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A deep learning palpebral fissure segmentation model in the context of computer user monitoring

The intense use of computers and visual terminals is a daily practice for many people. As a consequence, there are frequent complaints of visual and non-visual symptoms, such as headaches and neck pain. These symptoms make up Computer Vision Syndrome and among the factors related to this syndrome are: the distance between the user and the screen, the number of hours of use of the equipment and the reduction in the blink rate, and also the number of incomplete blinks while using the device. Although some of these items can be controlled by ergonomic measures, controlling blinks and their efficiency is more complex. A considerable number of studies have looked at measuring blinks, but few have dealt with the presence of incomplete blinks. Conventional measurement techniques have limitations when it comes to detecting and analyzing the completeness of blinks, especially due to the different eye and blink characteristics of individuals, as well as the position and movement of the user. Segmenting the palpebral fissure can be a first step towards solving this problem, by characterizing individuals well regardless of these factors. This work investigates with the development of Deep Learning models to perform palpebral fissure segmentation in situations where the eyes cover a small region of the images, such as images from a computer webcam. The segmentation of the palpebral fissure can be a first step in solving this problem, characterizing individuals well regardless of these factors. Training, validation and test sets were generated based on the CelebAMask-HQ and Closed Eyes in the Wild datasets. Various machine learning techniques are used, resulting in a final trained model with a Dice Coefficient metric close to 0.90 for the test data, a result similar to that obtained by models trained with images in which the eye region occupies most of the image. Keywords: Palpebral fissure. UNet. LinkNet. Computer Vision Syndrome. incomplete blink.

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A novel end-to-end dual-camera system for eye gaze synchrony assessment in face-to-face interaction

Quantification of face-to-face interaction can provide highly relevant information in cognitive and psychological science research. Current commercial glint-dependent solutions suffer from several disadvantages and limitations when applied in face-to-face interaction, including data loss, parallax errors, the inconvenience and distracting effect of wearables, and/or the need for several cameras to capture each person. Here we present a novel eye-tracking solution, consisting of a dual-camera system used in conjunction with an individually optimized deep learning approach that aims to overcome some of these limitations. Our data show that this system can accurately classify gaze location within different areas of the face of two interlocutors, and capture subtle differences in interpersonal gaze synchrony between two individuals during a (semi-)naturalistic face-to-face interaction.

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A study of the cognitive process of pedestrian avoidance behavior based on synchronous EEG and eye movement detection

Pedestrian avoidance behavior often occurs in underground public spaces that connect urban rail transit and commercial complexes. This study proposes a co-monitoring method based on eye movement and electroencephalogram (EEG) to study pedestrian avoidance behavior in a real environment, taking the underground public space of the commercial complex of the Luoxiong Road railway station in Wuhan City as an experimental site. It is found that pedestrian avoidance behavior is influenced by both personal and environmental factors. The pedestrian avoidance behavior is a comprehensive response to the evaded person and the current environment. The personal factors mainly affect the pedestrian avoidance mode, while the environmental factors mainly affect the frequency of avoidance behavior. Avoidance patterns are related to the tendency of Chinese pedestrians to walk right, and the frequency of avoidance behavior is related to the complexity of the intersection of pedestrian walking routes within the environment, so avoidance behavior can be reduced by using spaces with good spatial connectivity in the design of underground public spaces. These findings provide theoretical support and data supplement for future environmental design optimization of underground public spaces.

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A Tool to Assist in the Analysis of Gaze Patterns in Upper Limb Prosthetic Use

Gaze-tracking, where the point of regard of a subject is mapped onto the image of the scene the subject sees, can be employed to study the visual attention of the users of prosthetic hands. It can show whether the user pays greater attention to the actions of their prosthetic hand as they use it to perform manipulation tasks, compared with the general population. Conventional analysis of the video data requires a human operator to identify the key areas of interest in every frame of the video data. Computer vision techniques can assist with this process, but fully automatic systems require large training sets. Prosthetic investigations tend to be limited in numbers. However, if the assessment task is well-controlled, it is possible to make a much simpler system that uses the initial input from an operator to identify the areas of interest and then the computer tracks the objects throughout the task. The tool described here employs colour separation and edge detection on images of the visual field to identify the objects to be tracked. To simplify the computer’s task further, this test uses the Southampton Hand Assessment Procedure (SHAP) to define the activity spatially and temporarily, reducing the search space for the computer. The work reported here concerns the development of a software tool capable of identifying and tracking the points of regard and areas of interest throughout an activity with minimum human operator input. Gaze was successfully tracked for fourteen unimpaired subjects and was compared with the gaze of four users of myoelectric hands. The SHAP cutting task is described and the differences in attention observed with a greater number of shorter fixations by the prosthesis users compared to unimpaired subjects. There was less looking ahead to the next phase of the task by the prosthesis users.

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Active and Independent Mobility on the Way to School: Case Study of the Redesign of the Infrastructure Surrounding Bunte Schule Währing in the City of Vienna

This article reports on a case study in the city of Vienna that investigated the influence of redesigning the infrastructure surrounding an elementary school on students' mobility behavior. The redesign mainly comprised construction and traffic organization measures. The evaluation is based on before-and-after surveys among parents. The results show significantly better ratings in terms of accessibility, quality of stay, and perceived social and traffic safety. While a positive mode choice tendency was observed for school trips of less than 500 m, there were no significant overall changes in mode choice or independent mobility.

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An analysis of the methodology and validation of the design and presentation of stimulus materials for eye-tracking experiments

Purpose, eye-tracking experimental method is gradually becoming a research hotspot for designing towards intelligence because of its ability to objectively reflect the visual perception process of the subjects. In order to enhance the rationality of the intelligent use of eye-tracking experimental method in the field of design, and to help solve the problem of the lack of a systematic approach to the design of stimulus materials in eye-tracking experiments, the corresponding theoretical model is proposed and verified in practice. Methods, relevant literature in the field of combining eye-tracking instrument and intelligent design at home and abroad is collected and organised, hierarchical relationships are sorted out, theoretical models are constructed, and eye-tracking experiments of China's underground signs are used as an example to verify the reliability of the method model. Conclusion, the design method of stimulus materials for eye movement experiments with experimental purpose as the guide and dimension division as the means is proposed, which provides certain case support for the formation of systematic theory for the intelligent design of stimulus materials in the future, and helps the researchers to be able to accurately carry out the design of stimulus materials based on the purpose of the research when carrying out the eye movement experiments, and better promotes the development of eye movement experimental method in the field of artificial intelligence, and achieves the perfect integration of science and technology and life.

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An end-to-end review of gaze estimation and its interactive applications on handheld mobile devices

In recent years, we have witnessed an increasing number of interactive systems on handheld mobile devices which utilise gaze as a single or complementary interaction modality. This trend is driven by the enhanced computational power of these devices, higher resolution and capacity of their cameras, and improved gaze estimation accuracy obtained from advanced machine learning techniques, especially in deep learning. As the literature is fast progressing, there is a pressing need to review the state-of-the-art, delineate the boundary, and identify the key research challenges and opportunities in gaze estimation and interaction. This article aims to serve this purpose by presenting an end-to-end holistic view in this area, from gaze capturing sensors, to gaze estimation workflows, to deep learning techniques, and to gaze interactive applications.

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An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review

Background: The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom. Objective: In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom. Methods: A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors. Results: Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis. Conclusions: This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape.

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Analysis of Interaction Methods in VR Virtual Reality

Virtual Reality is a new revolution in interaction. As a super technology that can trick the human brain, Virtual Reality is widely used in healthcare, education, business, medical, entertainment and industry. In the field of VR, developers are committed to giving users a full and realistic interactive experience, for example by transmitting the senses of touch, smell, sight and hearing to the user's brain and restoring the user's perception of the real world first-hand. This requires not only powerful technology, but also sophisticated interaction design. Many VR giants are currently working on various technologies and systems to enhance the user's interaction experience in the virtual world. This article analyses three of the main interaction methods (motion capture, eye tracking and haptic feedback) and their associated device products and provide a brief comparison and summary of them. Although the interaction methods in VR are not yet unified and the various virtual reality devices on the market have their own flaws, with more and more players getting involved and the rapid development of technology, it is not difficult to judge that in the near future virtual reality interaction devices will explode like a rocket.

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Anticipatory driving in automated vehicles: The effects of driving experience and distraction

Objective: To understand the influence of driving experience and distraction on drivers’ anticipation of upcoming traffic events in automated vehicles. Background: In nonautomated vehicles, experienced drivers spend more time looking at cues that indicate upcoming traffic events compared with novices, and distracted drivers spend less time looking at these cues compared with nondistracted drivers. Further, pre-event actions (i.e., proactive control actions prior to traffic events) are more prevalent among experienced drivers and nondistracted drivers. However, there is a research gap on the combined effects of experience and distraction on driver anticipation in automated vehicles. Methods: A simulator experiment was conducted with 16 experienced and 16 novice drivers in a vehicle equipped with adaptive cruise control and lane-keeping assist systems (resulting in SAE Level 2 driving automation). Half of the participants in each experience group were provided with a self-paced primarily visual-manual secondary task. Results: Drivers with the task spent less time looking at cues and were less likely to perform anticipatory driving behaviors (i.e., pre-event actions or preparation for pre-event actions such as hovering fingers over the automation disengage button). Experienced drivers exhibited more anticipatory driving behaviors, but their attention toward the cues was similar to novices for both task conditions. Conclusion: In line with nonautomated vehicle research, in automated vehicles, secondary task engagement impedes anticipation while driving experience facilitates anticipation. Application: Though Level 2 automation can relieve drivers of manually controlling the vehicle and allow engagement in distractions, visual-manual distraction engagement can impede anticipatory driving and should be restricted.

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A deep learning palpebral fissure segmentation model in the context of computer user monitoring

A novel end-to-end dual-camera system for eye gaze synchrony assessment in face-to-face interaction

A study of the cognitive process of pedestrian avoidance behavior based on synchronous EEG and eye movement detection

A Tool to Assist in the Analysis of Gaze Patterns in Upper Limb Prosthetic Use

Active and Independent Mobility on the Way to School: Case Study of the Redesign of the Infrastructure Surrounding Bunte Schule Währing in the City of Vienna

An analysis of the methodology and validation of the design and presentation of stimulus materials for eye-tracking experiments

An end-to-end review of gaze estimation and its interactive applications on handheld mobile devices

An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review

Analysis of Interaction Methods in VR Virtual Reality

Anticipatory driving in automated vehicles: The effects of driving experience and distraction

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