AI-Powered Smart Glasses for Digital Eye Strain and Drowsiness Detection

Abstract: Prolonged digital screen usage can contribute to visual discomfort, eye fatigue, and drowsiness, particularly during extended periods of computer use. This paper presents an AI-powered smart glasses system designed to monitor eye activity and identify patterns associated with digital eye strain and drowsiness. The proposed architecture incorporates a Convolutional Neural Network (CNN) for eye-state classification and a Long Short-Term Memory (LSTM) network for temporal analysis of eye-related features. Eye activity is monitored using facial and eye landmarks, from which eye aspect ratio and related features are extracted. Sequential analysis is used to distinguish normal eye activity from patterns associated with eye strain and drowsiness. The system architecture also includes an alert mechanism intended to notify the user when potentially concerning eye-activity patterns are detected. The implementation work includes CNN-based eye-image classification, LSTM-based state classification, and a real-time eye-monitoring prototype. The approach provides a foundation for wearable monitoring of eye activity and supports further integration into a compact smart glasses platform.

Keywords: Smart Glasses, Digital Eye Strain, Drowsiness Detection, Convolutional Neural Network, Long Short-Term Memory, Eye Aspect Ratio, Real-Time Monitoring.