Stress detection using wearable physiological sensors

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

Research output: Chapter or section in a book/report/conference proceedingChapter or section

96 Citations (SciVal)


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
Number of pages7
ISBN (Print)9783319189130
Publication statusPublished - 1 Jun 2015

Publication series

NameLecture Notes in Computer Science

Bibliographical note

Evidence can be found via the conference program here (page 14) The final publication is available at Springer via


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


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