Artículo

Stress detection using wearable physiological and sociometric sensors

  • Oscar Martinez Mozos /
  • Virginia Sandulescu /
  • Sally Andrews /
  • David Ellis /
  • Nicola Bellotto /
  • Radu Dobrescu /
  • Jose Manuel Ferrandez
Journal ar
International Journal of Neural Systems
  • Volumen: 27
  • Número: 2
  • Fecha: 01 marzo 2017
  • ISSN: 01290657
  • Tipo de fuente: Revista
  • DOI: 10.1142/S0129065716500416
  • Tipo de documento: Artículo
  • Editorial: World Scientific Publishing Co. Pte Ltd wspc@wspc.com.sg
© 2017 World Scientific Publishing Company. 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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