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Stress detection using wearable physiological sensors

Research output: Contribution in Book/Report/ProceedingsChapter

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  • Virginia Sandulescu
  • Sally Andrews
  • David Ellis
  • Nicola Bellotto
  • Oscar Martinez-Mozos
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Publication date1/06/2015
Host publicationArtificial computation in biology and medicine
Place of PublicationCham
PublisherSpringer
Pages526-532
Number of pages7
ISBN (Electronic)9783319189147
ISBN (Print)9783319189130
<mark>Original language</mark>English

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume9107
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Abstract

As the population increases in the world, the ratio of health carers is rapidly decreasing. Therefore, there is an urgent need to create new technologies to monitor the physical and mental health of people during their daily life. In particular, negative mental states like depression and anxiety are big problems in modern societies, usually due to stressful situations during everyday activities including work. This paper presents a machine learning approach for stress detection on people using wearable physiological sensors with the final aim of improving their quality of life. The presented technique can monitor the state of the subject continuously and classify it into ”stressful” or ”non-stressful” situations. Our classification results show that this method is a good starting point towards real-time stress detection.

Bibliographic note



Evidence can be found via the conference program here (page 14) http://www.iwinac.uned.es/iwinac2015/Full-Prog.pdf

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-18914-7_55