Spatio-Temporal Gaze Regularity-Guided Dynamic Fuzzy Bayesian Network for Intention Inference

Gaze-based object manipulation intention inference is pivotal to natural and intuitive human-robot interaction. Existing methods confine spatial regularity to independent object selection and treat temporal regularity only as sequential order, thus spatio-temporal gaze regularities remain insufficiently exploited and lack a unified treatment. The objective of this study is to statistically analyze, model, and integrate gaze regularities within a unified probabilistic framework for inference. Accordingly, we propose a spatio-temporal gaze regularities guided dynamic fuzzy Bayesian network (DFBN) for intention inference. We statistically analyze the spatial gaze regularity as mutual exclusion and co-occurrence in joint object selection patterns, and the temporal regularity as duration-dependent attention with sequential dependencies. The regularities are modeled into probabilistic form, with spatio regularity modeled by autoregressive logistic regression and temporal regularity modeled by a Fuzzy Gaze-LSTM that fuses gaze duration with sequential order. These probabilistic models are fused into a likelihood modifier to generate interpretable posterior probabilities of intention, integrating a Bayesian network and sequential inference. Cross-dataset evaluations indicate stable and high performance. DFBN attains 96.47 1.75% accuracy and 96.41 1.85% F1 score, maintains accuracy on error sequences, and generalizes across younger and older groups, supporting robust intention inference. This study has the potential to inform other human-robot interaction assistance strategies by serving as intuitive gaze regularity cues.