Abstract
Improving psychologically disabled people’s life quality and integration in society is strongly linked with providing them higher levels of autonomy. Occasionally, these people suffer from emotional blockages produced by situations that can be overwhelming for them. Thus, detecting whether the person is entering a mental blockage produced by stress can facilitate to mitigate the symptoms of that blockage. This work presents different enhancements and variations for an existing fuzzy logic stress detection system based on monitoring different physiological signals (heart rate and galvanic skin response). It proposes a method based on wavelet processing to improve the detection of R peaks of electrocardiograms. It also proposes to decompose the galvanic response signal into two components: the average value and the variations.
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References
Bradley, M., et al.: Measuring emotion: The self-assessment manikin and the semantic differential. J. Behavioral Therapy & Experimental Psychiatry 25, 49–59 (1994)
Parkka, J., Ermes, M., Van Gils, M.: Automatic feature selection and classification of physical and mental load using data from wearable sensors. In: 10th IEEE Int. Conf. on Information Technology and Applications in Biomedicine (ITAB), pp. 1–5 (2010)
Sharma, N., Gedeon, T.: Artificial Neural Network Classification Models for Stress in Reading. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part IV. LNCS, vol. 7666, pp. 388–395. Springer, Heidelberg (2012)
Gonzalez, J.: Providing telecommunications access to people with special needs. IEEE J. on Selected Areas in Communication 9(4), 601–604 (1991)
de Santos Sierra, A., et al.: A Stress-Detection System Based on Physiological Signals and Fuzzy Logic. IEEE Trans. on Ind. Electronics 58(10), 4857–4865 (2011)
Sato, W., et al.: Emotion Elicitation Effect of Films in a Japanese Sample. Social Behavior and Personality 35(7), 863–874 (2007)
Bloch, S., et al.: Specific respiratory patterns distinguish among human basic emotions. International Journal of Psychophysiology 11, 141–154 (1991)
Ekman, P., et al.: Autonomic nervous system activity distinguishes among emotions. Science 221, 1208–1210 (1983)
Coan, J., Allen, J.: Frontal EEG asymmetry as a moderator and mediator of emotion. Biological Psychology 67, 7–50 (2004)
Martínez, R., et al.: First results in modelling stress situations by analysing physiological human signals. In: Proc. of IADIS Int. Conf. on e-Health, pp. 171–175 (2012)
Sakr, G.E., et al.: Subject independent agitation detection. In: IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics (AIM 2008), pp. 200–204 (2008)
Lee, C.K., et al.: Using Neural Network to Recognize Human Emotions from Heart Rate Variability and Skin Resistance. In: IEEE 27th Annual Int. Conf. of the Engineering in Medicine and Biology Society, pp. 5523–5525 (2006)
Woźniak, M., Graña, M.: Emilio Corchado A survey of multiple classifier systems as hybrid systems. Information Fusion 16, 3–17 (2014)
Luís Calvo-Rolle, J., Corchado, E.: A Bio-inspired knowledge system for improving combined cycle plant control tuning. Neurocomputing 126, 95–105 (2014)
Mokhayeri, F., Akbarzadeh-T, M.-R.: Mental Stress Detection Based on Soft Computing Techniques. In: IEEE Int. Conf. on Bioinformatics and Biomedicine, pp. 430–433 (2011)
Sakr, G.E., et al.: Support Vector Machines to Define and Detect Agitation Transition. IEEE Trans. on Affective Computing 1(2), 98–108 (2010)
Nelson, R.J.: An Introduction to Behavioral Endocrinology. Massachussets. Sinauer Associates 11, 557–591 (2000)
Sasikala, P., Wahidabanu, R.S.D.: Robust R Peak and QRS detection in Electrocardiogram using Wavelet Transform. In: Int. J. of Advanced C. Science and Applications, vol. 1 (2010)
Hong-tu, Z., Jing, Y.: The Wavelet Decomposition and Reconstruction Based on The Matlab. In: Proc. of the Third Int. Symposium on Electronic Commerce and Security Workshops (ISECS 2010), China (2010)
Talbi, et al.: New Method of R-Wave Detection by Continuous Wavelet Transform. Signal Processing: An International Journal (SPIJ) 5, 165–173 (2011)
de Lannoy, G., et al.: A Supervised Wavelet Transform Algorithm for R Spike Detection in Noisy ECGs. In: Fred, A., Filipe, J., Gamboa, H. (eds.) BIOSTEC 2008. CCIS, vol. 25, pp. 256–264. Springer, Heidelberg (2008)
Martis, R.J., Chakraborty, C., Ray, A.K.: Wavelet Based Machine Learning Techniques for ECG Analysis. In: Machine Learning in Healthcare Informatics, pp. 25–45 (2014)
Gross, J.J., Levenson, R.W.: Emotion Elicitation Using Films. Cognition and Emotion 9, 87–108 (1995)
CSEA-NIMH: The international affective picture system: Digitalized photographs. The Center of Research in Psychophysiology, Florida (1999)
Lang, P.J.: Behavioral treatment and bio-behavioral assessment: Computer applications. In: Technology in Mental Health and Delivery Systems, pp. 119–137 (1980)
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Salazar-Ramirez, A., Irigoyen, E., Martinez, R. (2014). Enhancements for a Robust Fuzzy Detection of Stress. In: de la Puerta, J., et al. International Joint Conference SOCO’14-CISIS’14-ICEUTE’14. Advances in Intelligent Systems and Computing, vol 299. Springer, Cham. https://doi.org/10.1007/978-3-319-07995-0_23
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DOI: https://doi.org/10.1007/978-3-319-07995-0_23
Publisher Name: Springer, Cham
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