Focusguard: Real-time self-monitoring system for enhancing student focus using computer vision

Soo, Jia Sheng (2025) Focusguard: Real-time self-monitoring system for enhancing student focus using computer vision. Final Year Project, UTAR.

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Abstract

A real-time computer vision tool called FocusGuard aids students in focusing and avoiding distractions while studying. The system uses Eye Aspect Ratio (EAR) to detect drowsiness, head pose estimation to monitor attention, and Mouth Aspect Ratio (MAR) to detect yawns to prevent fatigue. These three crucial components of student focus are addressed by the system. The solution encourages organized study sessions by combining these elements with a Pomodoro Timer. Key facial features are tracked by the system using MediaPipe's facial landmark detection. FocusGuard gives students timely audio alerts to help them focus again when it detects signs of distraction or drowsiness. An early warning sign of possible fatigue is the yawn detection, while the head pose analysis detects whether students are avoiding their eyes from their study materials. The combination of these monitoring features with the Pomodoro Technique, which divides study sessions into concentrated work periods and selected breaks, is a significant innovation. This combination results in a clever study partner that keeps an eye on and directs student conduct. Students can monitor their focus patterns over time with the system's web-based interface, which shows session statistics and real-time metrics. The system's ability to detect periods of fatigue and diminished attention is demonstrated by preliminary testing. FocusGuard is a real-world example of how computer vision technology can be used to enhance student productivity and learning.

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