In this paper, a YOLO is designed and developed. based screen time monitoring system that combines data logging and real-time face detection for precise computer tracking duration of use. The suggested system offers automated monitoring devoid of human involvement, addressing the growing issue of excessive screen time. The system is based on a specially trained YOLO object detection model that uses facial recognition to detect the presence of a particular user and guarantees accurate detection in a range of backgrounds and lighting conditions. There are four major subsystems that make up the core architecture: an OpenCV-based video processing pipeline for frame acquisition and visualization, a YOLOv8-based real- time detection engine tuned for webcam input, a MySQL-backed data storage system for recording cumulative screen time and presence intervals, and a PyQt-based graphical user interface with session control, usage analytics, and real-time monitoring.
V et al. (2026) studied this question.