Comparison of Machine Learning Techniques for Psychophysiological Stress Detection-Reference-Cited by-同舟云学术

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Comparison of Machine Learning Techniques for Psychophysiological Stress Detection

Published:2016 Issue: Volume: Page:13-22
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
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Author:

Smets Elena,Casale Pierluigi,Großekathöfer Ulf,Lamichhane Bishal,De Raedt Walter,Bogaerts Katleen,Van Diest Ilse,Van Hoof Chris

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-32270-4_2

Reference23 articles.

1. Kawakami, N., Haratani, T.: Epidemiology of job stress and health in Japan: review of current evidence and future direction. Ind. Health 37, 174–186 (1999)

2. Bakker, A., Demerouti, E.V.W.: Using the job demands-resources model to predict burnout and performance. Hum. Resour. Manag. 43, 83–104 (2004)

3. Hoehn, T., Braune, S., Scheibe, G., Albus, M.: Physiological, biochemical and subjective parameters in anxiety patients with panic disorder during stress exposure as compared with healthy controls. Eur. Arch. Psychiatry Clin. Neurosci. 247, 264–274 (1997)

4. Karthikeyan, P., Muragappan, M., Yaacob, S.: Analysis of Stroop Color-Word test-based human stress detection using electrocardiography and heart rate variability signals. Arab. J. Sci. Eng. 39, 1835–1847 (2014)

5. Wijsman, J., Grundlehner, B., Hermens, H.: Wearable physiological sensors reflect mental stress state in office-like situations. In: Humaine Association Conference on Affective Computing and Intelligent Interaction (2013)

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