Stress detection using wearable physiological and sociometric sensors

Oscar Martinez-Mozos, Virginia Sandulescu, Sally Andrews, David Alexander Ellis, Nicola Bellotto, Radu Dobrescu, Jose Manuel Ferrandez

Research output: Contribution to journalArticle

43 Citations (Scopus)

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.
Original languageEnglish
JournalInternational Journal of Neural Systems
Volume27
Issue number2
DOIs
Publication statusPublished - Mar 2017

Keywords

  • Activity monitoring
  • assistive technologies
  • physiology
  • sensors
  • signal classification
  • sociometric badges
  • stress
  • stress detection
  • wearable technology

Cite this

Martinez-Mozos, O., Sandulescu, V., Andrews, S., Ellis, D. A., Bellotto, N., Dobrescu, R., & Ferrandez, J. M. (2017). Stress detection using wearable physiological and sociometric sensors. International Journal of Neural Systems, 27(2). https://doi.org/10.1142/S0129065716500416