Report: Dynamic Eye Movement Matching and Visualization Tool in Neuro Gesture
This work addresses the challenge of measuring audience attention in educational settings, but it is incremental as it applies existing methods to a specific domain without introducing new paradigms.
The researchers tackled the problem of inferring audience attention during lectures by analyzing eye movement and EEG data, using Time Warp Edit Distance to calculate similarity in eye movement trajectories and clustering patterns, and developed a visualization tool to assess gesture-attention relationships.
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.