Abstract
Stress is attributed as the natural response of body towards unwanted and challenging conditions of society and environment. The consequences of stress are in general very harmful. However, in some specific situations, stress can be highly serious to the human health as it impacts the cardiovascular system in addition to other body sub systems. Timely Detection of stress through physiological sensors may help in arresting the disease. As the stress results in change of heart rate, skin conduction, temperature, blood pressure and oxygen levels, hence, measurement and assessment of these parameters is useful in deciding the stress levels into a candidate. In this paper, a fuzzy logic based automatic stress detection system is designed to timely detect the patients passing through this silent killer disease. The physiological signals of human body are used to assess the current state of person. The parameters used for the purpose are heart rate, Galvanic Skin Response (GSR), and temperature. The fuzzy logic is an exciting branch of artificial intelligence that is used to make useful decisions under uncertain and incomplete input knowledge. A synthetic data set of 500 patients is also developed to train and test the proposed architecture. The algorithms used for this purpose are Hill Climbing (HC) and Simulated Annealing (SA) for fuzzy membership functions training. The results show that Simulated Annealing gives a testing accuracy of 89% in comparison to Hill climbing with 81% accuracy. Future work is directed towards using new methods like type-2 fuzzy logic and deep learning etc.
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References
Cohen, B.E., Edmondson, D., Kronish, I.M.: State of the art review: depression, stress, anxiety, and cardiovascular disease. Am. J. Hypertens. 28(11), 1295–1302 (2015). https://doi.org/10.1093/ajh/hpv047
Liu, Y., Cao, L., Li, X., Jia, Y., Xia, H.: Awareness of mental health problems in patients with coronavirus disease 19 (COVID-19): a lesson from an adult man attempting suicide. Asian J. Psychiatr. 51, 102106 (2020). https://doi.org/10.1016/j.ajp.2020.102106
Coan, J.A., Allen, J.J.B.: Frontal EEG asymmetry as a moderator and mediator of emotion. Biol. Psychol. 67(1), 7–50 (2004). https://doi.org/10.1016/j.biopsycho.2004.03.002
Poon, C.-S., Siniaia, M.S.: Plasticity of cardiorespiratory neural processing: classification and computational functions. Respir. Physiol. 122(2–3), 83–109 (2000)
Sadati, S.M.S., Qureshi, F.U., Baker, D.: Energetic and economic performance analyses of photovoltaic, parabolic trough collector and wind energy systems for Multan, Pakistan. Renew. Sustain. Energy Rev. 47, 844–855 (2015). https://doi.org/10.1016/j.rser.2015.03.084
Picard, R.W.: Affective Computing. MIT Press, Cambridge (2000)
Can, Y.S., Arnrich, B., Ersoy, C.: Stress detection in daily life scenarios using smart phones and wearable sensors: a survey. J. Biomed. Inform. 92, 103139 (2019). https://doi.org/10.1016/j.jbi.2019.103139
Ghulam, M., Yannick, F., Abdelkader, G., Michel, F., Hocine, N.: Level of detail based AI adaptation for agents in video games. In: 5th International Conference on Agents and Artificial Intelligence (ICAART) (2013).https://doi.org/10.1007/978-3-642-29966-7
Mahdi, G., Gouaich, A.: Interval based integrated real-time coordination for multi-agent systems. In: Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, pp. 664–669, (2011) https://doi.org/10.5220/0003188406640669
Baloch, S.A., Shaikh, A.Z.: Fuzzy logic enabled spectrum access scheme for cognitive radio applications. In: International Conference on Innovations in Computer Science (2018)
Baloch, S.A., Shaikh, A.Z., Ahmed, A.: Survey on fuzzy logic enabled cognitive radio. In: International Conference for Emerging Technologies in Computing, pp. 190–196 (2018)
Ahmed, M., Naqvi, S., Ali, I., Uddin, S., Shaikh, A.Z.: A fuzzy inferencing system for highlighting the complexities involved in electronic surveys with emotions. Int. J. Comput. Sci. Netw. Secur. 19(10), 6–14 (2019)
de Santos Sierra, A., Avila, C.S., Casanova, J.G., del Pozo, G.: A stress-detection system based on physiological signals and fuzzy logic. IEEE Trans. Ind. Electron. 58(10), 4857–4865 (2011). https://doi.org/10.1109/TIE.2010.2103538
Zalabarria, U., Irigoyen, E., Martinez, R., Larrea, M., Salazar-Ramirez, A.: A low-cost, portable solution for stress and relaxation estimation based on a real-time fuzzy algorithm. IEEE Access 8, 74118–74128 (2020). https://doi.org/10.1109/ACCESS.2020.2988348
Chen, J., Abbod, M., Shieh, J.-S.: Pain and stress detection using wearable sensors and devices—a review. Sensors, 21(4) (2021). https://doi.org/10.3390/s21041030
Albertetti, F., Simalastar, A., Rizzotti-Kaddour, A.: Stress detection with deep learning approaches using physiological signals. In: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, pp. 95–111 (2020)
Durán-Acevedo, C.M., Carrillo-Gómez, J.K., Albarracín-Rojas, C.A.: Electronic devices for stress detection in academic contexts during confinement because of the COVID-19 pandemic. Electronics, 10(3) (2021). https://doi.org/10.3390/electronics10030301
Salazar-Ramirez, A., Irigoyen, E., Martinez, R., Zalabarria, U.: An enhanced fuzzy algorithm based on advanced signal processing for identification of stress. Neurocomputing 271, 48–57 (2018). https://doi.org/10.1016/j.neucom.2016.08.153
Lakshmi, B., Boban, E., Sulphikar, N., Aparna, T.M., Dhanya, S., Anjaly, S.V.: Mental stress calculation using fuzzy logic algorithm. Int. J. Appl. Eng. Res. 15(1), 35–43 (2020)
Ceniza, A., Cantara, A.: Stress sensor prototype: determining the stress level in using a computer through validated self-made Heart Rate (HR) and Galvanic Skin Response (GSR) sensors and fuzzy logic algorithm. Int. J. Eng. Res. Technol., 5(3) (2016)
Dobbins, C., Fairclough, S.: Detecting and visualizing context and stress via a fuzzy rule-based system during commuter driving. IEEE Int. Conf. Pervasive Comput. Commun. Workshops (PerCom Workshops) 2019, 499–504 (2019). https://doi.org/10.1109/PERCOMW.2019.8730600
Karim, S., et al.: A fuzzy inferencing system for stress management of university students in COVID-19 for Pakistan. In: 2nd International Conference on Computational Sciences and Technologies, 17–19 December 2020 (INCCST 2020), MUET Jamshoro, pp. 5–10 (2020)
de Santos Sierra, A., Ávila, C.S., del Pozo, G., Casanova, J.G.: Stress detection by means of stress physiological template. In: 2011 Third World Congress on Nature and Biologically Inspired Computing, pp. 131–136 (2011). https://doi.org/10.1109/NaBIC.2011.6089448
Shi, Y., Ruiz, N., Taib, R., Choi, E., Chen, F.: Galvanic Skin Response (GSR) as an index of cognitive load. In: CHI 2007 Extended Abstracts on Human Factors in Computing Systems, New York, NY, USA: Association for Computing Machinery, pp. 2651–2656 (2007)
Zhai, J., Barreto, A.: Stress detection in computer users based on digital signal processing of noninvasive physiological variables. Int. Conf. IEEE Eng. Med. Biol. Soc. 2006, 1355–1358 (2006). https://doi.org/10.1109/IEMBS.2006.259421
Naqvi, S.: Modelling FTIR Spectral Data with Type-I and Type-II Fuzzy Sets for Breast Cancer Grading, Ph. D. Thesis, The University of Nottingham for the Degree of Doctor of Philosophy. The University of Nottingham (2014)
Suman, B., Kumar, P.: A survey of simulated annealing as a tool for single and multiobjective optimization. J. Oper. Res. Soc. 57, 1143–1160 (2006)
Acknowledgment
Dr. Shabbar Naqvi would like to thank the administration of Balochistan University of Engineering and Technology Khuzdar, Balochistan, Pakistan for providing resources to complete the research.
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Naqvi, S., Shaikh, A.Z., Altaf, T., Singh, S. (2021). Fuzzy Logic Enabled Stress Detection Using Physiological Signals. In: Miraz, M.H., Southall, G., Ali, M., Ware, A., Soomro, S. (eds) Emerging Technologies in Computing. iCETiC 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 395. Springer, Cham. https://doi.org/10.1007/978-3-030-90016-8_11
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