AN INVESTIGATION OF DECISION ANALYTIC METHODOLOGIES FOR STRESS IDENTIFICATION

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering , Engineering, Electrical & Electronic

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VOLUME 6 , ISSUE 4 (September 2013) > List of articles

AN INVESTIGATION OF DECISION ANALYTIC METHODOLOGIES FOR STRESS IDENTIFICATION

Yong Deng * / Chao-Hsien Chu * / Huayou Si * / Qixun Zhang * / Zhonghai Wu *

Keywords : Stress detection, physiological sensors, feature selection, information fusion, classification

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 6, Issue 4, Pages 1,675-1,699, DOI: https://doi.org/10.21307/ijssis-2017-610

License : (CC BY-NC-ND 4.0)

Received Date : 18-January-2013 / Accepted: 31-January-2013 / Published Online: 05-September-2013

ARTICLE

ABSTRACT

In modern society, more and more people are suffering from some type of stress. Monitoring and timely detecting of stress level will be very valuable for the person to take counter measures. In this paper, we investigate the use of decision analytics methodologies to detect stress. We present a new feature selection method based on the principal component analysis (PCA), compare three feature selection methods, and evaluate five information fusion methods for stress detection. A driving stress data set created by the MIT Media lab is used to evaluate the relative performance of these methods. Our study show that the PCA can not only reduce the needed number of features from 22 to five, but also the number of sensors used from five to two and it only uses one type of sensor, thus increasing the application usability. The selected features can be used to quickly detect stress level with good accuracy (78.94%), if support vector machine fusion method is used.

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REFERENCES

[1] H. Abdi, L.J. Williams, “Principal Components Analysis”, Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 2, No. 4, 2010, pp. 433–459.
[2] A. Akbas, “Evaluation of the Physiological Data Indicating the Dynamic Stress Level of Drivers”, Scientific Research and Essays, Vol. 6, No. 2, 2011, pp. 430-439.
[3] APA (American Psychological Association), “Stress in America: Our Health at Risk”, Accessed on June 2012. URL: http://www.apa.org/news/press/releases/stress/index.aspx
[4] F. Angus, J. Zhai, “Front-end Analog Pre-processing for Real Time Psychophysiological Stress Measurements”, Proceedings of the 9th World Multi-Conference on Systematics, Cybernetics and Informatics (WMSCI05), 2005, pp. 218-221.
[5] J. Bakker, M. Pechenizkiy, N. Sidorava, “What’s Your Current Stress Level? Detection of Stress Patterns from GSR Sensor Data”, Proceedings of the11th IEEE International Conference on Data Mining Workshops, 2011, pp. 573-580.
[6] L. Bergman, P. Corabian, C. Harstall, “Effectiveness of Organisational Interventions for the Prevention of Occupational Stress”, Alberta: Institute of Health Economics, Accessed on June 2012. URL: http://www.ihe.ca/publications/library/2009/effectiveness-of-organizational-interventions-for-the-prevention-of-workplace-stress/
[7] A.-M. Cretu, and P. Payeur, “Biologically-inspired Visual Attention Features for a Vehicle Classification Task”, The International Journal on Smart Sensing and Intelligent Systems, Vol. 4, No. 3, 2011, pp. 402-423.
[8] J. R.T. Davidson, S.W. Book, “Assessment of a New Self-Rating Scale for Post-traumatic Stress Disorder”, Psychological Medicine, Vol. 27, No. 1, 1997, pp.153-160.
[9] R. Duda, P. Hart., D. Stork, “Pattern Classification”, (2nd Ed.).Wiley Inter-science, 2001
[10] FlexComp, “ProComp Software Version 1.41 User’s Manual”, Thought Technology Ltd., Montreal, QC, Canada, 1994.
[11] M. Hall, “Correlation Based Feature Selection for Machine Learning”, Doctoral Dissertation, University ofWaikato, 1999.
[12] S. Haykin, “Neural Networks: A Comprehensive Foundation (2nd Ed.)”, Englewood Cliffs, NJ: Prentice-Hall, 1998.
[13] J.A. Healey, “Wearable and Automotive Systems for Affect Recognition from Physiology”, Doctoral Dissertation, Massachusetts Institute of Technology, MA, 2000.
[14] J.A. Healy, R.W. Picard, “Detecting Stress During Real-World Driving Tasks Using Physiological Sensors”, IEEE Transaction on Intelligent Transportation System, Vol. 6, No. 2, 2005, pp.156-166.
[15] E. Jovanov, A. O’Donnell Lords, D. Raskovic, P.G. Cox, R. Adhami, F. Andrasik, “Stress Monitoring Using a Distributed Wireless Intelligent Sensor System”, IEEE Engineering in Medicine and Biology Magazine, Vol. 22, No. 3, 2003, pp. 49-55.
[16] A. Kaklauskas, E.K. Zavadskas, V. Pruskus, A. Vlasenko, L. Bartkiene, “Recommended Biometric Stress Management System”, Expert Systems with Applications, Vol. 38, 2011, pp.14011-14025.
[17] A. Malhi, R. Gao, “Feature Selection for Defect Classification in Machine Condition Monitoring”, 20th IEEE Instrumentation Measurement Technology Conf., Vol. 1, 2003, Vail, CO, pp. 36-41.
[18] A. Moosavian, H. Ahmadi, A. Tabatabaeefar, B. Sakhaei, “An Appropriate Procedure for Detection of Journal-Bearing Fault Using Power Spectral Density, K-Nearest Neighbor and Support Vector Machine”, The International Journal on Smart Sensing and Intelligent Systems,Vol.5, No. 3, 2012, pp.685-700.
[19] M. Nako, “Work-related Stress and Psychosomatic Medicine”, BioPsycho Social Medicine, Vol. 4, No. 4, 2010, Doi:10.1186/1751-0759-4-4.
[20] Office for National Statistics, Social and Vital Statistics Division and Northern Ireland Statistics and Research Agency. Central Survey Unit, 2010. “Labour Force Survey, 1975-2010”, Colchester, Essex: UK Data Archive. URL:http://www.esds.ac.uk/government/lfs/
[21] PHYSIONET, “Stress Recognition in Automobile Drivers (drivedb)”, Accessed on June 2012. URL: http://physionet.org/cgi-bin/atm/ATM/.
[22] K. Polat, S. Güneş, “A Novel Hybrid Intelligent Method Based on C4.5 Decision Tree Classifier and One-against-all Approach for Multi-Class Classification Problems”, Expert Systems with Applications, Vol. 36, 2009, pp. 1587-1592.
[23] I. Rish, “An Empirical Study of the Naive Bayes Classifier”, Proceedings of IJCAI-01 workshop on Empirical Methods in AI, 2001, pp. 41-46, Sicily, Italy.
[24] S. Ruggieri, “Efficient C4.5”, IEEE Transactions on Knowledge and Data Engineering, Vol. 14, No. 2, 2002, pp. 438-444.
[25] V. Vapnik, “The Nature of Statistical Learning Theory”, Springer-Verlag, New York, NY, USA. 1995. ISBN: 0-387-94559-8.
[26] D. Watson, J.W. Pennebaker, “Health Complaints, Stress, and Distress: Exploring the Central Role of Negative Affectivity”, Psychological Review, Vol. 96, No. 2, 1989, pp. 234-254.
[27] S. Wold, “Principal Component Analysis”, Chemometrics and Intelligent Laboratory Systems, Vol. 2, No. 1-3, 1987, pp. 37-52.
[28] K.Y. Yeung, W.L. Ruzzo, “Principal Component Analysis for Clustering Gene Expression Data”, Bioinformatics, Vol. 17, No. 9, 2001, pp. 763-774.
[29] L. Yu, H. Liu, “Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution”, Proceedings of the 20th International Conference on Machine Learning (ICML-2003), Washington, DC, Vol. 3, 2003, pp. 856-863.
[30] J. Zhai, A. Barreto, “Stress Detection in Computer Users Through Non-Invasive Monitoring of Physiological Signals”, Biomedical Science Instrumentation, Vol. 42, 2006, pp. 495-500.
[31] L. Zhang, T. Tamminedi, A. Ganguli, G. Yosiphon, J. Yadegar, “Hierarchical Multiple Sensor Fusion Using Structurally Learned Bayesian Network”, Proceedings of Wireless Health, 2010, pp. 174-183.

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