Stress detection using wearable physiological sensors

Virginia Sandulescu, Sally Andrews, David Ellis, Nicola Bellotto, Oscar Martinez-Mozos

Research output: Chapter in Book/Report/Conference proceedingChapter

40 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationArtificial computation in biology and medicine
Place of PublicationCham
PublisherSpringer, Singapore
Pages526-532
Number of pages7
ISBN (Print)9783319189130
DOIs
Publication statusPublished - 1 Jun 2015

Publication series

NameLecture Notes in Computer Science

Keywords

  • Stress detection
  • Wearable physiological sensors
  • Assistive technologies
  • Signal classification
  • Quality of life technologies

Cite this

Sandulescu, V., Andrews, S., Ellis, D., Bellotto, N., & Martinez-Mozos, O. (2015). Stress detection using wearable physiological sensors. In Artificial computation in biology and medicine (pp. 526-532). (Lecture Notes in Computer Science). Springer, Singapore. https://doi.org/10.1007/978-3-319-18914-7_55