Ho-Wan Kwak

Human intention recognition based on eyeball movement pattern and pupil size variation

To develop an efficient nonverbal human computer interaction system it is important to interpret the users implicit intention, which is vague. According to cognitive visuo-motor theory, the human eye movements are a rich source of information about the human intention and behavior. According to Beattys study, a task-evoked pupillary response is a consistent index of the human cognitive load and attention. In this paper, we propose a novel approach for a humans implicit intention recognition based on the eyeball movement pattern and pupil size variation. Based on the Bernards research, we classify the humans implicit intention during a visual stimulus as informational and navigational intent. In the present study, the navigational intent refers to the humans idea to find some interesting objects in a visual input without a particular goal while the informational intent refers to the humans aspiration to find a particular object of interest. The proposed model utilizes the salient features of the eye such as fixation length, fixation count and pupil size variation as the inputs to classify the humans implicit intention. The experimental results show that the proposed model can achieve plausible recognition performance.

Human implicit intent transition detection based on pupillary analysis

Interpretation of human implicit intention is crucial in the development of an efficient nonverbal human computer interaction system. According to cognitive visuo-motor theory, the human eye movements and pupillary responses are rich source of information about the human intention and behavior. It has been observed that under conditions of constant illumination and accommodation, pupil size varies systematically in relation to a variety of physiological and psychological factors, such as level of mental effort. It is well known that pupillary responses could be used to measure the differences in cognitive load under various tasks. In this paper, we try to detect the transition between the different human implicit intents based on the pupil state analysis. In real-world environment, the pupillary response can be influenced by various external factors like intensity and size of the image. To overcome the influence of the external factors, we develop a robust baseline model. The proposed approach detects the transition of the humans implicit intent from navigational intent to informational intent and vice versa during a visual stimulus. The approach also detects the transition among the different states of the informational intent such as informational intent generation, informational intent maintenance and informational intent disappear.

Recognition of human's implicit intention based on an eyeball movement pattern analysis

We propose a new approach for a humans implicit intention recognition system based on an eyeball movement pattern analysis. In this paper, we present a comprehensive classification of humans implicit intention. Based on Bernards research, we define the Humans implicit intention as informational and navigational intent. The intent for navigational searching is to locate a particular interesting object in an input scene. The intent for informational searching is to locate interesting area concerning a particular topic in order to obtain information from a specific location. In the proposed model, eyeball movement pattern analysis is considered for classifying the two different types of implicit intention. The experimental results show that the proposed model generates plausible recognition performance using a fixation length and counts with a simple nearest neighborhood classifier.

Saccadic movement deficiencies in adults with ADHD tendencies.

Abstract The goal of the present study was to explore deficits in gaze detection and emotional value judgment during a saccadic eye movement task in adults with attention deficit/hyperactivity disorder (ADHD) tendencies. Thirty-two participants, consisting of 16 ADHD tendencies and 16 controls, were recruited from a pool of 243 university students. Among the many problems in adults with ADHDs, our research focused on the deficits in the processing of nonverbal cues, such as gaze direction and the emotional value of others’ faces. In Experiment 1, a cue display containing a face with emotional value and gaze direction was followed by a target display containing two faces located on the left and right side of the display. The participant’s task was to make an anti-saccade opposite to the gaze direction if the cue face was not emotionally neutral. ADHD tendencies showed more overall errors than controls in making anti-saccades. Based on the hypothesis that the exposure duration of the cue display in Experiment 1 may have been too long, we presented the cue and target display simultaneously to prevent participants from preparing saccades in advance. Participants in Experiment 2 were asked to make either a pro-saccade or an anti-saccade depending on the emotional value of the central cue face. Interestingly, significant group differences were observed for errors of omission and commission. In addition, a significant three-way interaction among groups, cue emotion, and target gaze direction suggests that the emotional recognition and gaze control systems might somehow be interconnected. The result also shows that ADHDs are more easily distracted by a task-irrelevant gaze direction. Taken together, these results suggest that tasks requiring both response inhibition (anti-saccade) and gaze-emotion recognition might be useful in developing a diagnostic test for discriminating adults with ADHDs from healthy adults.