Print Email Facebook Twitter Cognitive activity recognition by analyzing eye movement with convolutional neural networks Title Cognitive activity recognition by analyzing eye movement with convolutional neural networks Author Brockbernd, Bob (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Lan, G. (mentor) Du, L. (mentor) Spaan, M.T.J. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2022-06-23 Abstract This research proposes a novel method to classify cognitive behavior based on eye-movement data. Most state-of-the-art approaches use conventional machine learning techniques needing manual feature extraction. This experiment explores the possibility of applying deep learning algorithms to cognitive activity recognition for feature extraction and classification of eye-movement data. Convolutional neural networks will be explored in particular. Two neural networks are proposed and optimized using hyperparameter tuning. This research shows that convolutional neural networks can indeed perform cognitive activity recognition. Some neural networks significantly outperform the state-of-the-art methods for known subjects. However, further research is needed to improve performance in classifying activities for unknown subjects. Subject Human Activity RecognitionGaze bazed activity recognitionCNN To reference this document use: http://resolver.tudelft.nl/uuid:5ff6be37-5ba5-4d7c-8070-417a660aac14 Part of collection Student theses Document type bachelor thesis Rights © 2022 Bob Brockbernd Files PDF Research_Paper_final.pdf 737.86 KB Close viewer /islandora/object/uuid:5ff6be37-5ba5-4d7c-8070-417a660aac14/datastream/OBJ/view