Stress detection using wearable physiological sensors

Sandulescu, Virginia, Andrews, Sally, Ellis, David , Bellotto, Nicola and Martinez Mozos, Oscar (2015) Stress detection using wearable physiological sensors. Lecture Notes in Computer Science, 9107 . pp. 526-532. ISSN 0302-9743

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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.

Additional Information:Series: Lecture Notes in Computer Science Artificial Computation in Biology and Medicine: International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2015, Elche, Spain, June 1-5, 2015, Proceedings, Part I
Keywords:stress detection, wearable physiological sensors, assistive technologies, signal classification, quality of life technologies, bmjgoldcheck, NotOAChecked
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G400 Computer Science
C Biological Sciences > C841 Health Psychology
Divisions:College of Science > School of Computer Science
College of Social Science > School of Psychology
ID Code:17143
Deposited On:15 Apr 2015 15:26

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