Response Surface Methodology to Optimize the Screen Brightness and Duration of Laptop Usage Based on the Eye Blink Rate

During the lockdown caused by the COVID-19 pandemic, the average screen time of a person saw a drastic increase. Laptops and personal computers were used throughout the pandemic for entertainment and work, and many jobs shifted to the online medium. Thus the use of laptops and personal computers for entertainment and work continued even after the pandemic. But, long hours of work combined with the use of screens lead to many people staring at a screen for a large part of their day leading to eye-related problems like digital eye syndrome. This became a widespread problem of its own as more and more people began to heavily rely on their digital devices. A low blink rate is also a prominent cause of digital eye strain. A healthy human at rest blinks around 17 times per minute while a human using a screen blinks around 12–15 times per minute. The aim and objective of the present study are to determine the optimal level of brightness and duration of usage for the most comfort of computer screen usage. In order to achieve the aims and objectives of the study, the data collected are analyzed using the response surface methodology (RSM) and contour plot method. The RSM optimizer is employed to compute the optimal brightness level and duration of usage. The data is collected by a python code built using the openCV, CVzone, and FaceMesh libraries. The data collected at different levels of brightness ranging from 50 to 100 at intervals of 10 and the duration of usage is ranged from 10 to 30 minutes at an interval of 10 minutes. Full factorial design of experiment (DOE) is employed to plan the experiments. The data were collected from 50 participants for 6 days from 10 AM to 3 PM.