A gaze-contingent intention decoding engine for human augmentation
Pavel A. Orlov, Ali Shafti, Chaiyawan Auepanwiriyakul, Noyan Songur, A. Aldo Faisal · Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications · 2018
Humans process high volumes of visual information to perform everyday tasks. In a reaching task, the brain estimates the distance and position of the object of interest, to reach for it. Having a grasp intention in mind, human eye-movements produce specific relevant patterns. Our Gaze-Contingent Intention Decoding Engine uses eye-movement data and gaze-point position to indicate the hidden intention. We detect the object of interest using deep convolution neural networks and estimate its position in a physical space using 3D gaze vectors. Then we trigger the possible actions from an action grammar database to perform an assistive movement of the robotic arm, improving action performance in physically disabled people. This document is a short report to accompany the Gaze-contingent Intention Decoding Engine demonstrator, providing details of the setup used and results obtained.