TY - CHAP
T1 - Stress detection using wearable physiological sensors
AU - Sandulescu, Virginia
AU - Andrews, Sally
AU - Ellis, David
AU - Bellotto, Nicola
AU - Martinez-Mozos, Oscar
N1 - 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
PY - 2015/6/1
Y1 - 2015/6/1
N2 - 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.
AB - 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.
KW - Stress detection
KW - Wearable physiological sensors
KW - Assistive technologies
KW - Signal classification
KW - Quality of life technologies
U2 - 10.1007/978-3-319-18914-7_55
DO - 10.1007/978-3-319-18914-7_55
M3 - Chapter or section
SN - 9783319189130
T3 - Lecture Notes in Computer Science
SP - 526
EP - 532
BT - Artificial computation in biology and medicine
PB - Springer, Singapore
CY - Cham
ER -
