Autoencoder-Based Human Stress Detection System Using Biological Signals

Stress and depression stand as the most prevalent psychological challenges in our daily lives, significantly reducing productivity in day-to-day tasks. Stress detection model typically relies on supervised learning and physiological /behavioral markers, with promising outcomes. However, label-collec...

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Bibliographic Details
Published in:2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI) pp. 1 - 7
Main Authors: Subathra, P., Malarvizhi, S.
Format: Conference Proceeding
Language:English
Published: IEEE 17.04.2024
Subjects:
Agglomerative clustering
EDA
Feature extraction
Heart rate
Human factors
K-means clustering
Productivity
Psychology
Silhouette score
Stress
Supervised learning
Surveys
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Summary:Stress and depression stand as the most prevalent psychological challenges in our daily lives, significantly reducing productivity in day-to-day tasks. Stress detection model typically relies on supervised learning and physiological /behavioral markers, with promising outcomes. However, label-collecting issues, such as delay and subjective ambiguity from surveys, have not been properly addressed. This work deployed the Wearable Stress and Affect Detection (WESAD) dataset, which gathered Electrodermal Activity (EDA) and Heart Rate (HR) data from 15 subjects using wearable sensors. These physiological features are extracted using an Autoencoder and categorized using two unsupervised machine learning algorithms namely K-means and Agglomerative. Experimental results showed that agglomerative clustering obtained in this proposed method outperformed best with a silhouette score of 0.85.
DOI:10.1109/RAEEUCCI61380.2024.10547833