EOG-Based Reading Detection in the Wild Using Spectrograms and Nested Classification Approach

Abstract

Electrooculography, also known as EOG, is a technique that is used to calculate the corneo-retinal standing potential, which is located between the cornea and the retina of the human eye. Applications of EOG include eye disease diagnosis and eye movement tracking. There has been various research on reading activity detection from EOG signals in controlled or laboratory settings. However, determining reading behaviours from data collected from real-world environments remains a challenging problem. Reading detection in practical scenarios can lead us to track our daily reading activity, in turn improving our learning experience and even workplace productivity. Tracking regular reading behaviour can also lead to further research in cognitive psychology, literacy development, reading motivation, and reading comprehension. In this study, we investigated an electrooculogram dataset that was collected on the field from 10 users who were engaged in their daily activities on two separate days. We propose a pipeline combining the statistical features with deep learning features from pre-trained ImageNet models. To detect the fine-grained reading activities, we employed a nested classification approach where we detect reading and not reading at first and then do one more step of classification to discriminate among three different reading activities. With our pipeline, we could achieve 66.56% accuracy in detecting the reading activities whereas the original dataset publication showed a baseline performance of only 32%.