Predicting Visual Focus of Attention From Intention in Remote Collaborative Tasks

While shared visual space plays a very important role in remote collaboration on physical tasks, it is challenging and expensive to track users´ focus of attention (FOA) during these tasks. In this paper, we propose to identify a user´s FOA from his/her intention based on task properties, people´s actions in the workspace, and conversational content. We employ a conditional Markov model to characterize a subject´s FOA. We demonstrate the feasibility of the proposed method using a collaborative laboratory task in which one partner (the helper) instructs another (the worker) on how to assemble online puzzles. We model a helper´s FOA using task properties, workers´ actions, and conversational content. 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 proposed model can be used to predicate a user´s FOA in a remote collaborative task without tracking the user´s eye gaze.