Stress Detection Indicators: A Review

Sachin T. Khurade, Satya Gowali, Chiranjeevi M. C., K. S. Shivaprakasha

Abstract


Stress is the way of body's response to any kind of demand in the normal life. The body reacts to these changes in terms of physical, mental and emotional responses. There are sample number of works being carried out to measure stress using physiological parameters such as Galvanic Skin Response (GSR), Blood Pressure (BP), Heart Rate (HR), Breathing Pattern, Speech Signal, ECG (Electro cardiograph) and EEG (Electroencephalography). This paper presents a qualitative analysis of different physiological parameters used to determine the stress level. 


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Fernandes A., Helawar R., Lokesh R., Tari T. & Shahapurkar A. V. Determination of stress using blood pressure and galvanic skin response. In Communication and Network Technologies (ICCNT), 2014 International Conference (IEEE). December 2014; 18: pp. 165-168.

Nagai Y., Goldstein L.H., Fenwick P.B. & Trimble M.R. Clinical efficacy of galvanic skin response biofeedback training in reducing seizures in adult epilepsy: a preliminary randomized controlled study. Epilepsy & Behavior. April 2004;5(2): pp. 216-23.

Suryadevara N.K., Mukhopadhyay S.C. & Barrack L. Towards a smart non-invasive fluid loss measurement system. Journal of medical systems. April 2015; 39(4): pp. 38.

Barnea O. & Shusterman V. Analysis of skin-temperature variability compared to variability of blood pressure and heart rate. In Engineering in Medicine and Biology Society, 1995, IEEE 17th Annual Conference. IEEE, September 1995;2: pp. 1027-1028.

Brennan M., O'Brien E. & O’Malley K. The effect of age on blood pressure and heart rate variability in hypertension. Journal of Hypertension. December 1986; 4(6): pp. S269-72.

Widanti N., Sumanto B., Rosa P. &Miftahudin M.F. Stress level detection using heart rate, blood pressure, and GSR and stress therapy by utilizing infrared. InIndustrial Instrumentation and Control (ICIC), 2015 International Conference. IEEE, May 2015:pp. 275-279.

Melillo P., Bracale M. & Pecchia L. Nonlinear Heart Rate Variability features for real-life stress detection. Case study: students under stress due to university examination. Biomedical engineering online. 2011 Dec; 10(1): pp. 96.

Cho Y., Bianchi-Berthouze N. & Julier S.J. Deep Breath: Deep learning of breathing patterns for automatic stress recognition using low-cost thermal imaging in unconstrained settings. In Affective Computing and Intelligent Interaction (ACII), 2017 Seventh International Conference. IEEE, October 2017:pp. 456-463.

Czap L. & Pintér J.M. Intensity feature for speech stress detection. In Carpathian Control Conference (ICCC), 2015 16th International. IEEE, May 2015: pp. 91-94.

Kurniawan H., Maslov A.V. & Pechenizkiy M. Stress detection from speech and galvanic skin response signals. InComputer-Based Medical Systems (CBMS), 2013 IEEE 26th International Symposium. IEEE,June 2013: pp. 209-214.

Zhou G., Hansen J.H. & Kaiser J.F. Nonlinear feature based classification of speech under stress. IEEE Transactions on speech and audio processing. March 2001;9(3): pp. 201-16.

Wu W. & Lee J. Development of full-featured ECG system for visual stress induced heart rate variability (HRV) assessment. InSignal Processing and Information Technology (ISSPIT), 2010 IEEE International Symposium. IEEE, December2010:pp. 144-149.

Vanitha L. & Suresh G.R. Hybrid SVM classification technique to detect mental stress in human beings using ECG signals. In Advanced Computing and Communication Systems(ICACCS),2013 International Conference. IEEE, December 2013:pp. 1-6.

Afonso V.X., Tompkins W.J., Nguyen T.Q., Michler K. &Luo S. Comparing stress ECG enhancement algorithms. IEEE Engineering in medicine and biology magazine. May 1996; 15(3): pp. 37-44.

Kalas M.S. &Momin B.F. Stress detection and reduction using EEG signals. InElectrical, Electronics, and Optimization Techniques (ICEEOT). International Conference. IEEE, March 2016: pp. 471-475.

Liao C.Y., Chen R.C. & Tai S.K. Emotion stress detection using EEG signal and deep learning technologies. In2018 IEEE International Conference on Applied System Invention (ICASI). IEEE, April 2018: pp. 90-93.

Prakash N.R. & Kaur J. A Study on Physiological Parameters Used To Monitor Stress in Experimentally Induced Stimuli.(IJCSIT) International Journal of Computer Science and Information Technologies. 2015;6(6): pp. 5244-6.

Chauhan M., Vora S.V. & Dabhi D. Effective stress detection using physiological parameters. In Innovations in Information, Embedded and Communication Systems (ICIIECS), 2017 International Conference. IEEE, March 2017: pp. 1-6.


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