Gaze Analysis System for Emotional Attunement in Human–Robot Interactions

Recent advances in artificial intelligence and robotics have accelerated the deployment of service robots in daily environments. However, many systems still lack adaptive responsiveness to the nonverbal behaviors of users. This study proposes a gaze-based user analysis system integrated into a mixed reality (MR) smart home environment to support attentional intention-aware human–system interactions. Rather than directly estimating emotional states, the proposed approach infers attentional intentions of users based on gaze behavior as an operational proxy for emotional attunement. Using gaze data collected through HoloLens 2, we develop a machine learning model based on a long short-term memory network combined with a mixture density network to predict future gaze coordinates in a three-dimensional space. The predicted gaze information is shared with a robotic partner to enable proactive context-aware information support. The proposed system demonstrates the feasibility of leveraging gaze prediction to anticipate user focus and provide adaptive support in MR-based smart environments.