GazeGuard
Because your mind needs a break
Supervised By
Start
Dr.Abdelrahman Metawly Eng. Abdallah Moataz
Agenda
Learning strategy based on small units of content that are consumed quickly. Ideal for reinforcing concepts or learning in a flexible way.
Consists of applying game dynamics (challenges, rewards, levels) in learning environments to increase user motivation and engagement.
Describes the process and pace at which a person acquires a new skill. The clearer and more accessible the information, the faster the progression.
Learning strategy based on small units of content that are consumed quickly. Ideal for reinforcing concepts or learning in a flexible way.
System Requirments
Related Works
Project Scope
GazeGuard Introduction
Learning strategy based on small units of content that are consumed quickly. Ideal for reinforcing concepts or learning in a flexible way.
Didactic technique that proposes solving real or simulated situations to foster critical thinking and practical application of knowledge.
Level of emotional and mental involvement of the user with the content. High engagement improves retention of information and overall course experience.
Feedback received by the user immediately after an activity. Helps correct errors quickly and improves understanding.
References
Conclusion
User Interface
Workflow
Cognitive fatigue comes from long screen time, sustained focus, and repetitive visual tasks.
Problem Statement
Eye strain affects up to 58% of adult computer users.
Concentration drops after 50–60 minutes of continuous screen use.
Visual fatigue is widespread among screen users, varying significantly by occupation
What is GazeGuard?
GazeGuard is an intelligent visual-fatigue monitoring system that watches for signs of eye strain and cognitive fatigue. Tracking your gaze, blinks, and facial cues to keep your mind protected during long screen sessions.
Related Works
Vs
In Scope
- Gamers & content creators
Out of Scope
- Clinical-grade evaluation
- Environments without a camera
Functional Requirements
AI Detection & Prediction
Continuous Monitoring
Use AI to detect early fatigue and predict strain before it escalates.
Track eyes, face, and head movement in real time.
Personalized Feedback
Provide alerts and recommendations based on user behavior.
Non-Functional Requirements
Data privacy and security.
Scalability
Real-time performance with minimal latency.
System Workflow
GazeGuard collects live visual data, cleans and analyzes it to identify signs of fatigue. The AI model predicts upcoming strain and delivers timely alerts and recommendations, with all results stored securely for future evaluation.
Describes the process and pace at which a person acquires a new skill. The clearer and more accessible the information, the faster the progression.
Describes the process and pace at which a person acquires a new skill. The clearer and more accessible the information, the faster the progression.
Camera Activitaion: The system captures live video frames via the built in laptop camera
Landmark Detection: Using OpenCV and MediaPipe
Describes the process and pace at which a person acquires a new skill. The clearer and more accessible the information, the faster the progression.
Describes the process and pace at which a person acquires a new skill. The clearer and more accessible the information, the faster the progression.
History Tracking: Saves logs securely for progress tracking and further learning.
Preprocessing: using normalization and interpolation
Smart Notifications: Flutter Ui delivers instant notifications with a personalized recommendation to take a break
Describes the process and pace at which a person acquires a new skill. The clearer and more accessible the information, the faster the progression.
Describes the process and pace at which a person acquires a new skill. The clearer and more accessible the information, the faster the progression.
Describes the process and pace at which a person acquires a new skill. The clearer and more accessible the information, the faster the progression.
ML: The AI engine processes these signals to predict fatigue
Feature Extraction: EAR ,blink rate, Pupil dialtion
Limitations
Environmental factors (lighting, angle) The system's accuracy can be affected by poor or unevenlighting, non-frontal cameraangles, and excessive distance between the user and the camera.
Dependence on standard camera hardware. GazeGuard relies on the user's standard Built-in / WebCam. Variations in camera quality across devices may lead to inconsistent detection performance.
Future Work
Mobile & Cross-Platform Expansion
Environmental Adaptability
Hardware Expansion
Implementing advanced light-compensation algorithms to maintain high detection accuracy in low-light or high-glare environments.
Develop mobile versions of the application to monitor visual fatigue on smartphones and tablets, ensuring a seamless experience across all digital devices
Testing the system's performance with external high-definition webcams versus built-in laptop cameras to establish hardware-specific accuracy benchmarks
Conclusion
GazeGuard demonstrates how AI and eye-tracking technologies can work together to detect early signs of fatigue and stress. The project shows strong potential for enhancing user safety, reducing eye strain, and promoting healthier digital habits.
References
[1] Rana, M. R., & Baul, S. , "Development of a Real-time Driver's Drowsiness Detection System Using MediaPipe Face Mesh. MECS Press." ,2025 [2] https://www.sightkick.ai/ Last Visited: 11/2/2026 [3] https://imotions.com/ Last visited : 11/2/2026 [4] American Optometric Association. “Most Americans Experience Digital Eye Strain from Overexposure to Computers According to Survey. 23 Oct. 2016 [5] Sievertsen, H. H., Gino, F., & Piovesan, M. "Cognitive Fatigue Influences Students' Performance on Standardized Tests." ,2016
Thank you!
GazeGuard
Shahd Abdelhalim
Created on February 5, 2026
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Transcript
GazeGuard
Because your mind needs a break
Supervised By
Start
Dr.Abdelrahman Metawly Eng. Abdallah Moataz
Agenda
Learning strategy based on small units of content that are consumed quickly. Ideal for reinforcing concepts or learning in a flexible way.
Consists of applying game dynamics (challenges, rewards, levels) in learning environments to increase user motivation and engagement.
Describes the process and pace at which a person acquires a new skill. The clearer and more accessible the information, the faster the progression.
Learning strategy based on small units of content that are consumed quickly. Ideal for reinforcing concepts or learning in a flexible way.
System Requirments
Related Works
Project Scope
GazeGuard Introduction
Learning strategy based on small units of content that are consumed quickly. Ideal for reinforcing concepts or learning in a flexible way.
Didactic technique that proposes solving real or simulated situations to foster critical thinking and practical application of knowledge.
Level of emotional and mental involvement of the user with the content. High engagement improves retention of information and overall course experience.
Feedback received by the user immediately after an activity. Helps correct errors quickly and improves understanding.
References
Conclusion
User Interface
Workflow
Cognitive fatigue comes from long screen time, sustained focus, and repetitive visual tasks.
Problem Statement
Eye strain affects up to 58% of adult computer users.
Concentration drops after 50–60 minutes of continuous screen use.
Visual fatigue is widespread among screen users, varying significantly by occupation
What is GazeGuard?
GazeGuard is an intelligent visual-fatigue monitoring system that watches for signs of eye strain and cognitive fatigue. Tracking your gaze, blinks, and facial cues to keep your mind protected during long screen sessions.
Related Works
Vs
In Scope
Out of Scope
Functional Requirements
AI Detection & Prediction
Continuous Monitoring
Use AI to detect early fatigue and predict strain before it escalates.
Track eyes, face, and head movement in real time.
Personalized Feedback
Provide alerts and recommendations based on user behavior.
Non-Functional Requirements
Data privacy and security.
Scalability
Real-time performance with minimal latency.
System Workflow
GazeGuard collects live visual data, cleans and analyzes it to identify signs of fatigue. The AI model predicts upcoming strain and delivers timely alerts and recommendations, with all results stored securely for future evaluation.
Describes the process and pace at which a person acquires a new skill. The clearer and more accessible the information, the faster the progression.
Describes the process and pace at which a person acquires a new skill. The clearer and more accessible the information, the faster the progression.
Camera Activitaion: The system captures live video frames via the built in laptop camera
Landmark Detection: Using OpenCV and MediaPipe
Describes the process and pace at which a person acquires a new skill. The clearer and more accessible the information, the faster the progression.
Describes the process and pace at which a person acquires a new skill. The clearer and more accessible the information, the faster the progression.
History Tracking: Saves logs securely for progress tracking and further learning.
Preprocessing: using normalization and interpolation
Smart Notifications: Flutter Ui delivers instant notifications with a personalized recommendation to take a break
Describes the process and pace at which a person acquires a new skill. The clearer and more accessible the information, the faster the progression.
Describes the process and pace at which a person acquires a new skill. The clearer and more accessible the information, the faster the progression.
Describes the process and pace at which a person acquires a new skill. The clearer and more accessible the information, the faster the progression.
ML: The AI engine processes these signals to predict fatigue
Feature Extraction: EAR ,blink rate, Pupil dialtion
Limitations
Environmental factors (lighting, angle) The system's accuracy can be affected by poor or unevenlighting, non-frontal cameraangles, and excessive distance between the user and the camera.
Dependence on standard camera hardware. GazeGuard relies on the user's standard Built-in / WebCam. Variations in camera quality across devices may lead to inconsistent detection performance.
Future Work
Mobile & Cross-Platform Expansion
Environmental Adaptability
Hardware Expansion
Implementing advanced light-compensation algorithms to maintain high detection accuracy in low-light or high-glare environments.
Develop mobile versions of the application to monitor visual fatigue on smartphones and tablets, ensuring a seamless experience across all digital devices
Testing the system's performance with external high-definition webcams versus built-in laptop cameras to establish hardware-specific accuracy benchmarks
Conclusion
GazeGuard demonstrates how AI and eye-tracking technologies can work together to detect early signs of fatigue and stress. The project shows strong potential for enhancing user safety, reducing eye strain, and promoting healthier digital habits.
References
[1] Rana, M. R., & Baul, S. , "Development of a Real-time Driver's Drowsiness Detection System Using MediaPipe Face Mesh. MECS Press." ,2025 [2] https://www.sightkick.ai/ Last Visited: 11/2/2026 [3] https://imotions.com/ Last visited : 11/2/2026 [4] American Optometric Association. “Most Americans Experience Digital Eye Strain from Overexposure to Computers According to Survey. 23 Oct. 2016 [5] Sievertsen, H. H., Gino, F., & Piovesan, M. "Cognitive Fatigue Influences Students' Performance on Standardized Tests." ,2016
Thank you!