Recognition of Intentional Eye-Movement Behaviors for Assistive Human–Robot Interaction

For individuals with limited limb function, conventional manual control methods such as joysticks are not readily applicable to assistive robot operation, highlighting the need for a hands-free control method. Eye movements, as natural signals of human intention, offer advantages in intuitiveness and accessibility and are particularly suitable for such scenarios. However, prior studies have primarily focused on fixation-based gaze interaction, and relying solely on fixation is inadequate for human–robot interaction tasks that involve both robot movement and target selection. This paper presents an assistive human–robot interaction (HRI) system based on eye-movement behavior recognition. First, an eye-movement dataset named GazeRobDB containing four eye-movement behavior classes was constructed. Representative eye-movement features were then extracted and analyzed to support eye-movement behavior classification. Next, a feature-based recognition approach using machine-learning classifiers was developed to distinguish the four eye-movement behavior classes—fixation, saccade, intentional fixation, and intentional movement. Among the evaluated classifiers, random forest (RF) achieved the highest overall classification performance and was combined with the extracted eye-movement features to form the eye-movement behavior recognition module of the proposed HRI system. The recognized intentional fixation and intentional movement behaviors were then mapped to target selection and robot movement control, respectively. The resulting system was evaluated with 10 participants. These results demonstrate the feasibility of the proposed framework for hands-free robot control under the evaluated conditions and highlight its potential for assistive human–robot interaction.