  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.
