Sandulescu, Virginia and Andrews, Sally and Ellis, David and Bellotto, Nicola and Martinez-Mozos, Oscar (2015) Stress detection using wearable physiological sensors. In: Artificial computation in biology and medicine. Lecture Notes in Computer Science . Springer, Cham, pp. 526-532. ISBN 9783319189130
proof.pdf - Accepted Version
Available under License Creative Commons Attribution.
Download (302kB)
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.