Stress Detection using Wearable Physiological Sensors Virginia Sandulescu 1 , Sally Andrews 2 , David Ellis 2 , Nicola Bellotto 2 , and Oscar Martinez Mozos 2 1 Politehnica University of Bucharest, Bucharest, Romania, s virg@yahoo.com 2 University of Lincoln, Lincoln, UK s.andrews@lincoln.ac.uk, dellis@lincoln.ac.uk, nbellotto@lincoln.ac.uk, omozos@lincoln.ac.uk 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 depres- sion 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 qual- ity 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. Keywords: stress detection, wearable physiological sensors, assistive technologies, signal classification, quality of life technologies 1 Introduction As the population increases in the world, the ratio of health carers is rapidly decreasing. Actually, the Organisation for Economic Co-operation and Devel- opment (OECD) warns about future shortages of available health workers and doctors [3]. Therefore, there is an urgent need to create new technologies to monitor the health of people, both physical and mental, during their daily life with the aim of supporting health workers, caregivers, and doctors in their tasks. These technologies, also known as Quality of Life Technologies (QoLTs), have emerged as the concept of applying findings from different technological areas to assist people and improve their quality of life. An emerging research topic inside QoLTs is their application to psychology and self-therapy to improve the mood of people and thus, their quality of life. Although there exist several technologies to support the health of people at the physiological level, the technologies that are able to provide similar support at the mental level are almost inexistent.