Automatic Gaze Classification for Aviators: Using Multi-task Convolutional Networks as a Proxy for Flight Instructor Observation

In this work, we investigate how flight instructors observe aviator scan patterns and assign quality to an aviator\u27s gaze. We first establish the reliability of instructors to assign similar quality to an aviator\u27s scan patterns, and then investigate methods to automate this quality using machine learning. In particular, we focus on the classification of gaze for aviators in a mixed-reality flight simulation. We create and evaluate two machine learning models for classifying gaze quality of aviators: a task-agnostic model and a multi-task model. Both models use deep convolutional neural networks to classify the quality of pilot gaze patterns for 40 pilots, operators, and novices, as compared to visual inspection by three experienced flight instructors. Our multi-task model can automate the process of gaze inspection with an average accuracy of over 93.0% for three separate flight tasks. Our approach could assist existing flight instructors to provide feedback to learners, or it could open the door to more automated feedback for pilots learning to carry out different maneuvers

Differential Privacy for Eye-Tracking Data

As large eye-tracking datasets are created, data privacy is a pressing concern for the eye-tracking community. De-identifying data does not guarantee privacy because multiple datasets can be linked for inferences. A common belief is that aggregating individuals' data into composite representations such as heatmaps protects the individual. However, we analytically examine the privacy of (noise-free) heatmaps and show that they do not guarantee privacy. We further propose two noise mechanisms that guarantee privacy and analyze their privacy-utility tradeoff. Analysis reveals that our Gaussian noise mechanism is an elegant solution to preserve privacy for heatmaps. Our results have implications for interdisciplinary research to create differentially private mechanisms for eye tracking

Visualizing the Reading Activity of People Learning to Read

Several popular visualizations of gaze data, such as scanpaths and heatmaps, can be used independently of the viewing task. For a specific task, such as reading, more informative visualizations can be created. We have developed several such techniques, some dynamic and some static, to communicate the reading activity of children to primary school teachers. The goal of the visualizations was to highlight the reading skills to a teacher with no background in the theory of eye movements or eye tracking technology. Evaluations of the techniques indicate that, as intended, they serve different purposes and were appreciated by the school teachers differently. Dynamic visualizations help to give the teachers a good understanding of how the individual students read. Static visualizations help in getting a simple overview of how the children read as a group and of their active vocabulary

Methodological Challenges in Eye-Tracking based Usability Testing of 3-Dimensional Software – Presented via Experiences of Usability Tests of Four 3D Applications

Eye-tracking based usability testing and User Experience (UX) research are widespread in the development processes of various types of software; however, there exist specific difficulties during usability tests of three-dimensional (3D) software. Analysing the screen records with gaze plots, heatmaps of fixations, and statistics of Areas of Interests (AOI), methodological problems occur when the participant wants to rotate, zoom, or move the 3D space. The data gained regarded the menu bar is mainly interpretable; however, the data regarded the 3D environment is hardly so, or not at all. Our research tested four software applications with the aforementioned problem in mind: ViveLab and Jack Digital Human Modelling (DHM) and ArchiCAD and CATIA Computer Aided Design (CAD) software. Our original goal was twofold. Firstly, with these usability tests, we aimed to identify issues in the software. Secondly, we tested the utility of a new methodology which was included in the tests. This paper summarizes the results on the methodology based on individual experiments with different software applications. One of the main ideas behind the methodology adopted is to tell the participants (during certain subtasks of the tests) not to move the 3D space while they perform the given tasks at a certain point in the usability test. During the experiments, we applied a Tobii eye-tracking device, and after the task completion, each participant was interviewed. Based on these experiences, the methodology appears to be both useful and applicable, and its visualisation techniques for one or more participants are interpretable

Event-driven Similarity and Classification of Scanpaths

Eye tracking experiments often involve recording the pattern of deployment of visual attention over the stimulus as viewers perform a given task (e.g., visual search). It is useful in training applications, for example, to make available an expert\u27s sequence of eye movements, or scanpath, to novices for their inspection and subsequent learning. It may also be potentially useful to be able to assess the conformance of the novice\u27s scanpath to that of the expert. A computational tool is proposed that provides a framework for performing such classification, based on the use of a probabilistic machine learning algorithm. The approach was influenced by the need to compute similarity of eye fixations at single points in time, such as would be required for video stimuli. This method is also useful for eye movement analysis over static images and some interactive tasks. The algorithm employs a common qualitative omparison method, the heatmap, in a quantitative way to measure deviation from group aggregate behavior. This quantitative comparison is performed at individual events, defined by the stimulus, such as frame timestamps of video or mouseclicks of interactive tasks. The algorithm is evaluated and found to be more accurate and discriminative than existing comparison algorithms for the stimuli used in the examined experiments

The Use of Eye-tracking in Information Systems Research: A Literature Review of the Last Decade

Eye-trackers provide continuous information on individuals’ gaze behavior. Due to the increasing popularity of eye- tracking in the information systems (IS) field, we reviewed how past research has used eye-tracking to inform future research. Accordingly, we conducted a literature review to describe the use of eye-tracking in IS research based on a sample of 113 empirical papers published since 2008 in IS journals and conference proceedings. Specifically, we examined the methodologies and experimental settings used in eye-tracking IS research and how eye-tracking can be used to inform the IS field. We found that IS research that used eye-tracking varies in its methodological and theoretical complexity. Research on pattern analysis shows promise since such research develops a broader range of analysis methodologies. The potential of eye-tracking remains unfulfilled in the IS field since past research has mostly focused on attention-related constructs and used fixation count metrics on desktop computers. We call for researchers to utilize eye-tracking more broadly in IS research by extending the type of metrics they use, the analyses they perform, and the constructs they investigate