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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  3/2014 - 9

 HIGHLY CITED PAPER 

Kohonen Neural Network Stress Detection Using Only Electrodermal Activity Features

BORNOIU, I.-V. See more information about BORNOIU, I.-V. on SCOPUS See more information about BORNOIU, I.-V. on IEEExplore See more information about BORNOIU, I.-V. on Web of Science, GRIGORE, O. See more information about GRIGORE, O. on SCOPUS See more information about GRIGORE, O. on SCOPUS See more information about GRIGORE, O. on Web of Science
 
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Download PDF pdficon (958 KB) | Citation | Downloads: 697 | Views: 2,891

Author keywords
biomedical signal processing, data analysis, electrophysiology, pattern recognition, self organizing feature maps

References keywords
stress(7), electrodermal(6), activity(5), emotion(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-08-31
Volume 14, Issue 3, Year 2014, On page(s): 71 - 78
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.03009
Web of Science Accession Number: 000340869800009
SCOPUS ID: 84907310113

Abstract
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This paper presents a method for identifying human stress levels by using a Kohonen neural network. The study focuses on differentiating between a relaxed and a stressed state and it presents a series of parameters (skin conductance response signal power, skin conductance response signal frequency, skin conductance level gradient, response rise time and response amplitude) extracted only from the electrodermal activity signal. A very strict recording protocol was used to minimize the artifacts caused by the bad connection between electrodes and skin. A stress inducing method is presented that can be used to replicate results in laboratory conditions.


References | Cited By  «-- Click to see who has cited this paper

[1] G. Rigas, C. Katsis, P. Bougia and D. Fotiadis, "A Reasoning-Based Framework for Car Driver’s Stress Prediction", in Proc. of 16th Mediterranean Conference on Control and Automation, Ajaccio, Corsica, France, pp. 627 - 632, Jun. 2008.

[2] J. A. Healey, R. W. Picard, "Detecting Stress during Real-World Driving Tasks Using Physiological Sensors", in IEEE Transactions on Intelligent Transportation Systems, vol. 6, issue 2, pp. 156-166, Jun. 2005.
[CrossRef] [Web of Science Times Cited 1012] [SCOPUS Times Cited 1291]


[3] J. Zhai, A. Barreto, "Stress Detection in Computer Users through Non-Invasive Monitoring of Physiological Signals", in Biomedical Sciences Instrumentation, vol. 42, pp. 495-500, 2006.

[4] A. Drachen, L. E. Nacke, G. Yannakakis, A. L. Pedersen, "Correlation between Heart-Rate, Electrodermal Activity and Player Experience in First-Person Shooter Games", in Proc. of the 5th ACM SIGGRAPH, ACM SIGGRAPH Publishers, pp. 49-54, 2009.
[CrossRef] [SCOPUS Times Cited 182]


[5] D. Kulic, E. A. Croft, "Affective State Estimation for Human-Robot Interaction", in IEEE Transactions on Robotics, vol. 23, issue 5, pp. 991-1000, Oct. 2007.
[CrossRef] [Web of Science Times Cited 136] [SCOPUS Times Cited 157]


[6] G. Rigas, C. D. Katsis, G Ganiatsas, D.I. Fotiadis, " A User Independent, Biosignal Based, Emotion Recognition Method", in Proc. of the 11th International Conference, Corfu, Greece, pp. 314-318, 2007.
[CrossRef] [SCOPUS Times Cited 55]


[7] B. Wolfram, Electrodermal Activity, Springer US, 2nd ed., 2012.
[CrossRef] [Web of Science Times Cited 1131] [SCOPUS Times Cited 1217]


[8] M. E. Dawson, A. M. Schell, D. Filion, "The Electrodermal System", in Handbook of Psychophysiology 3rd ed., New York: Cambridge University Press, 2007.
[CrossRef]


[9] S. Schmidt, R. Schnider, M. Binder, D. Burkele, H. Walach, "Investigating Methodological Issues in EDA-DMILS: Results from a Pilot Study", Journal of Parapsychology, vol. 65, pp. 59-82, 2001.

[10] R. Henriques, A. Paiva, C. Antunes, "On the Need of New Methods to Mine Electrodermal Activity in Emotion-Centered Studies", in 8th International Workshop, ADMI 2012, Valencia, Spain, pp. 203-215, Jun. 2012.
[CrossRef] [SCOPUS Times Cited 18]


[11] R. Henriques, A. Paiva, C. Antunes, "Accessing Emotion Patterns from Affective Interactions Using Electrodermal Activity", in 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), Geneva, pp. 43-48, Sept. 2013.
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 18]


[12] C. Kirschbaum, K. M. Pirke, D. Hellhammer, "The 'Trier Social Stress Test' - A Tool for Investigating Psychobiological Stress Responses in a Laboratory Setting", in Neuropsychobiology, no. 28, pp. 76-81, 1993.

[13] M. V. Thoma, R. La Marca, R. Bronnimann, L. Finkel, U. Ehlert, U.M. Nater, "The Effect of Music on the Human Stress Response", in PLoS ONE, vol. 8, 2013.
[CrossRef] [Web of Science Times Cited 142] [SCOPUS Times Cited 163]


[14] S. Schmidt, H. Walach, "Electrodermal Activity (EDA) - State-of-the-Art Measurement and Techniques for Parapsychological Purposes", in Journal of Parapsychology, vol. 64, pp. 139-163, 2000.

[15] I. V. Bornoiu, O. Grigore, "A Study about Feature Extraction for Stress Detection", 2013 8th International Symposium in Advanced Topics in Electrical Engineering (ATEE), Bucharest, Romania, May 2013.
[CrossRef] [Web of Science Times Cited 20] [SCOPUS Times Cited 23]


[16] K. Kim, S.W. Bang, S.R. Kim, "Emotion Recognition System Using Short-Term Monitoring of Physiological Signals", in Medical and Biological Engineering and Computing 2004, vol. 42, pp. 419-427, 2004.
[CrossRef] [Web of Science Times Cited 525] [SCOPUS Times Cited 683]


[17] T. Cover, P. Hart, "Nearest neighbor pattern classification", in IEEE Transactions on Information Theory, vol. 13, issue 1, pp. 21-27, January 1967.
[CrossRef] [SCOPUS Times Cited 8919]


[18] T. Kohonen, "The Self-Organizing Map", Proceedings IEEE, vol. 78, no. 9, pp. 1464-1479, Sept. 1990.
[CrossRef] [Web of Science Times Cited 4660] [SCOPUS Times Cited 5879]


[19] T. Kohonen, Self-Organizing Maps, Springer-Verlag, Berlin, 1995.
[CrossRef]


[20] S. Haykin, Neural Networks. A comprehensive foundation, Second Edition, Prentice Hall, 1999.

[21] R. Rojas, Neural Networks. A systematic introduction, Berlin, 1996.

[22] M. Su, T. Liu, H. Chang, "Improving the Self-Organizing Feature Map Algorithm Using an Efficient Initialization Scheme", in Tamkang Journal of Science and Engineering, vol.5, no.1, pp. 35-48, 2002.

[23] I. T. Jolliffe, Principal Component Analysis, Springer Series in Statistics, 2nd ed., Springer, NY, 2002.



References Weight

Web of Science® Citations for all references: 7,639 TCR
SCOPUS® Citations for all references: 18,605 TCR

Web of Science® Average Citations per reference: 318 ACR
SCOPUS® Average Citations per reference: 775 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2022-11-21 22:45 in 101 seconds.




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Stefan cel Mare University of Suceava, Romania


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