Report: Dynamic Eye Movement Matching and Visualization Tool in Neuro Gesture

TL;DR

This paper introduces a tool that uses eye movement data and clustering techniques to infer audience attention during gestures, complemented by visualization for better assessment, advancing understanding of non-verbal communication in educational settings.

Contribution

It presents a novel method combining time warp edit distance and clustering for analyzing eye movements, along with a visualization tool to assess gesture-attention relationships.

Findings

Effective clustering of eye movement patterns.

Visualization aids in interpreting attention levels.

Potential for real-time attention inference.

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.