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Stress Detection Using Wearable Physiological and Sociometric Sensors

2016; World Scientific; Volume: 27; Issue: 02 Linguagem: Inglês

10.1142/s0129065716500416

1793-6462

Óscar Martínez Mozos, Virginia Săndulescu, Sally Andrews, David A. Ellis, Nicola Bellotto, Radu Dobrescu, José Manuel Ferrández Vicente,

Mental Health Research Topics

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 [Formula: see text]-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.