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Where do pedestrians look when crossing? A state of the art of the eye-tracking studies

It has been widely shown in the literature that analysing eye movements and positions can provide useful information for a better understanding of human perception and cognition. The eye-tracking technology, as a process of measuring where people look, has established itself as a widespread means of studying visual information processing in several domains, including in the study of human walking. Street-crossing can be defined as a particular form of walking. Indeed, several elements have to be considered in the decision-making process, such as the distance headway, traffic density, vehicle speed, etc. It is also a very risky aspect of walking as pedestrians are considered one of the most vulnerable road users. In this article, we present an up-to-date comprehensive review of existing eye-tracking experiments in the literature, from the pedestrian's point of view, with a view to study the effects of both internal (e.g., age) and external (e.g., road environment) factors on pedestrians' road crossing gaze behaviour. Furthermore, the current gaps in the literature are then discussed in order to open up some future perspectives in the field, such as the forthcoming introduction of automated vehicles on the roads.

7 versions available

500,000 images closer to eyelid and pupil segmentation

Human gaze behavior is not the only important aspect about eye tracking. The eyelids reveal additional important information; such as fatigue as well as the pupil size holds indications of the workload. The current state-of-the-art datasets focus on challenges in pupil center detection, whereas other aspects, such as the lid closure and pupil size, are neglected. Therefore, we propose a fully convolutional neural network for pupil and eyelid segmentation as well as eyelid landmark and pupil ellipsis regression. The network is jointly trained using the Log loss for segmentation and L1 loss for landmark and ellipsis regression. The application of the proposed network is the offline processing and creation of datasets. Which can be used to train resource-saving and real-time machine learning algorithms such as random forests. In addition, we will provide the worlds largest eye images dataset with more than 500,000 images.

4 versions available

500,000 images closer to eyelid and pupil segmentation

Human gaze behavior is not the only important aspect about eye tracking. The eyelids reveal additional important information; such as fatigue as well as the pupil size holds indications of the workload. The current state-of-the-art datasets focus on challenges in pupil center detection, whereas other aspects, such as the lid closure and pupil size, are neglected. Therefore, we propose a fully convolutional neural network for pupil and eyelid segmentation as well as eyelid landmark and pupil ellipsis regression. The network is jointly trained using the Log loss for segmentation and L1 loss for landmark and ellipsis regression. The application of the proposed network is the offline processing and creation of datasets. Which can be used to train resource-saving and real-time machine learning algorithms such as random forests. In addition, we will provide the worlds largest eye images dataset with more than 500,000 images.

4 versions available

A comparison of pilot upset recovery performance in simulation and flight

This research examined the differences in pilot upset recovery performance in simulation and actual flight. The study compared pilots' ability to recover from unanticipated aircraft upsets in both environments to determine the effectiveness of simulation training versus real-world training. Data were gathered from multiple pilot groups with varying levels of experience and proficiency. The findings provide insights into the advantages and limitations of using simulation for upset recovery training and highlight the importance of incorporating both simulation and flight training for comprehensive pilot preparation.

3 versions available

A Compression of Quiet Eye in Children with High and Low Motor Proficiency

The aim of this study was to compare quiet eye in children with high and low motor proficiency. In this causal-comparative study, 40 children (7 to 14 years old) in Isfahan city were selected by multiple cluster sampling method. Participants performed throwing and catching tasks in 10 trials. The data of quiet eyes were recorded by an eye tracking device (Ergoneers) and analyzed by Dikablis 3.1 software when performing the desired task. After the evaluation of data normality, the data were analyzed by independent t test and Pearson correlation coefficient at the significance level of 0.05. Independent t test results showed that children with high motor proficiency were better in the onset of QE (P =0.0001), offset of QE (P =0.023), QE duration (P =0.0001) and catching performance (P =0.0001) than children with lower motor proficiency. Also, the results showed a significant negative relationship between the catching performance and the onset of QE and a significant positive relationship between the catching performance and the offset of QE and QE duration in both groups of children with high and low motor proficiency. The results of the study generally revealed that the QE is considered as an effective variable associated with motor performance and appropriate to expertise level.

2 versions available

A ground-truth data set and a classification algorithm for eye movements in 360-degree videos

The segmentation of a gaze trace into its constituent eye movements has been actively researched since the early days of eye tracking. As we move towards more naturalistic viewing conditions, the segmentation becomes even more challenging and convoluted as more complex patterns emerge. The definitions and the well-established methods that were developed for monitor-based eye tracking experiments are often not directly applicable to unrestrained set-ups such as eye tracking in wearable contexts or with head-mounted displays. The main contributions of this work to the eye movement research for 360° content are threefold: First, we collect, partially annotate, and make publicly available a new eye tracking data set, which consists of 13 participants viewing 15 video clips that are recorded in 360°. Second, we propose a new two-stage pipeline for ground truth annotation of the traditional fixations, saccades, smooth pursuits, as well as (optokinetic) nystagmus, vestibulo-ocular reflex, and pursuit of moving objects performed exclusively via the movement of the head. A flexible user interface for this pipeline is implemented and made freely accessible for use or modification. Lastly, we develop and test a simple proof-of-concept algorithm for automatic classification of all the eye movement types in our data set based on their operational definitions that were used for manual annotation. The data set and the source code for both the annotation tool and the algorithm are publicly available at https://web.gin.g-node.org/ioannis.agtzidis/360_em_dataset.

3 versions available

A user experience‐based toolset for automotive human‐machine interface technology development

The development of new automotive Human-Machine Interface (HMI) technologies must consider the competing and often conflicting demands of commercial value, User Experience (UX) and safety. Technology innovation offers manufacturers the opportunity to gain commercial advantage in a competitive and crowded marketplace, leading to an increase in the features and functionality available to the driver. User response to technology influences the perception of the brand as a whole, so it is important that in-vehicle systems provide a high-quality user experience. However, introducing new technologies into the car can also increase accident risk. The demands of usability and UX must therefore be balanced against the requirement for driver safety. Adopting a technology-focused business strategy carries a degree of risk, as most innovations fail before they reach the market. Obtaining clear and relevant information on the UX and safety of new technologies early in their development can help to inform and support robust product development (PD) decision making, improving product outcomes. In order to achieve this, manufacturers need processes and tools to evaluate new technologies, providing customer-focused data to drive development. This work details the development of an Evaluation Toolset for automotive HMI technologies encompassing safety-related functional metrics and UX measures. The Toolset consists of four elements: an evaluation protocol, based on methods identified from the Human Factors, UX and Sensory Science literature; a fixed-base driving simulator providing a context-rich, configurable evaluation environment, supporting both hardware and software-based technologies; a standardised simulation scenario providing a repeatable basis for technology evaluations, allowing comparisons across multiple technologies and studies; and a technology scorecard that collates and presents evaluation data to support PD decision making processes. The Evaluation Toolset was applied in three technology evaluation case studies, conducted in conjunction with the industrial partner, Jaguar Land Rover. All three were live technology development projects, representing hardware and software concepts with different technology readiness levels. Case study 1 evaluated a software-based voice messaging system with reference to industry guidelines, confirming its performance and identifying potential UX improvements. Case study 2 compared three touchscreen technologies, identifying user preference and highlighting specific usability issues that would not have been found though analytical means. Case study 3 evaluated autostereoscopic 3D displays, assessing the effectiveness of 3D information while highlighting design considerations for long-term use and defining a design space for 3D content. Findings from the case studies, along with learning from visits to research facilities in the UK and USA, was used to validate and improve the toolset, with recommendations made for implementation into the PD workflow. The driving simulator received significant upgrades as part of a new interdisciplinary research collaboration with the Department of Psychology to support future research activity. Findings from the case studies also directly supported their respective technology development projects; the main outcome from the research therefore is an Evaluation Toolset that has been demonstrated, through application to live technology development projects, to provide valid, relevant and detailed information on holistic evaluations of early-phase HMI technologies, thus adding value to the PD process.

3 versions available

Abstracts of the 20th European Conference on Eye Movements, 18-22 August 2019, in Alicante (Spain)

Previous research has shown that when individuals are asked questions referring to visual stimuli seen before, their eye movements spontaneously return to the visual area where the stimuli were first seen. This recurring eye movement phenomenon has been shown to assist the memory retrieval of visual images. It is thus possible that oculomotor dysfunction can account for visual memory deficits, due to their diminished tendency to retrieve the same eye movement pattern. Considering patients with traumatic brain injury (TBI) characteristically suffer from visual memory deficits, also typical among this population, can partially account for this memory impairment. In this study, 27 healthy individuals and 27 patients with TBI from the Lowenstein Rehabilitation Hospital participated in a memory task. Participants were first exposed to stimuli and were then asked questions about the displayed stimuli. The testing session was conducted for each participant under two conditions: (1) while eyes were free to move over the screen; (2) while eyes were fixated. Study findings show that the control group significantly benefitted from the free-viewing condition in comparison to the fix-viewing condition, while this effect was absent among the TBI group. This was corroborated by the eye tracking data showing that participation in ...

5 versions available

AcE-Bs2019LangkawiIsland

Reading efficiency is one of the main concerns among the teachers, publishers, and also eye care practitioners. The size of the text was among factor that might affect the reading. The eye movement behaviour was studied in six different text legibility. The saccades and fixation were recorded and tracked among twenty-five university students. Significant changes in eye movements behaviour in term of saccades and fixations occurred when the university students read passages with different text legibility. The eye movements behaviour was able to adapt to the changes in shape, and size of presented reading materials for better understanding of reading.

Eye Tracking Glasses

Software

8 versions available

Automatic Prediction of Health Literacy through an eye tracker

The Web is one of the main sources of information. Health-related topics are one of the most searched topics on Web, fact corroborated by a study made in 2013 which concluded that 72% of the web users in the USA use it to search for information about health. Statistics gathered in Europe show that almost every country had percentages of individuals searching online for health information above 50% in 2017. With this high percentage of people searching for health topics online, it is noticeable that their diversity is great, which makes difficult the return of the most appropriate results to everyone. Health literacy is the degree to which an individual has the capacity to obtain, communicate, process and understand basic health information and services to make appropriate health decisions. It is important because, among other things, it determines the capacity individuals have to manage their health. Regarding health information retrieval, it influences the way people understand the documents retrieved from the Web and by understanding individual’s health literacy it may be possible to customize the results returned on the search engine results page (SERP). Health Literacy can be assessed using instruments that were already validated by other studies, being the same based on questionnaires that are applied to people. That way, this study is important because we’re trying to predict health literacy without being intrusive through an eye-tracker. Eye tracking is the process of measuring either the point of gaze (where one is looking) or the motion of an eye relative to the head. Also, as eye tracking technology allows us to assess how long users spend looking at specific components of one object, we can determine the important aspects of one web page to the user. Eye tracking technology is used in many different areas of research being very important in every one of them. Furthermore, one of the largest eye-tracker systems vendors (SensoMotoric Instruments) was acquired by Apple which indicates that this technology will be integrated into Apple products, which will, on one hand, give even more visibility to eye tracking technology and, on the other hand, will make this study have a short-term impact. This work aims to understand if eye movements vary according to the person’s health literacy during the search for health related topics on the web, through the use of an eye tracker. This study consisted of an experiment with users in a controlled environment. Also, there were created work tasks situations in order to motivate the research on health related topics, registering and comparing the ocular movements of the two groups of users, of high (adequate) and low (inadequate) health literacy. During the study itself, analyzing the points where one person is looking on a specific web page, or a predefined set of pages, we’ll try to see if there is a pattern between participants’ health literacy and their eye movement patterns regarding those pages. Then, the data will be analyzed trying to understand if there is a relation between health literacy and how individuals view information related to health on the Web. Finally, this study lead to conclude that participants with higher health literacy were more careful when trying to retrieve information about health conditions, spending more time in SERPs and giving more importance to the source of the content presented to them.

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Where do pedestrians look when crossing? A state of the art of the eye-tracking studies

500,000 images closer to eyelid and pupil segmentation

500,000 images closer to eyelid and pupil segmentation

A comparison of pilot upset recovery performance in simulation and flight

A Compression of Quiet Eye in Children with High and Low Motor Proficiency

A ground-truth data set and a classification algorithm for eye movements in 360-degree videos

A user experience‐based toolset for automotive human‐machine interface technology development

Abstracts of the 20th European Conference on Eye Movements, 18-22 August 2019, in Alicante (Spain)

AcE-Bs2019LangkawiIsland

Automatic Prediction of Health Literacy through an eye tracker

Explore Cutting-Edge Research in Human Factors and Ergonomics