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Brief Review of Human Stress Detection Using the Processing of Physiological Signals
Current Issue
Volume 3, 2018
Issue 1 (January)
Pages: 13-15   |   Vol. 3, No. 1, January 2018   |   Follow on         
Paper in PDF Downloads: 46   Since Jan. 25, 2018 Views: 949   Since Jan. 25, 2018
Luis Junqueira, Strategic Group of Technology, Research and Innovation (GETEPI), Sao Paulo, Brazil.
Marta Cardoso Pina, Federal Institute of Education, Science and Technology (IFSP), Sao Paulo, Brazil.
The identification of an alteration in biological signs related to presence of stress in the human organism can contribute to the awareness of individuals about their current status of stress, allowing them to take some preventive action against the worsening of their health conditions. This paper presents a brief review of the physiological variables generally used for stress detection and the most common methods and techniques used for analysis of the collected data by biofeedback devices.
Stress Detection, Physiological Signals, Biofeedback Devices
O. JAFAROVA, M. SHTARK (1998). Computer Biofeedback: Trends of Development. In: The 4th IEEE APEIE International Conference Proceedings, pp. 162-164.
J. LEMAIRE et al. (2011). The Effect of a Biofeedback-based Stress Management Tool on Physician Stress: A Randomized Controlled Clinical Trial. Open Medicine, 5(4), pp. 154-163.
C. DUNSTER (2012). Treatment of Anxiety and Stress with Biofeedback. Global Advances in Health and Medicine, 1(4), pp. 76-83.
P. RATANASIRIPONG et al. (2012). Biofeedback Intervention for Stress and Anxiety among Nursing Students: A Randomized Controlled Trial. International Scholarly Research Network ISRN Nursing, pp. 1-5.
J. ZHAI, A. BARRETO (2006). Stress Recognition Using Non-invasive Technology. In: Proceedings of the 19th International Florida Artificial Intelligence Research Society Conference, pp. 395-400.
C. YUCHA, C. GILBERT (2004). Evidence-Based Practice in Biofeedback and Neurofeedback. AAPB, Colorado Springs.
S. REISMAN (1997). Measurement of Physiological Stress. In: Proceedings of the IEEE 23rd Northeast Bioengineering Conference, pp. 21-23.
M. ROVERE et al. (2003). Heart Failure Patients Short-Term Heart Rate Variability Strongly Predicts Sudden Cardiac Death in Chronic. Circulation, Journal of The American Heart Association, 107, pp. 565-570.
J. THAYER et al. (2010). The Relationship of Autonomic Imbalance, Heart Rate Variability and Cardiovascular Disease Risk Factors. International Journal of Cardiology, 141, pp. 122-131.
N. SHARMA, T. GEDEON (2012). Objective Measures, Sensors and Computational Techniques for Stress Recognition and Classification. Computer Methods and Programs in Biomedicine, 108, pp. 1287-1301.
Y. SHI et al. (2010). Personalized Stress Detection from Physiological Measurements. In: Proceedings of the 2nd International Symposium on Quality of Life Technology, Las Vegas, pp. 1-5.
K. PLARRE et al. (2011). Continuous Inference of Psychological Stress from Sensory Measurements Collected in the Natural Environment. In: The 10th Int. Conference on Information Processing in Sensor Networks, pp. 97-108.
N. CARBONARO et al. (2011). Wearable Biomonitoring System for Stress Management: A Preliminary Study on Robust ECG Signal Processing. In: IEEE Int. Symposium on a World of Wireless, Mobile and Multimedia Networks, pp. 1-6.
N. SULAIMAN et al. (2011). EEG-based Stress Features Using Spectral Centroids Technique and k-Nearest Neighbor Classifier. In: 13th International Conference on Computer Modelling and Simulation, pp. 69-74.
M. KUMAR et al. (2012). Stress Monitoring Based on Stochastic Fuzzy Analysis of Heartbeat Intervals. IEEE Transactions on Fuzzy Systems, 20(4), pp. 746-759.
P. KARTHIKEYAN et al. (2013). Detection of Human Stress using Short-Term ECG and HRV Signals. Journal of Mechanics in Medicine and Biology, 13(3), pp. 1-29.
D. GIAKOUMIS et al. (2013). Subject-dependent Biosignal Features for Increased Accuracy in Psychological Stress Detection. International Journal of. Human-Computer Studies, 71, pp. 425-439.
GLOBO (2014). Levantamento mostra que estresse atinge mais as mulheres. Available: http://g1.globo.com/jornal-hoje/ videos/t/edicoes/v/levantamento-mostra-que-estresse-atinge-mais-as-mulheres/3245177.
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