analyzing and predicting focus of attention in remote collaborative tasks
To overcome the limitations of current technologies for remote collaboration, we propose a system that changes a video feed based on task properties, people's actions, and message properties. First, we examined how participants manage different visual resources in a laboratory experiment using a collaborative task in which one partner (the helper ) instructs another (the worker ) how to assemble online puzzles. We analyzed helpers' eye gaze as a function of the aforementioned parameters. Helpers gazed at the set of alternative pieces more frequently when it was harder for workers to differentiate these pieces, and less frequently over repeated trials. The results further suggest that a helper's desired focus of attention can be predicted based on task properties, his/her partner's actions, and message properties. We propose a conditional Markov model classifier to explore the feasibility of predicting gaze based on these properties. The accuracy of the model ranged from 65.40\% for puzzles with easy-to-name pieces to 74.25\% for puzzles with more difficult to name pieces. The results suggest that we can use our model to automatically manipulate video feeds to show what helpers want to see when they want to see it.
focus of attention remote collaborative tasks keyword spotting computer-supported cooperative work eye tracking