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A Persuasive System for Stress Detection and Management in an Educational Environment

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Internet of Things (GIoTS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13533))

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Abstract

This paper addresses the development of a persuasive IoT system for stress detection and management in students during classroom situations. An emotion-aware persuasive architecture is developed with four modules: Context Acquisition, Context Manager, Persuasion Manager and Context-Aware Applications. By using the galvanic skin response biomarker, the real-time stress level is measured by the wearable wristband Empatica E4. The data, processed and classified on discrete stress levels from 0 to 5, is sent to the context module that identifies situations of interest where the students need positive reinforcement from the persuasive system. Based on the situation of interest and the user’s profile, the persuasive module composes personalized persuasive messages displayed in a mobile application. The persuasive system was evaluated through an exploratory study during a class session, with encouraging results in detecting stress levels and the positive effect of persuasive messages on students.

This research was founded by Concytec - World Bank: “Improvement and extension of services of the National System of Science, Technology and Innovation” 8682-PE. through its executive unit ProCiencia. Contract Nro: 014–2019-FONDECYT-BM-INC.INV.

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References

  1. Abreu, C., Campos, P.F.: Raising awareness of smartphone overuse among university students: a persuasive systems approach. MDPI Informatics 9, 15 (2022)

    Google Scholar 

  2. Akanksha, E.: Framework for propagating stress control message using heartbeat based IoT remote monitoring analytics. Int. J. Electr. Comput. Eng. (IJECE). 10, 4615 (2020)

    Google Scholar 

  3. Alhasani, M., Alkhawaji, A., Orji, R.: Mental health and time management behavior among students during covid-19 pandemic: Towards Persuasive Technology Design. medRxiv (2021)

    Google Scholar 

  4. Ananthanarayan, S., Siek, K.A.: Persuasive wearable technology design for health and wellness. In: 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops, pp. 236–240 (2012). https://doi.org/10.4108/icst.pervasivehealth.2012.248694

  5. Balakrishna, C., Rendon-Morales, E., Aviles-Espinosa, R., Dore, H., Luo, Z.: Challenges of wearable health monitors. In: A Case Study of Foetal ECG Monitor, pp. 1–6. IEEE (2019)

    Google Scholar 

  6. Brick, T.R., Mundie, J., Weaver, J., Fraleigh, R., Oravecz, Z.: Low-burden mobile monitoring, intervention, and real-time analysis using the wear-it framework: example and usability study. JMIR Formative Res. 4(6), e16072 (2020)

    Article  Google Scholar 

  7. Brunschwiler, T., et al.: Internet of the body - wearable monitoring and coaching. In: 2019 Global IoT Summit (GIoTS), pp. 1–6. IEEE (2019)

    Google Scholar 

  8. Cialdini, R.B., Cialdini, R.B.: Influence: The Psychology of Persuasion, vol. 55. Collins, New York (2007)

    Google Scholar 

  9. Ghavanini, S.A., Homayounvala, E., Rezaeian, A.: Mood-tracking application as persuasive technology for reduction of occupational stress. Int. J. Mobile Learn. Organ. 12(2), 143–161 (2018)

    Article  Google Scholar 

  10. Kumar, A., Sharma, K., Sharma, A.: Hierarchical deep neural network for mental stress state detection using IoT based biomarkers. Pattern Recogn. Lett. 145, 81–87 (2021)

    Google Scholar 

  11. Li, Q., Xue, Y., Zhao, L., Jia, J., Feng, L.: Analyzing and identifying teens’ stressful periods and stressor events from a microblog. IEEE J. Biomed. Health Inform. 21(5), 1434–1448 (2016)

    Article  Google Scholar 

  12. Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing sax: a novel symbolic representation of time series. Data Min. Knowl. Disc. 15(2), 107–144 (2007)

    Article  MathSciNet  Google Scholar 

  13. Llerena, D., Delgado, R., Ubilluz, C., Lopez, R.: A prototype proposal for detection and reduction of stress by using brain waves and IoT. In: 2020 International Conference of Digital Transformation and Innovation Technology (Incodtrin), pp. 12–16. IEEE (2020)

    Google Scholar 

  14. López, A.B.: Modelo de registro y modos de cuantificación para la técnica de listado de pensamientos. Anuario de psicología/The UB Journal of psychology 41–50 (1987)

    Google Scholar 

  15. Mamani, Y.: Deteccion de estres en tiempo real a partir de señales de voz y datos fisiologicos. Universidad Nacional de San Agustin de Arequipa, Arequipa, Peru, degree project (2021)

    Google Scholar 

  16. Masood, K., Alghamdi, M.A.: Modeling mental stress using a deep learning framework. IEEE Access 7, 68446–68454 (2019)

    Article  Google Scholar 

  17. Naeini, E.K., Azimi, I., Rahmani, A.M., Liljeberg, P., Dutt, N.: A real-time ppg quality assessment approach for healthcare Internet-of-Things. Procedia Comput. Sci. 151, 551–558 (2019)

    Article  Google Scholar 

  18. Onyema, E.M., Eucheria, N.C., Obafemi, F.A., Sen, S., Atonye, F.G., Sharma, A., Alsayed, A.O.: Impact of coronavirus pandemic on education. J. Educ. Pract. 11(13), 108–121 (2020)

    Google Scholar 

  19. Oti, O., Azimi, I., Anzanpour, A., Rahmani, A.M., Axelin, A., Liljeberg, P.: IoT-based healthcare system for real-time maternal stress monitoring. In: 2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, pp. 57–62. ACM (2018)

    Google Scholar 

  20. Perosanz, J.J.I.: La técnica del listado de pensamientos como método de investigación en comunicación publicitaria. Comunicación & cultura 3, 43–62 (1998)

    Google Scholar 

  21. Petty, R.E., Cacioppo, J.T.: The elaboration likelihood model of persuasion. In: Communication and persuasion, pp. 1–24. Springer, New York (1986). https://doi.org/10.1007/978-1-4612-4964-1_1

  22. Pollreisz, D., TaheriNejad, N.: A simple algorithm for emotion recognition, using physiological signals of a smart watch. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2353–2356. IEEE (2017)

    Google Scholar 

  23. Rodic-Trmcic, B., Labus, A., Bogdanovic, Z., Despotovic-Zrakic, M., Radenkovic, B.: Development of an IoT system for students’ stress management. Facta Univ. Ser. Electron. Energ. 31, 329–342 (2018)

    Article  Google Scholar 

  24. Romine, W.L., Schroeder, N.L., Graft, J., Yang, F., Sadeghi, R., Zabihimayvan, M., Kadariya, D., Banerjee, T.: Using machine learning to train a wearable device for measuring students’ cognitive load during problem-solving activities based on electrodermal activity, body temperature, and heart rate: development of a cognitive load tracker for both personal and classroom use. Sensors 20(17), 4833 (2020)

    Article  Google Scholar 

  25. Safa, M., Pandian, A.: Applying machine learning algorithm to sensor coupled IoT devices in prediction of cardiac stress - an integrated approach. Mater. Today: Proc. (2021)

    Google Scholar 

  26. Sarmiento-Calisaya, E., Calcina, P., Cuno, A.: An emotion-aware persuasive architecture to support challenging classroom situations. In: 2022 IEEE International Conference on Consumer Electronics (ICCE). IEEE (2022)

    Google Scholar 

  27. Schmidt, P., Reiss, A., Duerichen, R., Marberger, C., Van Laerhoven, K.: Introducing wesad, a multimodal dataset for wearable stress and affect detection. In: Proceedings of the 20th ACM International Conference on Multimodal Interaction, pp. 400–408 (2018)

    Google Scholar 

  28. Suni Lopez, F., Condori-Fernandez, N., Catala, A.: Towards real-time automatic stress detection for office workplaces. In: Lossio-Ventura, J.A., Muñante, D., Alatrista-Salas, H. (eds.) SIMBig 2018. CCIS, vol. 898, pp. 273–288. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11680-4_27

    Chapter  Google Scholar 

  29. Uday, S., Jyotsna, C., Amudha, J.: Detection of stress using wearable sensors in IoT platform. In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 492–498. IEEE (2018)

    Google Scholar 

  30. Vallès-Català, T., Pedret, A., Ribes, D., Medina, D., Traveria, M.: Effects of stress on performance during highly demanding tasks in student pilots. Int. J. Aerosp. Psychol. 31(1), 43–55 (2021)

    Article  Google Scholar 

  31. Verma, P., Sood, S.K.: A comprehensive framework for student stress monitoring in fog-cloud IoT environment: m-health perspective. Med. Biol. Eng. Comput. 57(1), 231–244 (2018). https://doi.org/10.1007/s11517-018-1877-1

    Article  Google Scholar 

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Authors and Affiliations

  1. Universidad Nacional de San Agustín de Arequipa, Arequipa, Peru

    Pablo Calcina-Ccori, Eduardo S. Rodriguez-Canales & Edgar Sarmiento-Calisaya

Authors
  1. Pablo Calcina-Ccori
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  2. Eduardo S. Rodriguez-Canales
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  3. Edgar Sarmiento-Calisaya
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Corresponding author

Correspondence to Pablo Calcina-Ccori .

Editor information

Editors and Affiliations

  1. University of Murcia, Murcia, Spain

    Aurora González-Vidal

  2. Central Michigan University, Mount Pleasant, MI, USA

    Ahmed Mohamed Abdelgawad

  3. University of Quebec at Montreal, Montreal, QC, Canada

    Essaid Sabir

  4. IoT Forum, Mandat International, Geneva, Switzerland

    Sébastien Ziegler

  5. University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Latif Ladid

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Calcina-Ccori, P., Rodriguez-Canales, E.S., Sarmiento-Calisaya, E. (2022). A Persuasive System for Stress Detection and Management in an Educational Environment. In: González-Vidal, A., Mohamed Abdelgawad, A., Sabir, E., Ziegler, S., Ladid, L. (eds) Internet of Things. GIoTS 2022. Lecture Notes in Computer Science, vol 13533. Springer, Cham. https://doi.org/10.1007/978-3-031-20936-9_19

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  • DOI: https://doi.org/10.1007/978-3-031-20936-9_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20935-2

  • Online ISBN: 978-3-031-20936-9

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