Collaboration Assistance Through Object Based User Intent Detection Using Gaze Data

As eye-tracking technology becomes increasingly prevalent in augmented reality (AR), new opportunities arise for collaborative applications. In this paper, we propose a novel approach to improve collaborative interaction through object-based user intent detection using gaze data. Our system uses reinforcement learning (RL) to dynamically adapt the user interface based on the context of the collaborative task. The system visualizes the user’s intent on a shared environment, allowing for improved collaborative awareness between users. We evaluate our approach in a user study scenario focused on visual search tasks. The results demonstrate that our system significantly improves task completion times and reduces cognitive load for users. Additionally, subjective feedback suggests that users are more aware of each other’s activity, further highlighting the benefits of our approach. We encourage conducting future user studies to assess the suitability of our approach for additional collaborative tasks.