Monitoring stress levels and associated clinical health risks utilizing IoT and AI technologies to promote mental health awareness in educational institutions

Analene Montesines Nagayo; Mahmood Zayid Al Ajmi; Naga Rama Krishna Guduri; Fatma Al Buradai

doi:10.31893/multiscience.2024ss0327

Keywords:

internet of things

mental health

stress level

artificial intelligence

mobile application

Abstract

This paper presents the development and implementation of a stress monitoring system and its associated health risks using IoT, GSM, and AI technologies. The designed system was implemented with IoT-enabled Particle Photon microcontrollers that read and processed data from biosensors and input switches to evaluate physiological factors including body thermal heat rate, systemic arterial pressure, heartbeat rate, electrical skin resistance, oxygen saturation level, pain or aches and breathing rate, as well as emotional and behavioral stress symptoms such as restlessness, being irritable or over-reactive, being upset or agitated, nervousness, and impatience. Real-time and distant access to the measured parameters were made feasible through IoT cloud platforms and customized smartphone apps. The system displayed the user's stress score and level (normal, mild, moderate, severe, and extremely severe), health risk level (zero, low, medium, or high), and stress and health management recommendations. When the resulting stress level was extremely high, the system generated a referral message and sent it by SMS to the guidance counselor for necessary mental health care support. As per the research findings, the IoT-based stress level detection prototype achieved an accuracy of 87.5% when compared to the data collected from the AI interactive stress evaluation mobile phone application. The developed application was based on the DASS-21 test and was administered to participants who volunteered from an institution in Oman. Furthermore, when compared to the NEWS2 early warning score system, the system's health risk level results had an accuracy of 93.75%. With the help of this research project, guidance counselors, psychologists, and medical professionals will be able to assess and address some of the mental and physical health concerns that university students and faculty members have, as well as raise mental health awareness among individuals.
References
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2. Baker, L. M., & Taylor, W. M. (1954). The relationship under stress between changes in skin temperature, electrical skin resistance, and pulse rate. Journal of Experimental Psychology, 48(5), 361.
3. Chandra, Y. (2021). Online education during COVID-19: Perception of academic stress and emotional intelligence coping strategies among college students. Asian Education and Development Studies, 10(2), 229-238.
4. Elsalem, L., Al-Azzam, N., Jum'ah, A. A., Obeidat, N., Sindiani, A. M., & Kheirallah, K. A. (2020). Stress and behavioral changes with remote E-exams during the Covid-19 pandemic: A cross-sectional study among undergraduates of medical sciences. Annals of Medicine and Surgery, 60, 271-279.
5. Fernandes, A., Helawar, R., Lokesh, R., Tari, T., & Shahapurkar, A. V. (2014, December). Determination of stress using blood pressure and galvanic skin response. In 2014 International Conference on Communication and Network Technologies (pp. 165-168). IEEE.
6. González-Rivera, J. A., Pagán-Torres, O. M., & Pérez-Torres, E. M. (2020). Depression, Anxiety and Stress Scales (DASS-21): Construct validity problem in Hispanics. European Journal of Investigation in Health, Psychology and Education, 10(1), 375-389.
7. Hernando, A., Lázaro, J., Arza, A., Garzón, J. M., Gil, E., Laguna, P., ... & Bailón, R. (2015, September). Changes in respiration during emotional stress. In 2015 Computing in Cardiology Conference (CinC) (pp. 1005-1008). IEEE.
8. Jain, Y., Gandhi, H., Burte, A., & Vora, A. (2020, November). Mental and physical health management system using ML, computer vision and IoT sensor network. In 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 786-791). IEEE.
9. Joshi, A., Kiran, R., Singla, H. K., & Sah, A. N. (2016). Stress management through regulation of blood pressure among college students. Work, 54(3), 745-752.
10. Klapproth, F., Federkeil, L., Heinschke, F., & Jungmann, T. (2020). Teachers' experiences of stress and their coping strategies during COVID-19 induced distance teaching. Journal of Pedagogical Research, 4(4), 444-452.
11. Lovibond, P. F., & Lovibond, S. H. (1995). The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behaviour Research and Therapy, 33(3), 335-343.
12. Moawad, R. A. (2020). Online learning during the COVID-19 pandemic and academic stress in university students. Revista Românească pentru Educaţie Multidimensională, 12(1 Sup2), 100-107.
13. Myrstad, M., Ihle-Hansen, H., Tveita, A. A., Andersen, E. L., Nygård, S., Tveit, A., & Berge, T. (2020). National Early Warning Score 2 (NEWS2) on admission predicts severe disease and in-hospital mortality from Covid-19–a prospective cohort study. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 28, 1-8.
14. Nagayo, A. M., Al Ajmi, M. Z. K., Guduri, N. R. K., & AlBuradai, F. S. H. (2023). IoT-based telemedicine health monitoring system with a fuzzy inference-based medical decision support module for clinical risk evaluation. In Proceedings of Third International Conference on Advances in Computer Engineering and Communication Systems: ICACECS 2022 (pp. 313-336). Singapore: Springer Nature Singapore.
15. Nagayo, A. M., Al Ajmi, M. Z. K., Guduri, N. R. K., & AlBuradai, F. S. H. (2023). Stress and emotion detection system using IoT and machine learning with a fuzzy inference-based mental health risk assessment module. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9), 3166-3169. Retrieved from https://ijritcc.org/index.php/ijritcc/article/view/9466
16. Parab, A. N., Savla, D. V., Gala, J. P., & Kekre, K. Y. (2020, November). Stress and emotion analysis using IoT and deep learning. In 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 708-713). IEEE.
17. Robinson, L. E., Valido, A., Drescher, A., Woolweaver, A. B., Espelage, D. L., LoMurray, S., ... & Dailey, M. M. (2023). Teachers, stress, and the COVID-19 pandemic: A qualitative analysis. School Mental Health, 15(1), 78-89.
18. Tivatansakul, S., & Ohkura, M. (2015, August). Improvement of emotional healthcare system with stress detection from ECG signal. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 6792-6795). IEEE.
19. Uday, S., Jyotsna, C., & Amudha, J. (2018, April). 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.
20. Vinkers, C. H., Penning, R., Hellhammer, J., Verster, J. C., Klaessens, J. H., Olivier, B., & Kalkman, C. J. (2013). The effect of stress on core and peripheral body temperature in humans. Stress, 16(5), 520-530.
21. Widanti, N., Sumanto, B., Rosa, P., & Miftahudin, M. F. (2015, May). Stress level detection using heart rate, blood pressure, and GSR and stress therapy by utilizing infrared. In 2015 International Conference on Industrial Instrumentation and Control (ICIC) (pp. 275-279). IEEE.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Nagayo, A. M., Al Ajmi, M. Z., Guduri, N. R. K., & Al Buradai, F. (2024). Monitoring stress levels and associated clinical health risks utilizing IoT and AI technologies to promote mental health awareness in educational institutions. Multidisciplinary Science Journal, 6, 2024ss0327. https://doi.org/10.31893/multiscience.2024ss0327

[1] Aeschliman, S. D., Blue, M. S., Williams, K. B., Cobb, C. M., & MacNeill, S. R. (2003). A preliminary study on oxygen saturation levels of patients during periodontal surgery with and without oral conscious sedation using diazepam. Journal of Periodontology, 74(7), 1056-1059.

[2] Baker, L. M., & Taylor, W. M. (1954). The relationship under stress between changes in skin temperature, electrical skin resistance, and pulse rate. Journal of Experimental Psychology, 48(5), 361.

[3] Chandra, Y. (2021). Online education during COVID-19: Perception of academic stress and emotional intelligence coping strategies among college students. Asian Education and Development Studies, 10(2), 229-238.

[4] Elsalem, L., Al-Azzam, N., Jum'ah, A. A., Obeidat, N., Sindiani, A. M., & Kheirallah, K. A. (2020). Stress and behavioral changes with remote E-exams during the Covid-19 pandemic: A cross-sectional study among undergraduates of medical sciences. Annals of Medicine and Surgery, 60, 271-279.

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

[6] González-Rivera, J. A., Pagán-Torres, O. M., & Pérez-Torres, E. M. (2020). Depression, Anxiety and Stress Scales (DASS-21): Construct validity problem in Hispanics. European Journal of Investigation in Health, Psychology and Education, 10(1), 375-389.

[7] Hernando, A., Lázaro, J., Arza, A., Garzón, J. M., Gil, E., Laguna, P., ... & Bailón, R. (2015, September). Changes in respiration during emotional stress. In 2015 Computing in Cardiology Conference (CinC) (pp. 1005-1008). IEEE.

[8] Jain, Y., Gandhi, H., Burte, A., & Vora, A. (2020, November). Mental and physical health management system using ML, computer vision and IoT sensor network. In 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 786-791). IEEE.

[9] Joshi, A., Kiran, R., Singla, H. K., & Sah, A. N. (2016). Stress management through regulation of blood pressure among college students. Work, 54(3), 745-752.

[10] Klapproth, F., Federkeil, L., Heinschke, F., & Jungmann, T. (2020). Teachers' experiences of stress and their coping strategies during COVID-19 induced distance teaching. Journal of Pedagogical Research, 4(4), 444-452.

[11] Lovibond, P. F., & Lovibond, S. H. (1995). The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behaviour Research and Therapy, 33(3), 335-343.

[12] Moawad, R. A. (2020). Online learning during the COVID-19 pandemic and academic stress in university students. Revista Românească pentru Educaţie Multidimensională, 12(1 Sup2), 100-107.

[13] Myrstad, M., Ihle-Hansen, H., Tveita, A. A., Andersen, E. L., Nygård, S., Tveit, A., & Berge, T. (2020). National Early Warning Score 2 (NEWS2) on admission predicts severe disease and in-hospital mortality from Covid-19–a prospective cohort study. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 28, 1-8.

[14] Nagayo, A. M., Al Ajmi, M. Z. K., Guduri, N. R. K., & AlBuradai, F. S. H. (2023). IoT-based telemedicine health monitoring system with a fuzzy inference-based medical decision support module for clinical risk evaluation. In Proceedings of Third International Conference on Advances in Computer Engineering and Communication Systems: ICACECS 2022 (pp. 313-336). Singapore: Springer Nature Singapore.

[15] Nagayo, A. M., Al Ajmi, M. Z. K., Guduri, N. R. K., & AlBuradai, F. S. H. (2023). Stress and emotion detection system using IoT and machine learning with a fuzzy inference-based mental health risk assessment module. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9), 3166-3169. Retrieved from https://ijritcc.org/index.php/ijritcc/article/view/9466

[16] Parab, A. N., Savla, D. V., Gala, J. P., & Kekre, K. Y. (2020, November). Stress and emotion analysis using IoT and deep learning. In 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 708-713). IEEE.

[17] Robinson, L. E., Valido, A., Drescher, A., Woolweaver, A. B., Espelage, D. L., LoMurray, S., ... & Dailey, M. M. (2023). Teachers, stress, and the COVID-19 pandemic: A qualitative analysis. School Mental Health, 15(1), 78-89.

[18] Tivatansakul, S., & Ohkura, M. (2015, August). Improvement of emotional healthcare system with stress detection from ECG signal. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 6792-6795). IEEE.

[19] Uday, S., Jyotsna, C., & Amudha, J. (2018, April). 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.

[20] Vinkers, C. H., Penning, R., Hellhammer, J., Verster, J. C., Klaessens, J. H., Olivier, B., & Kalkman, C. J. (2013). The effect of stress on core and peripheral body temperature in humans. Stress, 16(5), 520-530.

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

Abstract

References

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