Deep Unsupervised Activity Visualization using Head and Eye Movements

Abstract

We propose a method of visualizing user activities based on user's head and eye movements. Since we use an unobtrusive eyewear sensor, the measurement scene is unconstrained. In addition, due to the unsupervised end-to-end deep algorithm, users can discover unanticipated activities based on the exploratory analysis of low-dimensional representation of sensor data. We also suggest the novel regularization that makes the representation person invariant.