Deep Learning for Karolinska Sleepiness Scale Classification Based On Eye Aspect Ratio with SMOTE-Enhanced Data Balancing

This paper addresses the challenge of accurately classifying sleepiness levels based on the Karolinska Sleepiness Scale (KSS) using Eye Aspect Ratio (EAR) data, especially when class imbalance leads to biased predictions. The research proposes a deep learning framework that integrates a Multi-Layer Perceptron (MLP) with the Synthetic Minority Over-sampling Technique (SMOTE) to balance the training data. EAR features, representing eye closure patterns, are extracted from video frames, and SMOTE is applied to generate synthetic data for underrepresented sleepiness classes. By training the MLP model on this balanced dataset, the system achieves a 97.6% classification accuracy in distinguishing four distinct sleepiness levels based on the KSS, demonstrating its effectiveness in reducing prediction bias and managing class imbalance, both crucial for real-time drowsiness detection systems