An AI-Driven Micro-Randomized Intervention Framework for Reducing Digital Eye Strain and Dizziness during Prolonged Screen Use

Dr Naheeda Zaib; Lalita; Dr. Deepak Chandra Uprety; Kanika Singhal; Dyuti Banerjee; Dr Jamal Akhtar khan

doi:10.29284/p4rd5517

Authors

Dr Naheeda Zaib Assistant Professor School of Computer Science and Applications Lovely Professional University Punjab
Lalita Assistant Professor School of Computer science and Engineering Lovely professional university Punjab india
Dr. Deepak Chandra Uprety Associate Professor and Research Coordinator Computer Science and Engineering Noida Institute of Engineering and Technology Knowledge Park- 2, Greater Noida
Kanika Singhal Department of CS Noida Institute of Engineering and Technology Greater Noida,Uttar Pradesh,India
Dyuti Banerjee Assistant Professor Artificial Intelligence and Data Science Department, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur District, 522302, Andhra Pradesh, India
Dr Jamal Akhtar khan Associate Professor, Lovely professional university

DOI:

https://doi.org/10.29284/p4rd5517

Keywords:

Digital eye strain; Artificial intelligence; Micro-randomized intervention; Eye tracking; Adaptive systems; Human–computer interaction.

Abstract

Extended exposure to digital screens has been associated with an increasing incidence of digital eye strain (DES), dizziness, and visual discomfort. Existing mitigation strategies, such as scheduled breaks and blue-light filtering, are largely static and do not adequately account for individual variations in fatigue response. In this work, an AI-based adaptive system is presented that monitors blink rate patterns, gaze stability, posture-related variations, and screen exposure duration to identify early signs of visual fatigue. When fatigue is detected, a micro-randomized and gamified eye-exercise module provides short, personalized interventions designed to alleviate strain and improve visual comfort. The proposed approach was evaluated through a controlled study involving 100 participants and compared with conventional break-based methods using standardized eye strain and dizziness questionnaires, together with AI-derived fatigue indicators. The findings indicate a measurable reduction in digital eye strain and dizziness, along with improved user engagement and comfort. Variability across participants was observed, suggesting that adaptive and personalized intervention strategies are particularly important for managing prolonged screen use. Overall, the study supports the potential of AI-driven micro-randomized interventions for enhancing visual wellness in screen-intensive environments.

References

1. Blehm, C., Vishnu, S., Khattak, A., Mitra, S., & Yee, R. W. (2005). Computer vision syndrome: a review. Survey of Ophthalmology, 50(3), 253–262.

2. Sheppard, A. L., & Wolffsohn, J. S. (2018). Digital eye strain: prevalence, measurement and amelioration. BMJ Open Ophthalmology, 3(1), e000146.

3. Mylona, I., et al. (2023). Spotlight on digital eye strain. Survey and Review of Computer Vision Syndrome.

4. Mary Eldo, B., Mathews, N. E., Johnson, B., & Roy, A. S. (2024). Impact of increased screen time on ocular health of medical undergraduate students during COVID 19. International Journal of Community Medicine and Public Health, 11(11), 4393–4398.

5. Albasheer, O., Jareebi, M. A., Alnami, R. M., et al. (2025). Computer Vision Syndrome Among Saudi University Students: A Cross Sectional Analysis of Risks and Discipline Variations. Healthcare, 13(21), 2798.

6. Determinants of digital eye strain among university students in a district of India: a cross sectional study. (2022). Journal of Clinical Epidemiology.

7. Computer vision syndrome and its determinants: A systematic review and meta analysis. (2022). PubMed.

8. Das, S., Kalita, L., & Sarma, B. (2025). The Impact of Digital Devices on Visual Health: Addressing Digital Eye Strain and Blue Light Exposure. Journal of Population Therapeutics and Clinical Pharmacology.

9. Dhankar, U., Kumar, A., Pradhan, N., & Gupta, N. (2025). Lumina AI: Real Time Eye Wellness Monitoring Using Advanced Computer Vision and Machine Learning.

10. Journal for Research in Applied Science and Engineering Technology.

11. Artificial intelligence based multimodal framework for non invasive detection of digital eye strain using thermal imaging and behavioral metrics (EyeStrainNet). (2025). ScienceDirect / Elsevier.

12. Aggarwal, D., Sharma, D., & Saxena, A. B. (2023). Detection of eye strain due to usage of electronic devices. International Journal of Health Sciences.

13. Digital Eye Strain Monitoring for One Hour Smartphone Engagement Through Eye Activity Measurement System. (2025). PubMed.