stress detection.
To analyze dataset characteristics, acquisition methods, and classification challenges.

Main Methods:

Focus on physiological signals from wearable devices.
Review advanced data processing methods.
Examine dataset features, acquisition, and classification.

Main Results:

Notable advances in data collection and algorithm development exist.
Limitations include real-world variability, individual differences, and objective validation difficulties.
Self-reported and contextual data can improve performance but lack consistency.

Conclusions:

Further research is needed to address privacy, long-term data annotation, and robustness in uncontrolled environments.
Privacy-preserving strategies and interdisciplinary collaboration are essential for reliable, scalable emotion-aware eHealth systems.