Title: Report: Dynamic Eye Movement Matching and Visualization Tool in Neuro Gesture,
Abstract: 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.