Natural grasp intention recognition based on gaze fixation in human-robot interaction

TL;DR

This paper presents a gaze fixation-based method for recognizing grasping intentions, enabling paralyzed patients to communicate intentions through eye movements with high accuracy in controlled and real environments.

Contribution

It introduces a novel fixation-based approach for grasp intention recognition that is effective for users with severe movement impairments.

Findings

Recognition accuracy exceeds 89% on training objects.

Average accuracy exceeds 85% on new objects.

Method is effective in real environment tests.

Abstract

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.