In the research of the impact of gestures using by a lecturer, one challenging task is to infer the attention of a group of audiences. Two important measurements that can help infer the level of attention are eye movement data and Electroencephalography (EEG) data. Under the fundamental assumption that a group of people would look at the same place if they all pay attention at the same time, we apply a method, "Time Warp Edit Distance", to calculate the similarity of their eye movement trajectories. Moreover, we also cluster eye movement pattern of audiences based on these pair-wised similarity metrics. Besides, since we don't have a direct metric for the "attention" ground truth, a visual assessment would be beneficial to evaluate the gesture-attention relationship. Thus we also implement a visualization tool.
@article{arxiv.1712.09709,
title = {Report: Dynamic Eye Movement Matching and Visualization Tool in Neuro Gesture},
author = {Qiangeng Xu and John Kender},
journal= {arXiv preprint arXiv:1712.09709},
year = {2018}
}