Reading Progress Tracking Using Convolutional Neural Networks on High-Noise Eye-Tracking Data
A. I. Shangareev, Sergey A. Stupnikov
Abstract
Abstract The paper is devoted to studying the methods for tracking the reading progress on eye-tracking data using deep learning neural networks. An architecture of the autoencoder neural network is developed that is intended for efficient use the spatial and temporal information. A data augmentation method is proposed that generates high noise data and preserves information about the correspondence of each gaze fixation to a corresponding word. The quality of the neural network model is experimentally evaluated on noisy data.
Topics & Concepts
Computer scienceConvolutional neural networkEye trackingTracking (education)Artificial intelligenceReading (process)Noise (video)Pattern recognition (psychology)Computer visionSpeech recognitionPsychologyImage (mathematics)Political scienceLawPedagogyGaze Tracking and Assistive TechnologyRetinal Imaging and Analysis