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Stress Detection Using Wearable Physiological Sensors

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9107)


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.


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

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  • DOI: 10.1007/978-3-319-18914-7_55
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Correspondence to Virginia Sandulescu .

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© 2015 Springer International Publishing Switzerland

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Sandulescu, V., Andrews, S., Ellis, D., Bellotto, N., Mozos, O.M. (2015). Stress Detection Using Wearable Physiological Sensors. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Artificial Computation in Biology and Medicine. IWINAC 2015. Lecture Notes in Computer Science(), vol 9107. Springer, Cham.

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