Gaze Based Detection of Grasp Intentions with Generated Data

Eye movements are intricately connected to physical actions, making them a valuable tool for inferring a person’s intended movements. This connection becomes particularly crucial for individuals with severe movement impairments or paralysis, as eye movements often serve as their primary means of communication and interaction with their surroundings. Despite this potential, eye-tracking technology has seen limited use in intention recognition applications. This study aims to develop a method for recognizing grasping intentions based on gaze fixation, allowing individuals with hand mobility challenges to express their desired actions by simply focusing on the object they wish to interact with. To achieve this, we conduct experiments to explore the relationship between gaze fixations and various tasks. From these observations, we derive features and perform statistical analyses.