Implementation Of Face Distance, Usage Duration, And Smart Break Monitoring System Using Python-Based Haar Cascade On Digital Device Usage

Abstract

The prolonged use of digital devices with improper viewing distance can lead to eye strain and visual discomfort. This study aims to develop a real-time monitoring system for digital device usage based on digital image processing. The system is implemented using the Python programming language and the OpenCV library, with the Haar Cascade Classifier method used to detect the user's face through a webcam. Distance estimation is performed using single-camera distance estimation based on facial anthropometric data and the width of the detected face in the image, and is stabilized using smoothing techniques. In addition, the system utilizes user age input to determine the recommended viewing distance range, calculates device usage duration, and evaluates the level of usage risk based on distance, age, and usage time. The system provides warnings in the form of visual alerts, beep sounds, break reminder audio, and a Smart Break feature that requires the user to rest for 20 seconds after a certain usage duration is reached. The implementation results show that the system is capable of detecting faces in real time, estimating distance with stable performance, and providing adaptive and responsive usage notifications. Therefore, the proposed system can be used as a simple and effective eye health monitoring tool without requiring additional hardware.