Emotions States Recognition Based on Physiological Parameters by Employing of Fuzzy-Adaptive Resonance Theory

Mahdis Monajati, Seyed Hamidreza Abbasi, Fereidoon Shabaninia, Sina Shamekhi

Biomedical Eng. Department, Electrical Eng. Faculty of Sahand University of Technology, Tabriz, Iran.
School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.

DOI: 10.4236/ijis.2012.224022                

Abstract

This paper is an investigation on negative emotions states recognition by employing of Fuzzy Adaptive Resonance Theory (Fuzzy-ART) considering the changes in activities of autonomic nervous system (ANS). Specific psychological experiments were designed to induce appropriate physiological responses on individuals in order to acquire a suitable database for training, validating and testing the proposed procedure. In this research, the three physiological applied signals are Galvanic Skin Response (GSR), Heart Rate (HR) and Respiration Rate (RR). The first experiment which is named Shock was designed to determine a criterion for the change of physiological signals of each individual. In the second one, a combination of two sets of questions has been asked from the subjects to induce their emotions. Finally, Physiological responses were analyzed by Fuzzy-ART to recognize which question excites the negative emotions. Detecting negative emotions from neutral is obtained with total accuracy of 94%.

Keywords

Physiological Signal; Galvanic Skin Response; Heart Rate; Respiration Rate; Fuzzy-ART; Emotion Recognition

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	R. M. Stern, W. J. Ray and K. S. Quigley, “Psychophysiological Recording,” 2nd Edition, Oxford University Press, New York, 2001. http://books.google.com/books/about/Psychophysiological_Recording.html
[2]	“Psychophysiology Studies,” http://www.adinstruments.com/solutions/research/applications/psychophysiology-studies#method
[3]	J. T. Cacioppo, G. G. Berntson, J. T. Larsen, K. M. Poehlmann and T. A. Ito, “The Psychophysiology of Emotion,” 2nd Edition, Guilford Press, New York, pp. 173-191.
[4]	E. B. Ford, “Lie Detection: Historical, Neuropsychiatric and Legal Dimensions,” International Journal of Law and Psychiatry, Vol. 29, No. 3, 2006, pp. 159-177. doi:10.1016/j.ijlp.2005.07.001
[5]	Committee to Review the Scientific Evidence on the Polygraph and National Research Council, “The Polygraph and Lie Detection,” The National Academies Press, Washington DC, 2003. http://www.nap.edu/catalog/10420.html
[6]	W. G. Iacono, “Accuracy of Polygraph Techniques: Problems Using Confessions to Determine Ground Truth,” Physiology & Behavior, Vol. 95, No. 1-2, 2008, pp. 24-26. doi:10.1016/j.physbeh.2008.06.001
[7]	E. Leon, G. Clarke, V. Callaghan and F. Sepulveda, “Real-Time Detection of Emotional Changes for Inhabited Environments,” Computers & Graphics, Vol. 28, No. 5, 2004, pp. 635-642. doi:10.1016/j.cag.2004.06.002
[8]	P. Ekman, “Facial Expression and Emotion,” American Psychologist, Vol. 48, No. 4, 1993, pp. 384-392. doi:10.1037/0003-066X.48.4.384
[9]	H. Holzapfel, M. Denecke, C. Fuegen and A. Waibel “Integrating Emotional Cues into a Framework for Dialogue Management,” Proceedings of Fourth IEEE International Conference on Multimodal Interfaces (ICMI’02), Pittsburgh, 14-16 October 2002, pp. 141-146.
[10]	C. D. Katsis, N. Katertsidis, G. Ganiatsas and D. I. Fotiadis, “Toward Emotion Recognition in Car-Racing Drivers: A Biosignal Processing Approach,” IEEE Transactions on Systems, Man and Cybernetics—Part A: Systems and Humans, Vol. 38, No. 3, 2008, pp. 502-512. doi:10.1109/TSMCA.2008.918624
[11]	G. Turpin and L. Harrison, “Electrodermal Activity,” In: G. Fink, Ed., Encyclopedia of Stress, Academic Press, San Diego, 2000, pp. 25-27. http://books.google.com/books/about/Encyclopedia_of_Stress.html
[12]	C. Zocchi, A. Rovetta and F. Fanfulla, “Physiological Parameters Variation during Driving Simulations,” IEEE International Conference on Advanced Intelligent Mecha- teronics, Zurich, 4-7 September 2007, pp. 1-6.
[13]	S. Layeghi, M. Dastmalchi, E. Jacobs and R. B. Knapp, “Pattern Recognition of the Polygraph Using Fuzzy Classification,” Proceedings of the Third IEEE Conference on Fuzzy Systems, Orlando, 26-29 June 1994, pp. 1825-1829. doi:10.1109/FUZZY.1994.343582
[14]	E. Jang, B. Park, S. Kim, C. Huh, Y. Eum and J. Sohn “Emotion Recognition through ANS Responses Evoked by Negative Emotions,” The Fifth International Conference on Advances in Computer-Human Interactions (ACHI), Valencia, 30 January-4 February 2012, pp. 218-223.
[15]	C. Chang, J. Zheng, C. Wang and P. Chung “Application of Support Vector Regression for Physiological Emotion Recognition,” International Computer Symposium, Tainan, 16-18 December 2010, pp. 12-17.
[16]	C. K. Lee, S. K. Yoo, Y. J. Park, N. H. Kim, K. S. Jeong and B. C. Lee, “Using Neural Network to Recognize Human Emotions from Heart Rate Variability and Skin Resistance,” 27th Annual Conference on Engineering in Medicine and Biology, Shanghai, 17-18 January 2006, pp. 5523-5525.
[17]	J. N. Bailenson, E. D. Pontikakis, I. B. Mauss, J. J. Gross, M. E. Jabon, C. A. C. Hutcherson, C. Nass and O. John, “Real-Time Classification of Evoked Emotions Using Facial Feature Tracking and Physiological Responses,” International Journal of Human-Computer Studies, Vol. 66, 2008, pp. 303-317. doi:10.1016/j.ijhcs.2007.10.011
[18]	A. S. Sierra, C. S. ávila, J. G. Casanova and G. B. Pozo “A Stress-Detection System Based on Physiological Signals and Fuzzy Logic,” IEEE Transactions on Industrial Electronics, Vol. 58, No. 10, 2011, pp. 4857-4865. doi:10.1109/TIE.2010.2103538
[19]	P. Karthikeyan, M. Murugappan and S. Yaacob, “A Review on Stress Inducement Stimuli for Assessing Human Stress Using Physiological Signals,” IEEE 7th International Colloquium on Signal Processing and Its Applications, Penang, 4-6 March 2011, pp. 420-425.
[20]	B. Aysin, J. Colombo and E. Aysin, “Comparison of HRV Analysis Methods during Orthostatic Challenge: HRV with Respiration or without?” Proceedings of the 29th Annual International Conference of the IEEE EMBS, Lyon, 23-26 August 2007, pp. 5047-5050.
[21]	R. J. Bryg, T. Vybiral and M. Maddens, “The Effect of Controlled Respiration on Parameters of Heart Rate Variability,” Proceedings of the IEEE Conference on Computers in Cardiology, Chicago, 23-26 September 1990, pp. 255-258.
[22]	M. E. Dawson, A. M. Schell and D. L. Filion, “Handbook of Psychophysiology,” Cambridge University Press, New York, 2000.
[23]	M. T. Jones, “Artificial Intelligence: A Systems Approa- ch,” Infinity Science Press, Hingham, 2008.
[24]	G. A. Carpenter, S. Grossberg and D. B. Rosen, “Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by Adaptive Resonance System,” Neural Networks, Vol. 4, 1991, pp. 759-771. doi:10.1016/0893-6080(91)90056-B
[25]	L. Jain, B. Lazzerini and H. Ugur, “Innovations in Art Neural Networks,” Physica-Verlag, Heidelberg, 2000. http://books.google.com/books/about/Innovations_in_Art_Neural_Networks.html
[26]	D. Papada and K. W. Jablokow, “Conceptual Design of a Driving Habit Recognition Framework,” IEEE Symposium on Computational Intelligence in Vehicle and Transportation Systems, Paris, 11-15 April 2011, pp. 59-66.
[27]	H. Isawa, H. Matsushita and Y. Nishio, “Fuzzy ART Combining Overlapped Categories Using Variable Vigilance Parameters,” International Workshop on Nonlinear Circuits and Signal Processing, Waikiki, 1-3 March 2009, pp. 661-664.