Investigating Differences in Gaze and Typing Behavior Across Age Groups and Writing Genres

Typing is one of the most common activities that are undertaken on a computer. It would therefore be interesting to investigate whether it is possible to deduce characteristics of the user, such as their age or the type of the document that they are writing, just simply from typing dynamics. In this paper, we study the coordination between eye gaze and typing dynamics, or the gaze-typing behavior, of subjects who are producing original text. We focus upon the differences between different age groups (children vs elderly seniors) and different genres of writing (reminiscent, logical and creative). Using machine-learning, we achieve an accuracy of 93.5% for age detection and 61.1% for the article-category detection, using a leave-one-subject-out cross-validation evaluation, which is 44% and 28% higher than baselines.