Visual Attention Score and Fatigue Level Measure of Students through Eye Analysis–Machine Learning Approach | IEEE Conference Publication | IEEE Xplore

Visual Attention Score and Fatigue Level Measure of Students through Eye Analysis–Machine Learning Approach


Abstract:

The new education paradigm changes the classrooms to E-Learning platforms. The student engagement monitoring is a key problem in E-Learning. This work put forward an effi...Show More

Abstract:

The new education paradigm changes the classrooms to E-Learning platforms. The student engagement monitoring is a key problem in E-Learning. This work put forward an efficient mechanism to measure the visual attention of a learner by analyzing various face attributes and the fatigue level. Eye closure is considered as an indicator of fatigue in humans. The novel architecture employs machine learning algorithms to predict the fatigue through bi-state eye classification. Different experimentation are done using various classifiers and feature extraction techniques. The accuracy of the best model derived reaches up to 93.6% through principal component analysis based feature extraction and random forest classifier. The testing of the model is performed on different datasets and on real-time video. The results indicate the efficacy of the model in analyzing the visual attention score during learning.
Date of Conference: 24-26 November 2022
Date Added to IEEE Xplore: 16 February 2023
ISBN Information:

ISSN Information:

Conference Location: Kochi, India

Contact IEEE to Subscribe

References

References is not available for this document.