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arXiv · 2604.12746

Stress Detection Using Wearable Physiological and Sociometric Sensors

Oscar Martinez Mozos·Virginia Sandulescu·Sally Andrews·David Ellis·Nicola Bellotto·Radu Dobrescu·Jose Manuel Ferrandez
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Abstract

Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and k-nearest neighbor. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST). Moreover, this paper assesses the discriminative ability of each sensor modality individually and considers their suitability for real-time stress detection. Finally, we present an study of the most discriminative features for stress detection.

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Oscar Martinez Mozos, Virginia Sandulescu, Sally Andrews, David Ellis, Nicola Bellotto, Radu Dobrescu, Jose Manuel Ferrandez. 2026-04-14. Stress Detection Using Wearable Physiological and Sociometric Sensors. https://doi.org/10.1142/s0129065716500416

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