Towards gaze-based prediction of the intent to interact in virtual reality

Brendan David-John, Candace Peacock, Ting Zhang, T. Scott Murdison, Hrvoje Benko, Tanya R. Jonker

Abstract: With the increasing frequency of eye tracking in consumer products, including head-mounted augmented and virtual reality displays, gaze-based models have the potential to predict user intent and unlock intuitive new interaction schemes. In the present work, we explored whether gaze dynamics can predict when a user intends to interact with the real or digital world, which could be used to develop predictive interfaces for low-effort input. Eye-tracking data were collected from 15 participants performing an item-selection task in virtual reality. Using logistic regression, we demonstrated successful prediction of the onset of item selection. The most prevalent predictive features in the model were gaze velocity, ambient/focal attention, and saccade dynamics, demonstrating that gaze features typically used to characterize visual attention can be applied to model interaction intent. In the future, these types of models can be used to infer user’s near-term interaction goals and drive ultra-low-friction predictive interfaces.