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    Rights statement: Preprint of an article published in International Journal of Neural Systems, 27, 2, 2017, 1650041 http://dx.doi.org/10.1142/S0129065716500416 © copyright World Scientific Publishing Company http://www.worldscientific.com/worldscinet/ijns

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Stress detection using wearable physiological and sociometric sensors

Research output: Contribution to journalJournal article

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  • Oscar Martinez-Mozos
  • Virginia Sandulescu
  • Sally Andrews
  • David Alexander Ellis
  • Nicola Bellotto
  • Radu Dobrescu
  • Jose Manuel Ferrandez
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Article number1650041
<mark>Journal publication date</mark>03/2017
<mark>Journal</mark>International Journal of Neural Systems
Issue number2
Volume27
Number of pages16
<mark>State</mark>Published
Early online date21/07/16
<mark>Original language</mark>English

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 neighbour. 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.

Bibliographic note

Preprint of an article published in International Journal of Neural Systems, 27, 2, 2017, 1650041 http://dx.doi.org/10.1142/S0129065716500416 © copyright World Scientific Publishing Company http://www.worldscientific.com/worldscinet/ijns