Electrooculogram based approach for prevention of dry eye condition in computer users

In this paper an eye blink rate tracker is proposed for the persons working with computer for long duration. Now-a-days a common problem of computer users is Dry Eyes. Low blink is the reason behind redness and dryness of the eyes. While working in front of a computer screen, blink rate tends to decrease. This leads to inadequate tear film formation on the eye cornea. Designing an eye blink detection algorithm to apply in dry eye prevention is the ultimate goal of this work. Eye blink data along with other eye movement is recorded from Electrooculogram using a laboratory developed data acquisition system. Blinks are detected from recorded data using threshold. A maximum average accuracy of 96.67% is obtained in offline mode. To detect blinks in real time the trained classifier is used. The system counts the number of blinks for a certain time interval. In case of insufficient blinks the computer gets logged off. Thus forcing people working on a computer for long periods to rest the eyes until the computer is turned on manually. In real time, the proposed method is validated using a study on fifteen participants.