John Paul Quilingking Tomas
ABSTRACT
This study focused on developing models for detecting fatigue in students using action units during conducting online classes. A multi-layered neural network was used as a classifier for the dataset and the parameters included were blinking frequency, blink duration, yawn frequency, yawn duration, and head roll, pitch, and yaw which were in the form of action units. Stratified K-Fold cross-validation was used for model validation. Overall results that the model with a batch size of 1800 and 2 epochs, input size of 3600 for the first layer and 1800 for the second layer which both have activation of ReLU, and input size of 1 in the third layer which has activation of Sigmoid showed an acceptable performance in predicting fatigue with 90.37% for recall and 90.23% for precision.
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Index Terms
- Model Development for Fatigue Detection During Synchronous Online Classes
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