Prevention of Burnout Syndrome Through Neuro-linguistic Programming Supported by the Web of Things: A Systematic Mapping




Burnout, emotions, Internet of Things, neurolinguistic programming, web of things, work stress


This article presents an exploration of the literature through a systematic mapping around the application of neurolinguistic programming techniques (NLP) supported on the Web of Things (WoT) to prevent Burnout syndrome. This syndrome is a type of work stress that causes physical, mental and emotional exhaustion, generating an inability to work, since it is a gradual process in which the worker loses interest in their tasks, lacks a sense of responsibility and can generate deep depression. In the studies found, the use of WoT for the detection of emotions and work stress stands out, for this, the use of sensors capable of measuring Galvanic response of the skin GSR, HR heart rate, PPG photoplethysmography, ECG electrocardiogram, cameras, Low-cost microphones and microprocessors, as well as the use of Artificial Intelligence to process this data, among the most used techniques and algorithms are SVM Support Vector Machines, K-nearest neighbor and Naive Bayes classifier. In jobs in which emotions or work stress are detected, very few attempt to alter the mental or environmental environment of the user to bring them to a positive emotion or reduce stress. The possibility of using NLP techniques in the prevention of Burnout syndrome was evidenced. However, no work was found that related WoT as a support to NLP techniques to prevent Burnout syndrome, which is considered as a research opportunity in these fields.


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Author Biographies

Líder Julián Rojas-Bolaños, M.Sc. (c), Universidad del Cauca

Roles: Conceptualization, Formal analisys, Investigation, Methodology, Validation, Writing - original draft, Writing - review & editing.

Miguel Ángel Niño-Zambrano, Ph. D., Universidad del Cauca

Roles: Supervision, Conceptualization, Formal analisys, Writing - original draft, Writing - review & editing.

Andrea Pabón-Guerrero, M.Sc. (c), Universidad del Cauca

Roles: Resource, Methodology, Writing - original draft, Writing - review & editing.


[1] F. Álvarez, Salud ocupacional. Bogotá, COLOMBIA: Ecoe Ediciones, 2009.

[2] C. Maslach, W. B. Schaufeli, and M. P. Leiter, "Job Burnout," Annual Review of Psychology, vol. 52 (1), pp. 397-422, 2001.

[3] H. Ziedenberg, I. Raz, and A. Bashiri, "The Health Caregiver's Perspective: The Importance of Emotional Support for Women with Recurrent RPL," Recurrent Pregnancy Loss: Evidence-Based Evaluation, Diagnosis and Treatment, pp. 167-177, 2016.

[4] D. H. Rao and D. G. Kulkarni, "NLP for stress mitigation in employees," in International Conference on Education and Management Technology, Cairo, Egypt, 2010, pp. 600-603.

[5] J. E. Thompson, L. Courtney, J. E. Thompson, and D. Dickson, "The effect of neurolinguistic programming on organisational and individual performance: A case study," Journal of European Industrial Training, vol. 26 (6), pp. 292-298, 2009.

[6] M. HemmatiMaslakpak, M. Farhadi, and J. Fereidoni, "The effect of neuro-linguistic programming on occupational stress in critical care nurses," Iranian Journal of Nursing and Midwifery Research, vol. 21(1), pp. 38-44, 2016.

[7] D. D. Szczygiel, and M. Mikolajczak, "Emotional Intelligence Buffers the Effects of Negative Emotions on Job Burnout in Nursing," Frontiers in Psychology, vol. 9 pp. 2649-2649, Dec. 2018.

[8] P. Pinheiro, J. Ramos, V. L. Donizete, P. Picanço, and G. H. De Oliveira, "Workplace Emotion Monitoring—An Emotion-Oriented System Hidden Behind a Receptionist Robot," in Mechatronics and Robotics Engineering for Advanced and Intelligent Manufacturing. Cham: Springer, 2017, pp. 407-420.

[9] P. Guillemin, and P. Friess, "Internet of things strategic research roadmap," in Internet of Things - Global Technological and Societal Trends. Aalborg: River Publisher, 2009, pp. 9-51.

[10] S. S. Mathew, Y. Atif, Q. Z. Sheng, and Z. Maamar, "Web of Things: Description, Discovery and Integration," in International Conference on Internet of Things and 4th International Conference on Cyber, Physical and Social Computing, Dalian, China, 2011, pp. 9-15.

[11] C. Vuppalapati, S. Kedari, A. Ilapakurti, S. Kedari, and J. Shankar, "Emotional health: A data driven approach to understand our emotions and improve our health," in IEEE International Conference on Computational Science and Engineering and IEEE International Conference on Embedded and Ubiquitous Computing, New York, USA, 2019, pp. 339-347.

[12] A. V. Kirenskaya, V. Y. Novototsky-Vlasov, A. N. Chistyakov, and V. M. Zvonikov, "The relationship between hypnotizability, internal imagery, and efficiency of neurolinguistic programming," International Journal of Clinical and Experimental Hypnosis, vol. 59(2), pp. 225-241, Jun. 2011.

[13] K. Petersen, R. Feldt, S. Mujtaba, and M. Mattsson, "Systematic Mapping Studies in Software Engineering," in Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering, Italy, Rome, 2008, pp. 1-10.

[14] C. Hoyos Botero, Un modelo para investigacion documental: guia teorico-practica sobre construccion de estados del arte con importantes reflexiones sobre la investigacion. Medellín: Señal Editora, 2000.

[15] C. E. Serrano, Modelo Integral para el Profesional en Ingeniería, Popayán: Universidad del Cauca, 2003.

[16] B. Kitchenham, and S. Charters, "Guidelines for performing systematic literature reviews in software engineering," Technical report, Ver. 2.3 EBSE Technical Report. EBSE, Jul. 2007.

[17] S. Uday, C. Jyotsna, and J. Amudha, "Detection of Stress using Wearable Sensors in IoT Platform," in Second International Conference on Inventive Communication and Computational Technologies, Coimbatore, India, 2018, pp. 492-498.

[18] R. Setiawan, F. Budiman, and W. I. Basori, "Stress Diagnostic System and Digital Medical Record Based on Internet of Things," in International Seminar on Intelligent Technology and Its Applications, Surabaya, Indonesia, 2019, pp. 348-353.

[19] T. Brunschwiler, J. Weiss, S. Paredes, A. Sridhar, U. Pluntke, S. M. Chau, S. Gerke, J. Barroso, E. Loertscher, Y. Temiz, P. Ruch, B. Michel, S. Zafar, and T. v. Kessel, "Internet of the Body - Wearable Monitoring and Coaching," in Global IoT Summit, Aarhus, Denmark, 2019, pp. 1-6.

[20] W. Lawanot, M. Inoue, T. Yokemura, P. Mongkolnam, and C. Nukoolkit, "Daily Stress and Mood Recognition System Using Deep Learning and Fuzzy Clustering for Promoting Better Well-Being," in IEEE International Conference on Consumer Electronics, Las Vegas, NV, USA, pp. 1-6.

[21] U. Pluntke, S. Gerke, A. Sridhar, J. Weiss, and B. Michel, "Evaluation and Classification of Physical and Psychological Stress in Firefighters using Heart Rate Variability," in 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Berlin, Germany, 2019, pp. 2207-2212.

[22] J. C. Holland, A. Sargolzaei, M. Horton, N. Khoshavi, and S. Sargolzaei, "Conceptual Framework for Stress and Comfort Enhancement using Fuzzy Controller," in SoutheastCon, Huntsville, USA, 2019, pp. 1-7.

[23] G. Tartare, X. Zeng, and L. Koehl, "Development of a wearable system for monitoring the firefighter's physiological state," in IEEE Industrial Cyber-Physical Systems, St. Petersburg, Russia, 2018, pp. 561-566.

[24] S. Muñoz, O. Araque, J. Fernando Sánchez-Rada, and C. A. Iglesias, "An emotion aware task automation architecture based on semantic technologies for smart offices," Sensors (Switzerland), vol. 18(5), May. 2018.

[25] J. Kim, K. B. Lee, S. Lee, H. Yang, and S. G. Hong, "A novel stress measurement system with handhold electrodes in massage chairs," in International Conference on Information and Communication Technology Convergence, Jeju, South Korea, 2016, pp. 859-863.

[26] V. Pachalag, and A. Malhotra, "Internet of Emotions: Emotion Management Using Affective Computing," in International Conference on Information and Communication Technology for Intelligent Systems, Cham, Chine, 2017, pp. 567-578.

[27] A. Menychtas, M. Galliakis, P. Tsanakas, and I. Maglogiannis, "Real-time integration of emotion analysis into homecare platforms," in 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Berlin, Germany, 2019, pp. 3468-3471.

[28] S. Nita, S. Bitam, and A. Mellouk, "A Body Area Network for Ubiquitous Driver Stress Monitoring based on ECG Signal," in International Conference on Wireless and Mobile Computing, Networking and Communications, Barcelona, Spain, 2019, pp. 1-6.

[29] U. Zalabarria, E. Irigoyen, R. Martinez, M. Larrea, and A. Salazar-Ramirez, "A Low-Cost, Portable Solution for Stress and Relaxation Estimation Based on a Real-Time Fuzzy Algorithm," IEEE Access, vol. 8 pp. 74118-74128, Apr. 2020.

[30] J. V. Raj, and S. T. V, "An IoT based Real-Time Stress Detection System for Fire-Fighters," in International Conference on Intelligent Computing and Control Systems, Madurai, India, 2019, pp. 354-360.

[31] H. J. Han, S. Labbaf, J. L. Borelli, N. Dutt, and A. M. Rahmani, "Objective stress monitoring based on wearable sensors in everyday settings," Journal of Medical Engineering and Technology, vol. 44(4), pp. 177-189, Apr. 2020.

[32] E. A. Sağbaş, S. Korukoglu, and S. Balli, "Stress Detection via Keyboard Typing Behaviors by Using Smartphone Sensors and Machine Learning Techniques," Journal of Medical Systems, vol. 44(4), e68, Feb. 2020.

[33] A. J. A. Majumder, T. M. McWhorter, Y. Ni, H. Nie, J. Iarve, and D. R. Ucci, "sEmoD: A Personalized Emotion Detection Using a Smart Holistic Embedded IoT System," in IEEE 43rd Annual Computer Software and Applications Conference, Milwaukee, USA, 2019, pp. 850-859.

[34] X. Liang, Y. Dai, H. Chen, and S. Lu, "Construction of emotional intelligent service system for the aged based on Internet of things," Advances in Mechanical Engineering, vol. 11(3), Mar. 2019.

[35] M. S. Hossain, and G. Muhammad, "Emotion recognition using secure edge and cloud computing," Information Sciences, vol. 504 pp. 589-601, Dec. 2019.

[36] S.-G. Choi, and S.-B. Cho, "Bayesian networks + reinforcement learning: Controlling group emotion from sensory stimuli," Neurocomputing, vol. 391, pp. 355-364, May. 2019.

[37] J. Zhang, Z. Yin, P. Chen, and S. Nichele, "Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review," Information Fusion, vol. 59 pp. 103-126, Jul. 2020.

[38] J. Nie, Y. Hu, Y. Wang, S. Xia, and X. Jiang, "SPIDERS: Low-cost wireless glasses for continuous in-situ bio-signal acquisition and emotion recognition," in IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI), New York, USA, 2020, pp. 27-39.

[39] J. Z. Lim, J. Mountstephens, and J. Teo, "Emotion recognition using eye-tracking: Taxonomy, review and current challenges," Sensors (Switzerland), vol. 20(8), e2384, Apr. 2020.

[40] B. Shirke, J. Wong, J. C. Libut, K. George, and S. J. Oh, "Brain-IoT based Emotion Recognition System," in 10th Annual Computing and Communication Workshop and Conference, Las Vegas, USA, 2020, pp. 991-995.

[41] W. T. Meshach, S. Hemajothi, and E. A. M. Anita, "Real-time facial expression recognition for affect identification using multi-dimensional SVM," Journal of Ambient Intelligence and Humanized Computing, vol. 2020, Jun. 2020.



How to Cite

Rojas-Bolaños, L. J., Niño-Zambrano, M. Ángel, & Pabón-Guerrero, A. (2020). Prevention of Burnout Syndrome Through Neuro-linguistic Programming Supported by the Web of Things: A Systematic Mapping. Revista Facultad De Ingeniería, 29(54), e11758.