International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Stress Monitoring and Relieving Application for IT Professionals

M. S. D. PereraUndergraduate, Department of Computer Science and Software Engineering, Faculty of Computing, Sri Lanka Institute of Information and Technology, Colombo, Sri LankaS. M. D. A. R JayathilakeUndergraduate, Department of Computer Science and Software Engineering, Faculty of Computing, Sri Lanka Institute of Information and Technology, Colombo, Sri LankaJ. D. RanasingheUndergraduate, Department of Computer Science and Software Engineering, Faculty of Computing, Sri Lanka Institute of Information and Technology, Colombo, Sri LankaS. V. BartholomeuszUndergraduate, Department of Computer Science and Software Engineering, Faculty of Computing, Sri Lanka Institute of Information and Technology, Colombo, Sri LankaH. M. Samadhi ChathurangaLecturer, Department of Information Technology, Faculty of Computing, Sri Lanka Institute of Information and Technology, Colombo, Sri LankaSamitha VidhanaarachchiLecturer, Department of Computer Science and Software Engineering, Faculty of Computing, Sri Lanka Institute of Information and Technology, Colombo, Sri LankaThilanga JayarathneCEO at Xinotech Technology Services Inc., Colombo, Sri LankaArosha DasanayakaCounselling Psychologist at Mind Heals, Kurunegala, Sri Lanka

Vol 7 No 10 (2023): Volume 7, Issue 10, October 2023 | Pages: 609-626

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 06-11-2023

doi Logo doi.org/10.47001/IRJIET/2023.710081

pdf iconFull Text PDF
Abstract

The proposed solution presented in this report aims to address the critical issue of stress detection and management among IT professionals. Our innovative approach leverages machine learning and consists of four key components, three of which actively monitor and analyze an individual's stress levels through their keystroke dynamics, heart rate variability (HRV) via an external mouse (utilizing cost-effective IOT devices), and facial expressions captured by a webcam. The fourth component focuses on providing tailored recommendations and suggestions to help users reduce their detected stress levels. Stress is a prevalent concern among IT professionals, with potential long-term repercussions on both physical and mental health. Recognizing the urgency of addressing this problem, our system facilitates early stress detection and offers practical strategies to mitigate and maintain stress at manageable levels. The ultimate goal is to enhance the overall work experience, minimize health complications, and boost productivity among IT professionals who utilize our user-friendly approach, which integrates seamlessly with their everyday tools and equipment. This holistic solution holds the promise of a healthier, happier, and more productive workforce in the IT industry. Furthermore, our system is designed to be scalable and adaptable to various IT environments, allowing organizations to tailor it to their specific needs and preferences. It can be seamlessly integrated into existing IT infrastructure, making it a cost-effective and efficient solution for companies seeking to prioritize the well-being of their IT professionals.

Keywords

Aggregation, Classification model, IT Professionals, Keystroke Dynamics, Machine learning, Predictions, Program, Script, Stress Detection


Citation of this Article

M. S. D. Perera, S. M. D. A. R Jayathilake, J. D. Ranasinghe, S. V. Bartholomeusz, H. M. Samadhi Chathuranga Samitha Vidhanaarachchi, Thilanga Jayarathne, Arosha Dasanayaka, “Stress Monitoring and Relieving Application for IT Professionals” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 7, Issue 10, pp 609-626, October 2023. Article DOI https://doi.org/10.47001/IRJIET/2023.710081
Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. S. Panicker and P. Gayathri, " A survey of machine learning techniques in physiology based mental stress detection systems.," Biocybernetics and Biomedical Engineering, pp. 444-469, 2019.
  2. A. Rezvani and P. Khosravi, "Emotional intelligence: The key to mitigating stress and fostering trust among software developers working on information system projects," International Journal of Information Management, pp. 139-150, 2019.
  3. S. Gedam and S. Paul, "A review on mental stress detection using wearable sensors and machine learning techniques," IEEE Access, no. 9, pp. 84045-84066, 2021.
  4. D. Arasu, A. AzlanMohamed, N. Ruhaiyem, N. Annamalai, S. Lutfi and M. Qudah, "Human Stress Recognition from Facial Thermal-Based Signature: A Literature Survey," CMES, 2022.
  5. M. Alva, M. Nachamai and J. Paulose, "A comprehensive survey on features and methods for speech emotion detection," ICECCT, pp. 1-6, 2015.
  6. S. Greene, H. Thapliyal and A. Caban, "A survey of affective computing for stress detection: Evaluating technologies in stress detection for better health," EEE Consumer Electronics Magazine, vol. 5(4), pp. 44-56, 2016.
  7. S. Mittal, S. Mahendra, V. Sanap and P. Churi, "How can machine learning be used in stress management: A systematic literature review of applications in workplaces and education," International Journal of Information Management Data Insights, no. 100110, 2022.
  8. P. J. Jayaraj, M. Ghazali and A. Gaber, "Relax App: Designing Mobile Brain-Computer Interface App to Reduce Stress among Students," International Journal of Innovative Computing, 2021.
  9. S. Kalansooriya, A. Kaluarachchi, C. Weerawickrama, D. Nanayakkara, D. Kasthuriarachchi and D. Adeepa, "“xīnlĭ” The Social Media App to Replenish Mental Health with the Aid of an Egocentric Network," in R10-HTC, 2022.
  10. M. Afsar, T. Crump and B. Far, "Reinforcement learning based recommender systems: A survey," ACM Computing Surveys, pp. 1-38, 2022.
  11. Y. Can, N. Chalabianloo and C. Ersoy, "Continuous stress detection using wearable sensors in real life: Algorithmic programming contest case study," Sensors, 2019.
  12. N. C. D. E. C. E. Yekta Said Can, "Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study," National Library of Medicine, 2019.
  13. Y. Can, H. Iles-Smith, N. Chalabianloo, D. Ekiz, J. Fernández-Álvarez, C. Repetto and C. Ersoy, "How to relax in stressful situations: a smart stress reduction system," Healthcare, vol. 8, no. 2, p. 100, 2020.
  14. C. Weerasinghe, "Stress Detection by Keystroke,App & Mouse Changes," [Online]. Available: https://www.kaggle.com/datasets/chaminduweerasinghe/stress-detection-by-keystrokeapp-mouse-changes.
  15. S. Liang and R. Srikant, "Why deep neural networks for function approximation," arXiv preprint, 2016.
  16. E. Ie, V. Jain, J. Wang, S. Narvekar, R. Agarwal, R. Wu and C. Boutilier, "SlateQ: A tractable decomposition for reinforcement learning with recommendation sets," 2019.
  17. C. Liu, X. Xu and D. Hu, "Multiobjective Reinforcement Learning: A Comprehensive Overview," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015.
  18. "3 types of stress and what you can do to fight them," [Online]. Available: https://www.betterup.com/blog/types-of-stress. .
  19. J. Hernandez, P. P. A. R. and M. C. , "Under Pressure: Sensing Stress of Computer Users," Association of Computing Machinery, p. 10, 2014.
  20. S.-H. LAU, "STRESS DETECTION FOR KEYSTROKE DYNAMICS," p. 232, 2018.
  21. A. A. M. S. Y. M. Lim, "Detecting Emotional Stress during Typing Task with," p. 10, 2014.
  22. Z.-L. L. W.-J. Y. J. D. H.-R. LV, "EMOTION RECOGNITION BASED ON PRESSURE SENSOR KEYBOARDS,," 2008.
  23. L. M. Vizer, "Detecting Cognitive and Physical Stress Through Typing Behavior," 2009.
  24. Paul H. Dietz, B. E. Jonathan Westhues and S. B. , "A Practical Pressure Sensitive Computer Keyboard," UIST, 2009.
  25. T. Yamauchi and K. X. , "Reading Emotion From Mouse Cursor Motions: Affective Computing Approach," Cognitive Science, 2017.
  26. H. Lv and W.-Y. W. , "Biologic verification based on pressure sensor keyboards and classifier fusion techniques," IEEE, 2006.
  27. D. Gunetti and C. P. , "Keystroke analysis of free text," ACM, 2005.
  28. L. Hai-Rong, L. Z.-L. Y. W.-J. and J. D. , "Emotion recognition based on pressure sensor keyboards," IEEE, 2008.
  29. G. Hytry, "IT’s Stressful. Ask DevOps, They’ll Know: Stress in the IT Sector [2022 Survey]," 2022. [Online]. Available: https://spacelift.io/blog/are-it-jobs-stressful.
  30. J. Fan, Z. Wang, Y. Xie and Z. Yang, "A theoretical analysis of deep Q-learning," Learning for dynamics and control, pp. 486-489.
  31. Ie, E., Hsu, C. W., Mladenov, M., Jain, V., Narvekar, S., Wang, J., ... & Boutilier, C. (2019). Recsim: A configurable simulation platform for recommender systems. arXiv preprint arXiv:1909.04847.

Copyright @ 2026 IRJIET. All Right Reserved.

Licensed underCreative Commons Attribution 4.0 International License.