Papers by Keyword: Eye Movement

Abstract: Nature Scene classification is a fundamental problem in image understanding. Human can recognize the scene instantly after only a glance. This is mainly because that our visual attention is easily attracted by the salient objects in scene. And these objects are always representative in the natural scene. It is unclear how humans achieve rapid scene categorization. But this kind of high-level cognitive behavior can be reflected by the eye movement. To identify this ability, we propose a model with the guidance of eye movement. It combines the bag of words (BOW) and spatial pyramid matching (SPM) methods to train and test our model on support vector machine (SVM). The eye movement experiments were employed to validate our model. We found that the subjects could recognize the scenes correctly even if given only a few saliency patches with less than one second. These results suggest that the eye tracking saliency patches play an important role for human scene categorization.

Abstract: The study of this paper is to implementation the fuzzy logic control designed for wheelchair motion based on the eye movement signals using electrooculograhphy (EOG) technique. This technique is to acquire the eye movement data from a person, for example, tetraplegia. The tetraplegia is paralysis caused by illness or injury to a human that result in the partial or total loss of use of all their limbs and torso. The eye movement data which was obtained can be used as a main communication tool between human and machine. The PD-type fuzzy controller was successfully designed and tested on the wheelchair model, for control the linear motion (focused for forward motion). The wheelchair model was developed using MSC.Visual Nastran 4D. The results obtained show that the PD-type fuzzy logic controller designed has successfully managed to track the input reference for linear motion set by the EOG signal.

Abstract: This study investigates the characteristics of eye movements by operating flat knitting machine. For the objective evaluation purpose of the flat knitting machine operation interface, we arrange participants finish operation tasks on the interface, then use eye tracker to analyze and evaluate the layout design. Through testing of the different layout designs, we get fixation sequences, the count of fixation, heat maps, and fixation length. The results showed that the layout design could significantly affect the eye-movement, especially the fixation sequences and the heat maps, the count of fixation and fixation length are always impacted by operation tasks. Overall, data obtained from eye movements can not only be used to evaluate the operation interface, but also significantly enhance the layout design of the flat knitting machine.

Abstract: The aim of this study is to perform the experimental verification on the fuzzy-based control designed for wheelchair motion. This motion control based on the eye movement signals using electrooculograhphy (EOG) technique. The EOG is a technique to acquire the eye movement data from a person, i.e tetraplegia, which the data obtained, can be used as a main communication tool. This study is about the implementation of the designed controller using PD-type fuzzy controller and tested on the hardware of the wheelchair system using the eye movement signal obtained through EOG technique as the motion input references. The results obtained show that the PD-type fuzzy logic controller designed has successfully managed to track the input reference for linear motion set (forward and backward direction) by the EOG signal.

Abstract: With the development of modern society, network marketing has become an important business models. In order to build an effective shopping website interface layout design visual assessment method, and promote the standardization of website interface layout design, in this study, using eye tracking technology and statistical methods, to get the viewed hotspots and browsing habits of the subjects., then analyzed the rationality of shopping website interface layout , to provide scientific reference for website interface layout design.

Abstract: Finding bugs in CMOS Integrated Circuit (IC) layouts is a basic skill for IC design engineers and students alike. The reading process of finding bugs is the basis for learning and teaching in electronic engineering. In this pilot study, eye-movement data was used in analyzing the reading process and nature of five participants (N=5) finding bugs in CMOS layouts. Data analysis of eye movements was based on nine types of ROI (Region of Interest). The ANOVA analysis of eye movements was analyzed. The findings of experimental results included that there were significant differences among the number of fixations of nine types of ROIs. The findings suggest how learners could read the bugged IC layouts effectively and efficiently.

Abstract: To investigate the different modes of human thinking, we designed an eye tracking experiment during people recognized two category images of histograms and scenes, and used the support vector machine (SVM) classification algorithm to classify these eye movement data. The results of statistical analysis showed that there were significant differences in saccade distance and pupil diameter between these two category images. By the feature selection, normalization of data preprocessing, and SVM classification, the results of classification analysis showed that there was a better performance on the classification of the histograms and scenes. These results suggest we can identify the modes of human thinking through the SVM classification methods based on the eye movement data.

Abstract: Objective To study expert and novice eye movement pattern during simulated landing flight for providing references to evaluate flight performance and training of pilots. Methods The subjects were divided in to two group s of expert and novice according to their flight simulation experience. Eye movement data were recorded when they were performing landing task. Comparison of expert and novice flight performance data and eye movement data was made. Results It was found that the differences between expert and novice lay not only in flight performance but also in eye movement pattern. Performance of expert was better than novice. Expert had shorter fixation time, more fixation points, faster scan velocity, greater scan frequency and wider scan area than novice. It was also found that eye movement pattern of expert bring lower mental workload than novice. Conclusion Flight performance is related to eye movement pattern. Effective eye movement pattern is related to good flight performance. The analysis of eye movement indices can evaluate pilots’ flight performance and provide reference for flight training.

Abstract: In order to evaluate pilot performance objectively, back propagation (BP) neural network model of 621423 form in topology with eye movement data was established. Data source of BP neural networks that came from former experiment and random interpolation was divided into training set and test set and normalized. Based on neural networks toolbox in Matlab, hidden layer nodes of BP networks were determined with empirical formula and experimental comparison ; BP algorithms in the toolbox were optimized; The training set data and test data were input into model for training and simulation; Pilot performance of the three skill levels was predicated and evaluated. The research shows that pilot performance can be accurately evaluated by setting up BP neural networks model with eye movement data and the evaluation method can provide a reference for flight training.