Natural grasp intention recognition based on gaze fixation in human-robot interaction
This work addresses the problem of intuitive human-robot interaction for individuals with severe hand movement disorders by leveraging gaze fixation for grasp intention recognition.
This paper proposes a method for recognizing grasping intentions based on gaze fixation. The method achieved over 89% accuracy on training objects and over 85% on unseen objects, enabling users with hand movement disorders to intuitively express their desired tasks.
Eye movement is closely related to limb actions, so it can be used to infer movement intentions. More importantly, in some cases, eye movement is the only way for paralyzed and impaired patients with severe movement disorders to communicate and interact with the environment. Despite this, eye-tracking technology still has very limited application scenarios as an intention recognition method. The goal of this paper is to achieve a natural fixation-based grasping intention recognition method, with which a user with hand movement disorders can intuitively express what tasks he/she wants to do by directly looking at the object of interest. Toward this goal, we design experiments to study the relationships of fixations in different tasks. We propose some quantitative features from these relationships and analyze them statistically. Then we design a natural method for grasping intention recognition. The experimental results prove that the accuracy of the proposed method for the grasping intention recognition exceeds 89\% on the training objects. When this method is extendedly applied to objects not included in the training set, the average accuracy exceeds 85\%. The grasping experiment in the actual environment verifies the effectiveness of the proposed method.