Abstract: Digital devices such as laptops, smartphones, and tablets have become essential tools for communication, nd professional work. As people increasingly depend on these devices, the amount of time spent looking at screens has grown significantly. Long hours of screen exposure can cause a condition commonly known as digital eye strain. Symptoms of digital eye strain include tired eyes, dryness, blurred vision, headaches, and reduced ability to focus. Many individuals experience these symptoms after extended periods of screen usage, but they often ignore them until the discomfort becomes severe.
Traditional solutions that attempt to reduce eye strain mainly rely on simple screen-time reminders or break notifications. These systems usually prompt users to rest their eyes after a fixed amount of time. However, these reminders do not evaluate the actual condition of the user’s eyes. Eye fatigue depends on several factors, such as blinking behaviour, gaze stability, posture, and the intensity of interaction with digital devices. Therefore, time-based reminders alone are not sufficient to detect or prevent eye strain effectively.
This research proposes an AI-based eye strain detection system that monitors eye behaviour in real time using a standard webcam. The system analyses several indicators, including blink rate, gaze movement, ocular micro-motions, head posture, and user interaction patterns such as typing and scrolling activity. These indicators are combined to calculate an eye strain score that represents the fatigue level of the user.
When the strain score crosses a predefined threshold, the system alerts the user and suggests taking a short break or adjusting posture. The proposed system also supports multi-user identification and multi-device environments, making it suitable for shared computers and modern workspaces. Because the system relies only on webcam input and computer vision techniques, it does not require specialised hardware. This makes the solution affordable, scalable, and suitable for everyday use in homes, offices, and educational institutions.

Keywords: Digital Eye Strain, Ocular Micro-Motion,Cognitive Load, Multi-Device Monitoring, Computer Vision.

Downloads: | DOI: 10.17148/IJIREEICE.2026.14379